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--- title: Genetic Variants of HNF4A, WFS1, DUSP9, FTO, and ZFAND6 Genes Are Associated with Prediabetes Susceptibility and Inflammatory Markers in the Saudi Arabian Population authors: - Dalal N. Binjawhar - Mohammed G. A. Ansari - Shaun Sabico - Syed Danish Hussain - Amal M. Alenad - Majed S. Alokail - Abeer A. Al-Masri - Nasser M. Al-Daghri journal: Genes year: 2023 pmcid: PMC10048403 doi: 10.3390/genes14030536 license: CC BY 4.0 --- # Genetic Variants of HNF4A, WFS1, DUSP9, FTO, and ZFAND6 Genes Are Associated with Prediabetes Susceptibility and Inflammatory Markers in the Saudi Arabian Population ## Abstract Prediabetes is a reversible, intermediate stage of type 2 diabetes mellitus (T2DM). Lifestyle changes that include healthy diet and exercise can substantially reduce progression to T2DM. The present study explored the association of 37 T2DM- and obesity-linked single nucleotide polymorphisms (SNPs) with prediabetes risk in a homogenous Saudi Arabian population. A total of 1129 Saudi adults [332 with prediabetes ($29\%$) and 797 normoglycemic controls] were randomly selected and genotyped using the KASPar SNP genotyping method. Anthropometric and various serological parameters were measured following standard procedures. Heterozygous GA of HNF4A-rs4812829 (0.64; $95\%$ CI 0.47–0.86; $p \leq 0.01$), heterozygous TC of WFS1-rs1801214 (0.60; $95\%$ confidence interval (CI) 0.44–0.80; $p \leq 0.01$), heterozygous GA of DUSP9-rs5945326 (0.60; $95\%$ CI 0.39–0.92; $$p \leq 0.01$$), heterozygous GA of ZFAND6-rs11634397 (0.75; $95\%$ CI 0.56–1.01; $$p \leq 0.05$$), and homozygous AA of FTO-rs11642841 (1.50; $95\%$ CI 0.8–1.45; $$p \leq 0.03$$) were significantly associated with prediabetes, independent of age and body mass index (BMI). Additionally, C-reactive protein (CRP) levels in rs11634397 (AA) with a median of 5389.0 (2767.4–7412.8) were significantly higher than in the heterozygous GA genotype with a median of 1736.3 (1024.4–4452.0) ($p \leq 0.01$). In conclusion, only five of the 37 genetic variants previously linked to T2DM and obesity in the Saudi Arabian population [HNF4A-rs4812829, WFS1-rs1801214, DUSP9-rs5945326, ZFAND6-rs11634397, FTO-rs11642841] were associated with prediabetes susceptibility. Prospective studies are needed to confirm the potential clinical value of the studied genetic variants of interest. ## 1. Introduction Type 2 diabetes mellitus (T2DM) is one of the most prevalent multifactorial chronic disorders, characterized by impaired glucose tolerance and insulin sensitivity, leading to hyperglycemia in both fasting and postprandial states [1,2,3]. It is a rising epidemic of the last century, globally affecting 536.6 million adults aged 20–79 and projected to escalate to 783.2 million by 2045 [4]. The Kingdom of Saudi Arabia (KSA) is not immune to this global epidemic, with an estimated 7 million people in KSA affected by type 2 diabetes mellitus (T2DM) and 3 million with prediabetes [5]. The International Diabetes Federation (IDF) forecasted that the Middle East region would experience the most significant relative growth in the T2DM population, whereas, according to the World Health Organization (WHO), KSA has the 2nd highest T2DM prevalence in the Middle East and 7th highest worldwide [4,5]. This rise in T2DM prevalence is alarming, as it is associated with or triggers various chronic, acute, macro-, and microvascular complications [6,7,8], which significantly impact the quality of life and exert a socioeconomic burden. The estimated global economic burden was 966 billion USD in 2021, which is estimated to possibly escalate to 1054 billion USD by 2045 [4]. Prediabetes is an intermediate stage of T2DM characterized by above normal blood glucose or HbA1c levels, but not high enough to meet the diabetes threshold [9]. It has been linked to a higher risk of chronic diabetes-related complications [10,11]. The rapid industrialization and urbanization in KSA resulted in a notable rise in living standards, leading to a Westernized lifestyle where unhealthy food patterns and limited physical activity dominate. Moreover, age, obesity, and a sedentary lifestyle are the conventional risk factors for T2DM [8,12,13]. Additionally, the Saudi population appears to have a genetic predisposition to T2DM, which might be due to the high prevalence of consanguineous marriages and gestational diabetes mellitus (GDM) [14,15]. Screening plays a significant role, as prediabetes is amenable to interventions that prevent/delay the transition to overt T2DM and reduce the risk of T2DM-related complications [16,17,18]. Aside from screening, a multidimensional approach is desperately needed to holistically understand this disease. Studying gene variants that affect DM phenotypes might be an effective tool in predicting and preventing prediabetes and related complications. In a milestone meta-analysis review involving 21 genome-wide association studies covering almost 123,000 individuals, with a replication set involving another 76,000 individuals, several genetic loci were identified with direct associations to glycemic traits, glucose homeostasis, and insulin resistance [19]. These novel loci were successfully replicated in a homogenous Chinese population, thus reinforcing their impact on diabetes risk [20]. 4Consequently, we previously assessed the association of these genetic loci with T2DM risk based on identified genetic variants that increase T2DM susceptibility in other populations and found that nine variants [WFS1, JAZF1, SLC30A8, CDKN2A/B, TCF7L2, KCNQ1, HMG20A, HNF4A, and DUSP9] were associated with T2DM in the Saudi population [21]. In addition, the same previously genotyped cohort was also investigated for its possible link to obesity, revealing only five allelic variants [FTO and TCF7L2 genes] out of 37 were associated with obesity in the Saudi Arabian population [22]. Given this information, it makes sense to further examine the individual and cumulative influences of these genetic variants on the development and susceptibility to prediabetes, as this intermediate stage to T2DM has yet to be investigated in the Arabian population. Given that genetic testing and interventional gene therapy is rapidly taking shape as an emerging field in medicine, genetic variant studies are needed to fully understand the molecular basis of common human disorders, including insulin-resistant diseases and, in this, case, intermediate stages (prediabetes) of harder outcomes (T2DM). Therefore, the rationale of the current study was to explore the association of previously established 37 SNPs with prediabetes susceptibility in the Saudi Arabian population. ## 2.1. Study Design and Population In this cross-sectional study, 1129 Saudi adults (332 with prediabetes and 797 with normoglycemia) aged between 30 and 60 years were randomly selected from the Biomarker screening project in Riyadh (RIYADH COHORT) [21]. This is a capital-wide epidemiological study comprising over 17,000 consenting Saudis recruited from various Primary Health Care Centers (PHCCs) in Riyadh, KSA. Demographic data and medical history were obtained through a self-administered general questionnaire. Moreover, written informed consent was obtained from all participants before inclusion in the study. Subjects taking anti-diabetic drugs or any medication known to affect glucose homeostasis were excluded from this study. Additionally, subjects who were morbidly obese, had thyroid disorders, including history of hyperparathyroidism, hypercalcemia, chronic kidney disease, or significantly affected with comorbidities that would interfere with study participation were excluded from the study. The study was conducted in accordance with the Declaration of Helsinki. Permission to collect samples from the different PHCCs were provided by the Ministry of Health, General Directorate of Affairs in Riyadh, KSA (No. 74191, dated Hijri $\frac{25}{05}$/1434, corresponding to 13 April 2013). ## 2.2. Biochemical Analysis Participants were requested to report to their allocated PHCCs after an overnight fast (>10 h) for anthropometric analysis and blood sampling. Peripheral blood was obtained in EDTA tubes for DNA extraction, while plain tubes were used to collect blood for serum analysis. Extracted serum and EDTA tubes were transferred to the Chair for Biomarkers of Chronic Diseases (CBCD) laboratory and stored at −20 °C until further analysis. Various anthropometric parameters were recorded, as mentioned in our previous study [23]. Fasting glucose and lipid profile (high- and low-density lipoprotein cholesterol, total cholesterol, and triglycerides) were measured routinely using an autoanalyzer (Konelab, Vantaa, Finland) [24]. Pro-inflammatory cytokines, including tumor necrosis factor α (TNF-α) and interleukins IL6 and IL1β, were measured using commercially available multiplex immunoassay kits that utilize the Luminex xMAP technology platform (Luminex Corporation, Austin, TX, USA), which enables simultaneous analysis of multiple biomarkers in human serum. For TNFα: intra-assay <$10\%$ coefficient of variation (CV), inter-assay <$20\%$ CV. For IL-6: intra-assay <$10\%$ CV, inter-assay <$15\%$ CV. For IL-1β: intra-assay <$10\%$ CV, inter-assay <$15\%$ CV. C-Reactive protein levels were quantified using a commercial enzyme-linked immunosorbent assay (ELISA) kit (Human C-Reactive Protein/CRP Quantikine ELISA Kit, R&D systems, Minneapolis, MN, USA) following the manufacturer’s instructions. ## 2.3. Prediabetes Screening The operational definition of prediabetes used in the present study was based on the cut-off provided by the American Diabetes Association (ADA), which is a fasting blood glucose level of 5.6–6.9 mmol/L (100–125 mg/dL) [25]. The fasting blood glucose level was measured using an automatic biochemical analyzer. For the purpose of this study, the fasting blood glucose was preferred over a 2 h oral glucose tolerance test (for the diagnosis of impaired glucose tolerance) since it is more practical for large-scale screening. ## 2.4. Genotyping Genomic DNA was extracted from the blood using the Blood Genomic Prep Mini Spin Kit (GE Healthcare, Chicago, IL, USA). A Nanodrop spectrophotometer (ND-1000, NanoDrop Technologies by Thermo Fisher Scientific, Wilmington, DE, USA) was used to quantify the concentrations of purified DNA ($\frac{260}{280}$). The 37 SNPs (rs7903146, rs5015480, rs12779790, rs10923931, rs10440833, rs11899863, rs13081389, rs3802177, rs849134, rs5215, rs1470579, rs6795735, rs1387153, rs243021, rs7578326, rs4457053, rs972283, rs896854, rs13292136, rs2311362, rs1552224, rs7957197, rs11634397, rs8042680, rs5945326, rs163184, rs4430796, rs4812829, rs1802295, rs7178572, rs2028299, rs3923113, rs16861329, rs1531343, rs1801214, rs10965250, and rs11642841) were evaluated in prediabetes subjects and their normoglycemic counterparts using the KASPar method (KbioScience, Hoddesdon, UK), with a genotype success rate of $99.1\%$ according to our earlier described work [21]. ## 2.5. Statistical Analysis Data was analyzed using SPSS version 21.0 software. Categorical variables were presented as N (%). Hardy–Weinberg (HW) distribution was assessed for the genotypes in the prediabetes group and their healthy counterparts. Normal quantitative variables were presented as mean (SD) and non-normal quantitative variables were presented as median (quartile 1–quartile 3). The independent samples t-test and Mann-Whitney U-test were used to determine statistical differences between normal and prediabetes subjects for normal and non-normal quantitative variables, respectively. The Kruskal–Wallis test was used to determine statistical differences between SNPs for respective quantitative variables. Bonferroni corrections were used to adjust for multiple comparison. Logistics regression was used to determine the association between prediabetes and SNPs. Furthermore, the effects of covariates including age, gender, and BMI, were removed to obtain the adjusted odds ratios (OR) with $95\%$ confidence interval (CI). A p value < 0.05 was considered statistically significant. ## 3.1. General Characteristics The anthropometric, clinical, and biochemical characteristics of the studied population according to prediabetes status are shown in Table 1. The prevalence of prediabetes in the studied population was $29.4\%$. The prediabetes group was significantly older than the control group ($p \leq 0.01$) and while the percentage of males was higher in the prediabetes group ($45\%$) than in the control group ($38\%$) ($$p \leq 0.03$$), prediabetes was more common in women than men. Measurements of weight, BMI, waist, hips, waist-hip ratio (WHR), fasting glucose, and triglycerides were significantly higher in prediabetes subjects than in controls ($p \leq 0.01$). No significant differences were seen between the prediabetes and control groups in terms of pro-inflammatory cytokines measured. ## 3.2. Association of T2DM-Related Genetic Variants with the Occurrence of Prediabetes A logistic regression analysis of the genotypes with the five SNPs is presented in Table 2. Prediabetes risk increased by $57\%$ among participants with the homozygous AA genotype of rs11642841 (FTO) compared to the CC genotype ($$p \leq 0.02$$). After adjusting for age, gender, and BMI, the risk was reduced to $50\%$. Furthermore, heterozygous GA of rs4812829 (HNF4A), rs5945326 (DUSP9), and rs11634397 (ZFAND6), along with heterozygous TC of rs1801214 (WFS1), were associated with a decreased risk for prediabetes. The relationship between the 37 T2DM-related SNP loci and predisposition to prediabetes was assessed by applying a logistic regression model using age, gender, and BMI as covariates. Among the 37 SNPs, five SNPs, including FTO (rs11642841), HNF4A (rs4812829), WFS1 (rs1801214), DUSP9 (rs5945326), and ZFAND6 (rs11634397), showed significant associations with prediabetes (p-values = 0.03, <0.01, <0.01, 0.01, 0.05, respectively) (Supplementary Table S1). Genotype frequencies of all the significant SNPs (rs11642841, rs4812829, rs1801214, and rs11634397) did not deviate from Hardy-Weinberg equilibrium in our population except for rs5945326 (Supplementary Table S2). ## 3.3. Association of Five Selected Genetic Variants with Anthropometric Measures Table 3 shows the median and quartiles of anthropometric data according to the studied polymorphisms. The median and quartiles of weight ($$p \leq 0.04$$) and BMI ($$p \leq 0.02$$) were significantly higher in the AA genotype than in the CC genotype of rs11642841 ($p \leq 0.05$). In addition, the median and quartiles of WHR in the GG genotype were higher than in the GA and AA genotypes of rs5945326 ($p \leq 0.01$). ## 3.4. Association of Five Selected Genetic Variants with Inflammatory Markers We assessed the association of the five selected SNPs with various inflammatory markers (Table 4). In rs11634397, CRP levels of the homozygous genotype (AA) with a median of 5389.0 (2767.4–7412.8) were significantly higher than those of the heterozygous GA genotype with a median of 1736.3 (1024.4–4452.0) ($p \leq 0.01$). Additionally, TNF-α, CRP, and IL-1β levels were associated with rs11634397, rs4812829, and rs1801214, respectively. This significance was lost in post-hoc analysis. ## 4. Discussion Epidemiological data reveal that approximately 5–$10\%$ of prediabetes subjects will develop diabetes each year and an equal percentage will return to normal [9]. In the past three decades, reports from KSA suggest a ten-fold rise in diabetes prevalence, which is anticipated to rise globally [5]. Identifying genetic markers potentially enables early detection and reduces the risk of T2DM prognosis and related complications. This study was primarily aimed at exploring the association of 37 T2DM-related genetic variants with prediabetes. These variants of interest conferred susceptibility to T2DM in European and South Asian diabetes populations [26,27] and were subsequently replicated in Saudi Arabian ethnic groups [21,22]. The current study revealed that five out of 37 genetic variants were associated with prediabetes and inflammation among Saudi Arabian adults. Interesting to note was the high prevalence of prediabetes ($29\%$) in the group and the anticipated worse cardiometabolic profile of individuals with prediabetes compared to controls, including being significantly older and the substantially higher prevalence in women. Hepatocyte nuclear factor 4-α (HNF4A) regulates hepatic gluconeogenesis and insulin secretion. It belongs to the nuclear receptor superfamily and plays a crucial role in glucose homeostasis in pancreatic β cells and the liver [28,29]. The corresponding gene is located on chromosome 20q13 and is directly implicated in insulin gene expression [28]. The present study is the first to report an association between the HNF4A gene variant (rs4812829) with prediabetes, suggesting that the heterozygous genotype (GA) is protective of prediabetes risk in Saudi Arabian adults. However, Wang et al. tested and linked P2 promoter polymorphism rs1884613 of HNF4A with prediabetes susceptibility in the Chinese Han population [30]. In other populations, a genome-wide association (GWA) study reported that the risk allele of rs4812829 was significantly associated with T2DM and (GDM) risk in a South Asian cohort [31,32]. Conversely, rs4812829 has also been associated with obesity [33,34]. Moreover, multiple studies in different ethnicities have linked T2DM susceptibility with HNF4A variants [29,31,35,36,37]. Interestingly, it was shown that HNF4A variants play a role in type I maturity-onset diabetes of the young (MODY) by impairing insulin sensitivity and β-cell function [38]. WFS1 encodes several proteins, including Wolframin, which is embedded in the endoplasmic reticulum membrane. It is widely expressed across various organs, particularly in the brain and pancreas [39]. Several studies have linked variations in the WFS1 gene to Wolfram Syndrome, an autosomal recessive disorder, and T2DM susceptibility [40,41,42,43]. In mice, WFS1 disruption resulted in increased glucose intolerance and insulin deficiency [44]. However, the underlying effect of these variants on the prediabetes phenotype has not been explored much. In the current study, heterozygous TC of the WFS1-rs1801214 variant located in the coding sequence showed a statistically significant association with prediabetes (0.60; $95\%$ CI 0.44–0.80; $p \leq 0.01$). To the best of our knowledge, no studies have shown a relationship between the WFS1-rs1801214 variant and prediabetes risk; however, Sparsø et al. revealed the interplay between other variants in WFS1 (rs734312, rs10010131) and the prediabetes phenotype [45]. DUSP9 and ZFAND6 are expressed in various tissues with a significant role in glucose homeostasis. The current study revealed that their variants, including the GA heterozygous of DUSP9 (rs5945326) and GG heterozygous of ZFAND6 (rs11634397) genotypes, contributed to decreased risk of developing prediabetes. The GA heterozygous of rs5945326 genotype was also associated with higher waist measurement and WHR. Moreover, the AA homozygous genotype had significantly higher CRP levels than the GA heterozygous genotype. These gene loci were associated with T2DM and β-cell dysfunction and are believed to play a prominent role in protecting against versus developing insulin resistance. There was no prior knowledge available revealing the impact of the various genotypes studied in the current study, including heterozygous GA of HNF4A-rs4812829, (0.64; $95\%$ CI 0.47–0.86; $p \leq 0.01$), heterozygous TC of WFS1-rs1801214 (0.60; $95\%$ CI 0.44–0.80; $p \leq 0.01$), heterozygous GA of DUSP9-rs5945326 (0.60; $95\%$ CI 0.39–0.92; $$p \leq 0.01$$), heterozygous GA of ZFAND6-rs11634397 (0.75; $95\%$ CI 0.56–1.01; $$p \leq 0.05$$), and homozygous AA of FTO-rs11642841, on prediabetes susceptibility. FTO located on 16q12.2 is substantially associated with elevated basal metabolic rate and T2DM [46]. Numerous FTO gene variants have been reported with an amplified contributory effect on T2DM and obesity [47,48]. However, our study showed that SNP rs1164284 had the most substantial susceptibility to prediabetes in the Arab population. Furthermore, among obesity-related traits, weight, BMI, and waist were recognized to be the most significantly associated with the homozygous genotype AA compared to the CC genotype. Importantly, findings from our previous study and other research are concurrent and support the current study outcomes, which suggests that BMI and waist circumference could be potential T2DM predictors. Among the various anthropometric parameters, WC was found to be a significant early marker of T2DM [22,46,49]. Several variants in the same or different genes act synergistically and affect diabetes phenotypes. Initially, the association of 37 T2DM-related SNPs with T2DM was reported in the European population. Subsequently, we replicated and identified around nine loci, with a significant effect on the development of T2DM in the Saudi Arabian population [21]. However, no study has shown the role/relationship of these SNPs in the development of prediabetes. For the first time, we found five loci with an independent effect on prediabetes susceptibility. It is not surprising that the risk alleles of these SNPs are all associated with pancreatic β-cell dysfunction. Even though the significance of prediabetes has been highly underscored, very few studies have assessed the diabetogenic impact of genetic variants. ## 5. Limitation The authors acknowledge certain limitations. Fasting glucose instead of a 2 h glucose tolerance test was used for prediabetes screening; hence, there is a risk of categorizing individuals with impaired glucose tolerance under the normoglycemic group. Nevertheless, the use of fasting glucose was justified as it is more practical for screening large numbers of participants. Additionally, the subjects of the current study are of Saudi Arabian ethnicity; hence, the outcomes of this research might not apply to other populations. 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--- title: Transcriptional Profiling of Rat Prefrontal Cortex after Acute Inescapable Footshock Stress authors: - Paolo Martini - Jessica Mingardi - Giulia Carini - Stefania Mattevi - Elona Ndoj - Luca La Via - Chiara Magri - Massimo Gennarelli - Isabella Russo - Maurizio Popoli - Laura Musazzi - Alessandro Barbon journal: Genes year: 2023 pmcid: PMC10048409 doi: 10.3390/genes14030740 license: CC BY 4.0 --- # Transcriptional Profiling of Rat Prefrontal Cortex after Acute Inescapable Footshock Stress ## Abstract Stress is a primary risk factor for psychiatric disorders such as Major Depressive Disorder (MDD) and Post Traumatic Stress Disorder (PTSD). The response to stress involves the regulation of transcriptional programs, which is supposed to play a role in coping with stress. To evaluate transcriptional processes implemented after exposure to unavoidable traumatic stress, we applied microarray expression analysis to the PFC of rats exposed to acute footshock (FS) stress that were sacrificed immediately after the 40 min session or 2 h or 24 h after. While no substantial changes were observed at the single gene level immediately after the stress session, gene set enrichment analysis showed alterations in neuronal pathways associated with glia development, glia–neuron networking, and synaptic function. Furthermore, we found alterations in the expression of gene sets regulated by specific transcription factors that could represent master regulators of the acute stress response. Of note, these pathways and transcriptional programs are activated during the early stress response (immediately after FS) and are already turned off after 2 h—while at 24 h, the transcriptional profile is largely unaffected. Overall, our analysis provided a transcriptional landscape of the early changes triggered by acute unavoidable FS stress in the PFC of rats, suggesting that the transcriptional wave is fast and mild, but probably enough to activate a cellular response to acute stress. ## 1. Introduction Stress is a physiological response to any condition that perturbs the homeostasis of a living organism. When rapidly activated and then shut off, the stress response is proadaptive, but it may become maladaptive when the stressful stimulus is repeated or overwhelming or in subjects with a genetic background of vulnerability [1]. Accordingly, stress is considered a primary risk factor for many psychiatric disorders, including Major Depressive Disorder (MDD) and Post Traumatic Stress Disorder (PTSD) [1]. The prefrontal cortex (PFC)—a region involved in working memory, decision-making, social interaction, and emotional processing—is a main target of stress [2,3,4,5]. Increasing evidence has consistently shown that the fast response to stress involves increased attention, vigilance, and improved PFC-mediated cognitive performance [6]. In previous studies, we have deeply characterized the functional and morphological changes induced in the PFC of rats by acute inescapable footshock (FS)—a widely used animal model of PTSD [7,8,9]. We demonstrated that FS induced a rapid and long-lasting enhancement of glutamate release in the PFC, already measurable immediately after stress exposure and for at least up to 24 h after [3,4,5]. FS also induced time-dependent modulation of both AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) and NMDA (N-methyl-D-aspartate) receptor subunit expression and phosphorylation, suggesting an early and transient enhancement of AMPA receptor-mediated currents followed by a transient activation of NMDA receptors [10]. Interestingly, these functional alterations of glutamatergic transmission were accompanied by dendritic atrophy and retraction, observed as early as 24 h after FS and sustained for at least 14 days [11]. Using positron emission tomography (PET), we also found a rapid increase in synaptic energy metabolism in the PFC and rapid and sustained alterations in working memory performance [12]. However, the detailed molecular mechanisms underlying these changes have not yet been fully elucidated and, to the best of our knowledge, the genome-wide expression profile in the PFC after acute traumatic stress in rats has never been investigated before. In this work, we performed microarray global transcriptome profiling in the PFC of FS-stressed rats to identify time-dependent transcriptional programs and associated pathways underlying FS-dependent molecular alterations that could take part in the response to acute stress. Genome-wide expression profiles of the PFC were obtained immediately after the 40 min FS session, as well as 2 and 24 h after the initiation of stress, to allow the monitoring of longitudinal changes in expression induced by acute traumatic stress. While no substantial statistically significant changes were observed at the single gene level immediately after stress exposure, gene set enrichment analysis (GSEA) showed alterations in neuronal pathways associated with neuronal morphology and synaptic function. Furthermore, we found alterations in the expression of gene sets regulated by specific transcription factors (TFs), which could represent master regulators of the acute stress response. Finally, we observed that the molecular mechanisms activated to face acute stress are switched off after 2 h, while at 24 h, the transcriptional profile is mainly unaffected. ## 2.1. Animals All experimental procedures involving animals were performed in accordance with the European Community Council Directive $\frac{2010}{63}$/UE and approved by Italian legislation on animal experimentation (Decreto Legislativo $\frac{26}{2014}$, authorization N $\frac{521}{2015}$-PR). Experiments were performed on adult male Sprague–Dawley rats (275–300 g). Rats were housed two per cage and maintained in a $\frac{12}{12}$ h light/dark schedule (lights on at 7:00 am), in a temperature-controlled facility with free access to food and water. The experiments were performed during the light phase (between 9:00 and 12:00 am) at least one week after arrival from the supplier (Charles River, Wilmington, MA, USA). ## 2.2. Footshock (FS) Stress Procedure The footshock (FS)-stress protocol was performed essentially as previously reported (40 min FS stress: 0.8 mA, 20 min total of actual shock with random inter-shock length between 2–8 s) [4,10,13]. Control rats were left undisturbed in their home cages. Rats were killed by decapitation at different time points (the number of animals involved in each experiment is reported in the figure legends): immediately after the stress session (40 min), and 2 or 24 h after the initiation of stress. The 2 and 24 h groups were put back in their home cages after the 40 min stress session until sacrifice. At sacrifice, the PFC was quickly dissected on ice and alternatively assigned to RNA extraction or protein purification. ## 2.3. RNA Extraction and Purification Total RNA from the rat PFC was isolated by single-step extraction using TRIzol Reagent (Life Technologies, Milan, Italy), according to the manufacturer’s instructions. Subsequent RNA clean-up was performed using the RNeasy mini kit (QIAGEN, Milan, Italy) to obtain high quality RNA. RNA quantification and quality controls were carried out using both spectrophotometric analysis (Nanodrop 2000, Nanodrop Technologies, Wilmington, DE, USA) and AGILENT Bioanalyzer 2100 lab-on-a-chip technology (AGILENT Technologies, Santa Clara, CA, USA) [14]. The purity of each sample was determined by assessing the A260:280 ratio, with acceptable values ranging from 1.8 to 2.2. ## 2.4. Microarray Procedures For microarray, 250 ng of the total RNA of each sample was processed using Ambion WT Expression Kits for amplification and using the Affymetrix Whole Transcript (WT) Sense Target Labeling Assay Kit (Life Technologies) to prepare an adequate amount of the labeled target for hybridization [15]. Briefly, total RNA was reverse-transcribed into double-stranded cDNA using random hexamers (thus avoiding the 3′-bias introduced using oligo-dT primers) tagged with a T7 promoter sequence. Then, the double-stranded cDNA was amplified by T7 RNA polymerase in an in-vitro transcription reaction to produce antisense cRNA. In the second cycle of cDNA synthesis with random hexamers, cRNA was reverse-transcribed into single-stranded DNA in the sense orientation. Then, 5.5 µg ssDNA was fragmented, terminally labeled with biotin, hybridized, and processed using Affymetrix GeneChip Rat Gene 1.0 ST Arrays. After washing and staining with fluorescent streptavidin using the Affymetrix GeneChip Fluidics Station 450, chips were scanned with the GeneChip Scanner 3000 7G. Fluorescent signals were acquired by Affymetrix GeneChip Command Console (AGCC) software. Microarray analysis was performed on the samples obtained immediately after the 40 min FS session ($$n = 8$$ FS vs. $$n = 8$$ controls), as well as 2 h ($$n = 6$$ FS vs. $$n = 6$$ controls) and 24 h ($$n = 5$$ FS vs. $$n = 5$$ controls) after the beginning of the stress session. ## 2.5. Microarray Expression Analysis Gene expression was read and quantified from CEL files using the read.celfiles function from the oligo R package (version 1.58.0) [16]. Data were normalized using the rma function from the oligo R package. Probe sets were annotated using the ragene10sttranscriptcluster.db R package (version 8.8.0). We kept only the probe sets with normalized expressions greater than 4. Principal component analysis (PCA) was performed using the sva function from the R base package. Differential expression analysis was performed with the limma R package (version 3.50.3), using the empirical Bayes moderated t-statistics test (eBayes function) [17]. Adjusted p-values were computed using the Benjamini–Hochberg method. Gene set analysis was performed with the ClusterProfiler R package (version 4.2.2) [18]. Gene Ontology (GO) gene sets were defined using the org.Rn.eg.db R annotation package. KEGG pathway gene sets were derived from the graphite R package (version 1.40.0) [19,20]. Transcription Factor (TF) gene sets for rats were retrieved from the msigdbr R package (version 7.5.1), which uses MSigDB regulatory gene sets (C3 category) [21,22]. We used gseGO for GSEA with the GO and GSEA function for KEGG and transcription factor (TF) target analysis. Both GSEA functions implement the GSEA method described in [22]. For both functions, additional parameters were set as follows: pAdjustMethod = “BH” (Benjamini–Hochberg method), pvalueCutoff = 0.1 (adjusted p-value significance threshold), minGSSize = 10, and maxGSSize = 500 (min and max dimension of gene set analyzed). TF activity was computed using the Gene set variation analysis (gsva) method from the GSVA R package (version 1.42.0) [23]. This method allows the computation of a single sample gene set enrichment score for the TF targets gene set. The enrichment score was used as a proxy of TF activity. A heatmap was created using the pheatmap R package, using gsva scores transformed in Z-score. ## 2.6. Western Blotting: Tissue Processing and Image Analysis PFC tissue was homogenized 1:10 (w/v) by a loose-fitting Potter in homogenization buffer (0.28 M sucrose buffered at pH 7.4 with Tris, containing phosphatase inhibitors (Thermo-Fisher Scientific, Milano, Italy) and 2 mL/ml of protease inhibitor cocktail (Merck-Millipore, Milano, Italy)). Protein concentrations were evaluated by Bradford or BCA assays (Merck-Millipore and Thermo Fisher Scientific, Milano, Italy, respectively) and 10–30 micrograms were loaded onto acrylamide SDS-PAGE gels. Western blotting was performed as previously described [10,24]. Specific primary antibodies used were: AKT (1:1000, Cell Signaling cod. 4691); pAKT (1:1000 Cell Signaling cod. 4056); CaM kinase II (1:1000 Chemicon cod: AB3111); pCaM kinase II (1:1000 Thermo Scientific cod: PA14614); CREB (1:1000 Cell Signaling cod: 91975); pCREB (1:1000, Cell Signaling cod: 91985); ERK (1:1000, Cell Signaling cod: 46955); pERK (1:1000, Cell Signaling cod: 43705); GR (1:1000 Santa Cruz cod. sc-1004); pGR (1:1000 Cell Signaling cod. 4161); MR (1:1000 Santa Cruz cod: sc-114112); and mGluR (1:1000, Abcam cod. ab15672). Antibodies against β-actin (1:40,000, Merck-Millipore, cod. Mab374) were used as an internal control. Incubation with primary antibodies was carried out overnight at 4 °C. Membranes were washed five times with TBS-Tween 20 $0.2\%$ and incubated for 1 h at room temperature with AP-conjugated secondary antibodies (Promega, Milan, Italy). Immunolabeled proteins were detected by incubation with Supersignal West Pico Chemiluminescent Substrate (Pierce, Rockford, IL, USA) or CDPStar (Roche Applied Science, Monza, Italy) detection reagents. The intensity of immunoreactive bands was analyzed with Image-Pro Plus. Data are presented as optical density ratios of the investigated protein band, normalized by β-actin bands in the same line, and are expressed as a percentage of controls. ## 2.7. Statistical Analysis All the statistical analyses were performed using the R environment for statistical computing and graphics (version 4.1.3), unless otherwise stated. For differential expression analysis, we used the empirical Bayes moderated t-statistics test (eBayes function) with p-values adjusted using the Benjamini–Hochberg method, implemented in limma [17]. For gene set/pathway analysis, we used GSEA [22], using log-fold-changes with p-values adjusted using the Benjamini–Hochberg method, implemented in the clusterProfiler R package. For Western blot experiments, statistical data analysis was carried out using GraphPad Prism 9 (GraphPad Software Inc., San Diego, CA, USA). Results are presented as means ± standard error of the mean (SEM). Welch’s t-test was used to compare the protein expression levels of FS vs. control animals. ## 3.1. Gene Expression Analysis in the PFC Immediately after Footshock Stress The PFC transcriptomes of rats subjected to FS stress were compared to controls immediately after the 40 min FS session. PCAs using both the most variable genes and the whole transcriptome did not clearly separate controls from FS rats (Supplementary Figure S1). Differential expression analysis between stressed and control rats confirmed this observation, as we found no significant differentially expressed genes (DEGs; adjusted p-value ≤ 0.05; summary statistics of the transcriptomic analyses are reported in Supplementary Table S1). Looking at the most up-regulated genes (i.e., those genes more highly expressed in the FS PFC compared to controls), we found a number of neuronal genes that encode for proteins that localize at synapses such as Drd1 and Drd2 (Dopamine receptor D1 and D2-log fold-change FS vs. controls of 1.13 and 1.31; p-value 0.05 and 0.051; adjusted p-value 0.54 and 0.54, respectively). Among the down-regulated genes, we observed that Grm2 (glutamate metabotropic receptor 2) was the most down-regulated (log fold-change FS vs. controls of −0.92; p-value 0.0169; adjusted p-value 0.51). ## 3.2. Gene Set Enrichment Analysis in the PFC of Rats Immediately after Footshock Stress We performed a Gene Set Enrichment Analysis (GSEA) to look for gene sets or biological pathways with small yet coordinated trends of up- or down-regulation without specifying a fixed threshold. As described in the original method [22], GSEA does not need to specify a list of differentially expressed genes, but works on the whole list of analyzed genes. We performed GSEA using the GO Biological Process annotation. Among the top 10 most significant pathways (adjust p-value ≤ 0.1) we found “glia cell development”, “axon ensheathment”, “ensheathment of neurons”, and “glial cell differentiation”, suggesting that glia–neuron responses could be involved in the response to acute stress (Figure 1A; Supplementary Table S2). We also performed GSEA using KEGG pathways, finding nine significant pathways with an adjusted p-value ≤ 0.1 (Figure 1B; Supplementary Table S3). Strikingly, five out of eight up-regulated pathways were involved in neuronal activity. “Dopaminergic synapse”, “Alcoholism”, and “Cocaine addiction” are pathways involving dopaminergic signaling and are ruled by the genes Drd1 and Drd2, which also show the highest log fold-changes (1.13 and 1.31, respectively; Figure 2A, Supplementary Figure S2) and belong to the core enrichment (i.e., among the most up-regulated) of the three pathways. Dopaminergic activation may imply the involvement of the cAMP signaling pathway, which is also enriched in our GSEA analysis. In the core enrichment of “Dopaminergic synapse”, we also found the two transcription factors c-fos (encoded by Fos, Fos proto-oncogene, AP-1 transcription factor subunit) and CREB (encoded by Creb1: cAMP responsive element binding protein 1), as well as Kif5b and Ppp1r1b (Kinesin and PP-1 on the KEGG map, respectively; Figure 2A), that are known to play crucial roles in the regulation of synaptic activity and plasticity [25,26]. Other up-regulated KEGG pathways included calcium signaling and signal transduction cascades. The only down-regulated (and the least significant) pathway was related to N-glycan biosynthesis. ## 3.3. Gene Expression Analysis and Gene Set Enrichment Analysis (GSEA) in the PFC of Rats 2 and 24 h after Acute Footshock Stress To evaluate the transcriptional changes induced by acute FS in the PFC over time, we examined the PFC transcriptome of FS-stressed animals 2 and 24 h after the beginning of the stress session. Results for differential expression analysis at 2 h and 24 h are reported in Supplementary Table S4 and S5, respectively. Overall, we found seven differentially expressed genes at 2 h and none at 24 h (adjusted p-value ≤ 0.05). Of note, Sgk1—a gene that has been previously reported to be up-regulated after acute stress—is among the DEGs observed at 2 h. GSEA revealed no activated processes 2 h after stress. On the contrary, we observed that many of the processes activated immediately after the stress session were significantly down-regulated 2 h later, including GO Biological Processes related to neurotransmitter trafficking such as “response to monoamine”, “response to dopamine”, and “response to catecholamine”, as well as KEGG pathways for “Cocaine addiction”, “Calcium signaling pathway”, and “Alcoholism” (Supplementary Figure S3; Supplementary Tables S6 and S7). Of note, the core-enrichment genes of the “Cocaine addiction” pathway largely overlapped with those found immediately after stress (Supplementary Figure S2A). Similarly, 2 h after stress, the expression of Grm2—the most down-regulated gene immediately after stress—was completely restored, going from a log fold-change FS vs. control of −0.92 immediately after stress (p-value 0.01688411, adjusted p-value 0.5152705) to 0.16 2 h later (p-value 0.56, adjusted p-value 0.96). At 24 h after FS, we found no sign of the transcriptional perturbation seen at 40 min. Pathway analysis mainly evidenced pathways related to RNA biosynthesis and inflammation (Supplementary Figure S4; Supplementary Tables S8 and S9). ## 3.4. Transcriptional Factor Gene Target Analysis Since most of the variations were observed immediately after the acute FS stress session, we deepened our analysis at this time point. We observed that several pathways activated immediately after acute FS stress pointed to the up-regulation of downstream TFs, including CREB and cFos. Therefore, we investigated if the targets of TFs were activated or repressed in response to FS. We performed GSEA using the regulatory set from the molecular signature database (MSigDB) [21,22]. We found a significant upregulation of several gene targets of TFs (adjusted p-value ≤ 0.1; Figure 3A; Supplementary Table S10). We found an enrichment of Serum Response Factor (SRF) target gene sets (adjusted p-values 0.0055)—a TF capable of modulating Egr$\frac{1}{2}$ and cFos expression as well as that of the Glucocorticoid Receptor (GR; adjusted p-values 0.031). We also found an enrichment for HSF1 and HSF2 target genes (adjusted p-value 0.0055, 0.0098 respectively). However, we did not find any CREB- or c-Fos (AP-1) target enrichment. Our analysis also highlighted the enrichment of CEBP targets (adjusted p-value 0.0066) that—to the best of our knowledge—has never been associated with acute stress in rats. We did not detect any TF target gene set that was significantly down-regulated. To evaluate the level of activation of TFs in each sample, we inferred TF activity by computing the sample-wise activity scores of all significant TFs. As shown in Figure 3B, an overall increased activity of GR, SRF, HSF$\frac{1}{2}$, and CEBP TFs was found in stressed animals. ## 3.5. Protein Expression Analysis of Stress Response Key Effectors in the PFC of Rats Immediately after Acute FS Stress To evaluate how the transcriptional wave might be implemented into a protein response, we selected key regulators identified by pathway and TF analyses and measured their protein expression levels in the PFC of rats immediately after FS stress by Western blotting. As key terminal regulators of the activated pathways “Dopaminergic Synapse”, “cAMP signaling pathway”, and “Calcium signaling pathway”, we selected calcium calmodulin (CaM) kinase 2a and Akt and measured both their total protein expression levels and their activation by phosphorylation. We observed no significant changes (Figure 4A–D). Moreover, we evaluated if the activation of the cAMP signaling pathway triggered an increase in CREB protein levels and its activation by phosphorylation. Although we did not observe any increase in CREB protein levels, we observed a significant increase in CREB phosphorylation levels in FS animals compared to controls (Welch’s t-test $p \leq 0.01$ Figure 4E,F, respectively), indicating that FS induced a rapid activation of CREB. Other kinases that are activated downstream of the cAMP signaling pathway are the ERKs, which are also responsible for the phosphorylation of CREB [27]. We observed a significant increase in ERK protein expression levels (Welch’s t-test $p \leq 0.05$) and a non-significant trend in increased ERKs phosphorylation (Figure 4G,H, respectively). TF target analysis showed several active TFs in FS-stressed animals immediately after stress; therefore, we tested the protein expression of the glucocorticoid receptor (GR) and mineralocorticoid receptor (MR), which are directly activated by corticosterone—the main stress hormone [28]. We observed no changes in GR (Figure 4I) and MR (Figure 4K) protein levels, while pGR significantly increased after FS (Welch’s t-test $p \leq 0.001$; Figure 4J)—suggesting its activation by phosphorylation, which is in line with the TF target gene set analysis. In the transcriptome analysis, we observed the downregulation of Grm2 transcription. This reduction was also confirmed at the protein expression level (Welch’s t-test $p \leq 0.05$; Figure 4L). ## 4. Discussion In the present study, we report a comprehensive analysis of the PFC transcriptomic profile in rats subjected to acute inescapable stress. We used the standardized FS stress model, which we have already dissected at both the functional and morphological level [3,4,5,10,11]. However, to the best of our knowledge, a global transcriptomic analysis in the PFC of this model has never been performed before. To shape the transcriptional wave following stress exposure, we looked at transcriptional changes immediately after the 40 min stress session, as well as 2 and 24 h after the initiation of stress [11]. Our results showed that neither immediately after FS stress, nor 2 or 24 h after, could substantial changes at the single-gene level be detected. This suggests that—in face of rapid and long-lasting functional, structural, and protein changes induced by acute FS in the PFC of rats [7,9]—transcriptional changes seem to be mild. Previous studies investigating the time-dependent transcriptional effects of acute stress have mainly been conducted in mice (although a few reports on rats are also available) and have essentially focused on the hippocampus [29,30,31,32,33]. Other brain areas have been investigated—including the amygdala, nucleus accumbens, and locus coeruleus—but the number of reports is small [34,35,36,37,38]. Although limited due to the use of microarrays instead of next-generation sequencing methods, to the best of our knowledge this is the first study describing the transcriptional signature of acute stress in the PFC. Of note, the previous literature on acute stress presents few points of convergence, basically reporting transient increases in a limited number of genes—mainly immediate–early genes [39,40,41,42,43,44]. Interestingly, a recent bioinformatic study analyzed the transcriptional profile associated with different stress conditions in mice and reported high variability in the pattern of gene expression after FS exposure and, remarkably, a different set of DEGs was obtained for each region, with a limited intersection between different studies [45]. This suggests that the transcriptional signature of stress is not only strictly dependent on the brain area analyzed, but also on the specific stress protocol applied and the sex, age, species, and strain of the animals used [1]. Future studies are required to unveil a more complex picture of the transcriptional response of the PFC to acute stress. For example, in the present study, we analyzed the whole PFC, without dissecting functional subregions (e.g., infralimbic or prelimbic PFC) or considering single-cell transcriptomic profiling. In this context, in a recent study, a large portion of the active transcriptional response to acute stress in the hippocampus was found to be driven by non-neuronal cell types—particularly vascular cells and astrocytes [46]. In our model, performing GSE Analysis—thus looking for gene sets or biological pathways with small yet coordinated trends of up- or down-regulation without specifying a fixed threshold—allowed the identification of pathways modulated by FS in the PFC of rats. Immediately after stress, we found upregulation of GO Biological Process terms such as “glia cell development”, “axon ensheathment”, “ensheathment of neurons”, and “glial cell differentiation”, which indicates the activation of gene sets that promote glial adaptation and glial/neuronal remodeling. Alterations in glial function have been implicated in mental disorders [47,48] as well as in the adaptive response to acute stress [49,50,51]. Our data are in line with the literature, indicating a role of acute stress in reshaping neuron–glia networking in the PFC. Furthermore, by analyzing KEGG pathways, we found eight up-regulated pathways and one down-regulated pathway. Strikingly, five out of eight up-regulated pathways (“Dopaminergic synapse”, “Alcoholism”, “Cocaine addiction”, “cAMP signaling pathway”, and “Calcium signaling pathway”) have a connection with synapse plasticity, memory, and neuronal responses to stress [52,53,54,55]. The remaining three pathways are significant mainly for their signal transduction cascades. The only down-regulated pathway is related to N-glycan biosynthesis, which has recently been associated with brain physiology and disorders [56]. The above mentioned pathways involve a high number of protein effectors, including CaM kinase II and Akt. Even if these two genes are not part of the core enrichment—given their centrality in these pathways—we analyzed their protein and phosphorylation levels, not finding any significant change immediately after stress. This may suggest that the CaM kinase 2a and Akt pathways are not directly involved in the early response to FS stress. To further understand the transcriptional response immediately after acute FS stress, we investigated the identified pathways, focusing on the genes in the core enrichment. We observed the activation of the Drd1 and Drd2 genes, coding for dopamine receptor 1 and 2 of the dopaminergic synapse, which are also key elements of the cocaine addiction- and alcoholism-related pathways. In the dopaminergic synapse, this chain of activation links to synapse plasticity and to the cAMP signaling pathway, which were also enriched in our GSEA analysis. Two transcription factors appear to be the final effectors of the “Dopaminergic synapse” and related pathways: c-fos (encoded by Fos, the Fos proto-oncogene and AP-1 transcription factor subunit) and CREB (encoded by Creb1: cAMP responsive element binding protein 1); both genes were in the core enrichment (i.e., the most up-regulated genes) of the pathways. Two other genes among those of the core enrichment of the Dopaminergic synapse were Kif5b and Ppp1r1b (Kinesin and PP-1 on the KEGG map). These two genes, along with the calcium signaling pathway, seem to play a crucial role in synaptic activity and plasticity—given Kif5b’s interaction with AMPA receptors and Ppp1r1b’s ability to inhibit both AMPA and NMDA receptors [57,58,59]. As a main effector of the cAMP signaling pathway, we analyzed CREB protein and ERKs, which are among the kinases that phosphorylate CREB [60]. We found that ERK proteins were significantly up-regulated in response to stress. Accordingly, we found that phosphorylated CREB, remarkably, increased soon after FS stress. Nevertheless, the CREB target gene set was not significant in our TF analysis, and thus CREB activity was not computed. Further analysis is necessary to explain this discrepancy. Finally, gene expression analysis immediately after FS stress indicated a trend in decreased mGluR2 transcription levels that was confirmed at the protein level. This receptor is one of the main metabotropic glutamate receptors and has been both repeatedly implicated in the response to acute and chronic stress [61,62,63,64,65] and proposed as a putative target for antidepressants [66,67,68]. Taken together, our transcriptional analysis strengthens the hypothesis that both dopaminergic and glutamatergic synapses could be targets and mediators of the acute stress response [69,70]. Importantly, we observed that the transcriptional wave exhausted rapidly over time. In fact, we found that most of the pathways that were active immediately after stress were down-regulated 2 h later—while 24 h after FS stress, no modified pathways were detected. Our data indicate that acute stress triggered a fast, but mild transcriptional response that was resolved in a few hours. Accordingly, a recent multiomic approach evaluating the phospho-proteome, proteome, transcriptome, mirnome, and translatome of the mouse dorsal and ventral hippocampus after acute stress highlighted calcium signaling, ERK/MAPK signaling, cAMP signaling, and CREB as master regulators of the hippocampal acute stress response [46]. Intriguingly, in line with our study, all the observed molecular changes resolved efficiently within four hours after the initiation of stress. To evaluate if the transcriptional changes were controlled by specific TFs, we inferred TF activity by focusing on those TFs whose target gene sets were significantly up-regulated. Of note, our analysis identified strong activity for TFs such as GR, SRF, HSF, and CEBP. The up-regulation of GR-regulated genes is not surprising in the context of the stress response, with GR being one of the main targets of corticosterone and GR activation that are necessary for the cellular stress response [71,72]. In line with this transcriptional data, we found that FS induced a significant up-regulation of GR phosphorylation—suggesting the activation of GR-dependent cellular pathways in response to FS stress. Furthermore, a transcriptional program that seems particularly relevant for the FS stress response is regulated by SRF. SRF is a master regulator of immediate–early gene expression in response to external stimuli [73]. SRF has been implicated in responses to both chronic and acute stress [74,75]. Deletion of the SRF gene specifically in glutamatergic neurons has been reported to induce hyperactivity, decreased anxiety, and impair working memory. In response to restraint stress, locomotor behavior and corticosterone release are impaired in Srf −/− mutant mice, indicating the requirement of SRF activation for the physiological stress response [75]. Our data showing SRF gene-set activation suggests that it also has a role in the response to acute FS stress. Another transcription factor that was found to be activated immediately after FS stress was CEBP. To the best of our knowledge, this is the first time that CEBP has been linked to the PFC transcriptional response after acute stress in rats. CEBP activation has also been reported in the mouse hippocampus 45 min after forced-swim stress [46]. Moreover, the loss of CEBP regulation has been shown to lead to abnormal synaptic function and cognitive disorders in mice [76], while in rats, it has been shown that CEBP activation of IGF-1 is necessary to promote neurite outgrowth and mitochondrial respiration in the brain cortex, which can protect against neurodegenerative disorders [77]. ## 5. Conclusions Overall, our work provides an overview of time-dependent transcriptional changes triggered by acute FS stress in the PFC of rats. We observed that transcriptional changes are fast, but mild, and resolve efficiently within 2 h after the initiation of stress. Moreover, we were able to identify a coordinated and consistent activation of transcriptional programs that may be involved in the response to FS. We found an involvement of dopaminergic and glutamatergic synapses as well as of cAMP signaling, which have also been found in previous works. 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--- title: Mode of Antifungal Action of Daito-Gettou (Alpinia zerumbet var. exelsa) Essential Oil against Aspergillus brasiliensis authors: - Kiyo Okazaki - Hidenobu Sumitani - Katsutada Takahashi - Yuji Isegawa journal: Foods year: 2023 pmcid: PMC10048414 doi: 10.3390/foods12061298 license: CC BY 4.0 --- # Mode of Antifungal Action of Daito-Gettou (Alpinia zerumbet var. exelsa) Essential Oil against Aspergillus brasiliensis ## Abstract Plant-derived essential oils (EOs) are used in medicines, disinfectants, and aromatherapy products. Information on the antifungal activity of EO of Alpinia zerumbet var. exelsa (known as Daito-gettou) found in Kitadaito Island, Okinawa, is limited. Therefore, we aimed to evaluate the antifungal activity of EOs obtained via steam distillation of leaves of Daito-gettou, which is a hybrid of A. zerumbet and A. uraiensis. Daito-gettou EO showed antifungal activity (minimum inhibitory concentration = $0.4\%$) against Aspergillus brasiliensis NBRC 9455, which was comparable to that of A. zerumbet found in the Okinawa main island. Gas chromatography/mass spectrometry revealed that the main components of Daito-gettou EOs are γ-terpinene, terpinen-4-ol, 1,8-cineole, 3-carene, and p-cymene. Terpinen-4-ol content (MIC = $0.075\%$) was $17.24\%$, suggesting that the antifungal activity of Daito-gettou EO was mainly attributable to this component. Daito-gettou EO and terpinen-4-ol inhibited mycelial growth. Moreover, calorimetric observations of fungal growth in the presence of Daito-gettou EO showed a characteristic pattern with no change in the initial growth rate and only a delay in growth. As this pattern is similar to that of amphotericin B, it implies that the action mode of Daito-gettou EO and terpinen-4-ol may be fungicidal. Further studies on the molecular mechanisms of action are needed for validation. ## 1. Introduction Essential oils (EOs) derived from plants contain various components with antimicrobial activity [1,2,3,4,5,6,7], and they have long been used as disinfectants and medicinal agents. These EOs have also been used in food products to control spoilage by microorganisms and extend shelf life. In recent years, the emergence of bacteria resistant to chemical preservatives and the consumer preference for natural products have increased the demand for plant-derived EOs [8]. EOs derived from herbs or spices such as Melaleuca alternifolia (tea tree) [9,10], Lavandula angustifolia (lavender) [11], Angelica major [12], Curcuma longa L. [13], Thymus capitatus [14], Thymus vulgaris L. [15], and Alpinia calcarata Roscoe [12] have been reported to have antibacterial and antifungal activities. They are anticipated to inhibit the growth of food-spoilage microorganisms and maintain the quality and safety of food products. In addition, the antimicrobial activity of EOs depends on the components of the EOs. These species contain numerous volatile compounds, such as terpenes and terpenoids, and aromatic and aliphatic components, which are naturally synthesized in various parts of plants as a part of their secondary metabolism [7]. The biological properties of EOs are determined by the concentration of their major components; therefore, analyzing the constituents of EOs is necessary to assess the biological activity. The components of EOs of Syzygium aromaticum L. Myrtaceae (clove) [16], Citrus tangerina (tangerine), Carum carvi (caraway) [17], and Homalomena pineodora [18] have been analyzed using gas chromatography/mass spectrometry (GC/MS). Alpinia zerumbet, a perennial plant belonging to the genus Alpinia of the family Zingiberaceae, is commonly known as “shell ginger” in English and “gettou” in Japanese. It is distributed throughout tropical to subtropical Asia, and it is distributed from Okinawa Island to southern Kyushu in Japan. The seeds, leaves, and stems of this species have been used for various purposes [19]; for example, the seeds have been used as medicinal herbs since ancient times and are considered to have an intestinal-regulatory effect. Moreover, the stems are used in industries as a source of paper, and the leaves and rhizomes are frequently used as herbal teas and spices, respectively [19,20]. In Okinawa, it was customary to wrap foods with the leaves to carry around; the leaves are also used as a wrap for steaming buns and rice cakes. The leaves are still used to wrap the traditional rice cake muchi. Based on their experience, the people living in Okinawa, where the temperature and humidity are high and the foods spoil easily, have recognized that gettou has antiseptic and antibacterial properties. EOs or extracts of gettou leaves have antinociceptive [21], anti-inflammatory, and antipyretic activities [22]. Owing to their medicinal effects, EOs or extracts of gettou leaves have been used in skin care products and insect repellents. As EOs have a sweet scent, they are also used in aroma oils and fragrances. There are multiple varieties of gettou, including Shima-gettou (A. zerumbet), which is widely distributed in Taiwan and the main island of Okinawa, Tairin-gettou (A. uraiensis) in northern Taiwan, and Daito-gettou (A. zerumbet var. exelsa) in Daito Islands, Okinawa Prefecture. Daito-gettou is considered to be a hybrid of A. zerumbet and A. uraiensis and has characteristics different from those of other gettou. For example, Daito-gettou grows to a height of over 4 m, which is taller than Shima-gettou, and does not bear fruit. The scent of Daito-gettou is similar to that of tea tree. Additionally, Daito-gettou when planted near sugarcane fields serves as a windbreaker and is used as a rope to bind harvested sugarcane. In this study, the antifungal activity of EOs of the leaves of Daito-gettou distributed on Kitadaito Island was determined to investigate the relationship between the activity and chemical components of EOs. Tea tree EO was used for comparison in this study because it has a high antimicrobial activity [9], and its scent is similar to Daito-gettou EO; thus, it was postulated that the components might be similar between the two EOs. In addition, to further understand the mechanism underlying the antifungal action of Daito-gettou, we used a microbial calorimeter to measure the heat generation pattern (growth thermogram) associated with fungal growth in the presence of Daito-gettou EO or antifungal agents. We understood the effects of these agents on the growth of the fungi in detail. In this study, we aimed to determine the mechanism underlying the antifungal action of Daito-gettou EO by comparing its growth thermograms with those of common antifungal agents. ## 2.1. Essential Oils Daito-gettou EO used in this study was donated by Kitadaito Island Development Organization, Okinawa Prefecture. Three Shima-gettou EOs were purchased from Nihon Gettou Co., Ltd. (Okinawa, Japan), Green Plan Shinjo Co., Ltd. (Okinawa, Japan), and Tree of Life Co., Ltd. (Tokyo, Japan) for comparison. These EOs are referred to as Shima-gettou 1, 2, and 3, respectively. Tea tree EO was purchased from Natural Organic Co., Ltd. (Tokyo, Japan). ## 2.2. Antifungal Agents and Chemicals Miconazole, diflucan (fluconazole), and 5-fluorocytosine were purchased from Combi-Blocks Inc. (San Diego, CA, USA). Itraconazole and voriconazole were purchased from Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan). 2-(4-Thiazolyl) benzimidazole (thiabendazole, TBZ) was obtained from San-ai Oil Co., Ltd. (Tokyo, Japan). Amphotericin B and terpinen-4-ol were purchased from FUJIFILM Wako Pure Chemical Corporation (Osaka, Japan), and α-pinene, camphene, 3-carene, limonene, γ-terpinene, 1,8-cineole, and p-cymene were purchased from Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan). ## 2.3. Minimum Inhibitory Concentration Aspergillus brasiliensis NBRC 9455 was purchased from the National Institute of Technology and Evaluation Biological Resource Center (NBRC, Tokyo, Japan) and used for antifungal tests. It was inoculated at three points on a Sabouraud dextrose agar plate containing $4.0\%$ (w/v) glucose, $1.0\%$ (w/v) peptone, and $1.5\%$ (w/v) agar (Becton Dickinson, Sparks, MD, USA). After being incubated for 7 d at 25 °C, spore suspension was prepared by adding sterile saline (15 mL) containing $0.1\%$ (w/v) polypeptone (Becton Dickinson) and $0.05\%$ (w/v) Tween-80 (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan) to the plate and gently rubbing the surface of the fungal colonies with a sterile loop. The spore suspension was filtered through a coarse cloth to remove mycelia, and the spores were counted using a hemocytometer. The concentration of the spore suspension was adjusted to 2 × 105 spores/mL using Sabouraud dextrose broth containing $4.0\%$ (w/v) glucose and $1.0\%$ (w/v) peptone for the broth dilution method. The minimum inhibitory concentration (MIC) of EOs and antifungal agents for A. brasiliensis was determined using the broth dilution method. EOs and antifungal agents were dissolved in $20\%$ ethyl alcohol solution. Ethyl alcohol was selected as the solvent in consideration of the future application of Daito-gettou to foods. Amphotericin B, which is insoluble in ethyl alcohol, was dissolved in DMSO (FUJIFILM Wako Pure Chemical Corporation). These solutions were serially diluted with Sabouraud dextrose broth, and 1 mL of the diluted solutions was pipetted into sterile test tubes containing 1 mL of the spore suspensions. The MIC was determined via visual inspection after incubating the mixtures at 25 °C for 3 d. ## 2.4. Gas Chromatography/Mass Spectrometry Analysis The components of EOs were analyzed using gas chromatography/mass spectrometry (GC/MS). The instrument (JMS-T100GCV; JEOL Ltd., Tokyo, Japan) was equipped with an Agilent DB-5MS capillary column (30 m × 0.25 mm i.d., 0.25-μm film thickness, Agilent Technologies, Santa Clara, CA, USA). The oven was programmed as follows: initial temperature of 40 °C (held for 5 min) and heated at a rate of 10 °C /min to 320 °C (held for 3 min). The injector temperature was maintained at 250 °C. Helium was used as the carrier gas at a flow rate of 1.0 mL/min. The ionization voltage was 70 eV, and the mass range was 35–650 m/z. Analysis was performed using two methods. First, the mass spectrum of each EO component was determined by comparison with the mass spectrum from the NIST 11 spectrum library. The percentage composition of each component was calculated based on the respective peak areas. Second, some major components of the EO were quantitatively analyzed. The amount of each component was obtained from a calibration curve prepared using the corresponding standard product. ## 2.5. Effects on Spore Germination and Mycelial Growth The inhibitory effects of Daito-gettou EO, Shima-gettou 1 EO, terpinen-4-ol, p-cymene, and 1,8-cineole on the mycelial growth of A. brasiliensis were determined using the modified “agar dilution method [2007]” for fungi [23]. Each antifungal agent was adjusted to concentrations at 25-, 50-, and 100-fold of the MIC with ethyl alcohol. The ethyl alcohol solutions (0.15 mL) and the dissolved Sabouraud dextrose agar medium (15 mL) were mixed in a Petri dish (φ90 mm) and solidified. The final concentrations of antifungal agents in agar media were adjusted to $\frac{1}{4}$, $\frac{1}{2}$, and $\frac{1}{1}$ MIC, respectively. A sterile paper disk (Toyo Roshi Kaisha, Ltd. (Tokyo, Japan): φ13-mm thick) was placed on an agar plate, and 0.1 mL of the spore suspension (1 × 105 spores/mL) was inoculated on the disk. After culturing at 25 °C for 3 d, the radius of the mycelia grown (excluding the radius of the paper disc) was measured, and the difference from the radius of the mycelia grown on an agar plate without antifungal agents (control) was used to estimate the inhibition of mycelial growth. The inhibitory rate was calculated as follows: Mycelial growth inhibitory rate (%) = (1 − mycelial growth distance on agar medium containing antifungal agent/mycelial growth distance on control agar medium) × 100. Furthermore, the inhibition of mycelial growth of A. brasiliensis by Daito-gettou EO and terpinen-4-ol was evaluated based on the weight of mycelia. Each solution of EO and terpinen-4-ol was serially diluted with Sabouraud dextrose broth, and 7.5 mL of the diluted solution was pipetted into a sterile Petri dish (φ90 mm) containing 7.5 mL of the spore suspension (2 × 105 spores/mL with Sabouraud dextrose broth). The concentration of EO and terpinen-4-ol in the broth was adjusted to 0–$0.4\%$ or 0–$0.075\%$, respectively. After incubating at 25 °C for 5 d, the mycelia in the cultures were filtered through a membrane filter (cellulose acetate, pore size: 0.8 μm) and washed with distilled water. Mycelia on the membrane filter were weighed after removing excess moisture with filter paper. ## 2.6. Calorimetric Measurements A multiplex batch calorimeter, Leonis (ADVANCE RIKO, Inc., Yokohama, Japan), with 25 calorimetric units was used. The apparatus was developed by Japan Science and Technology, a government agency, and it is now sold as a commercial product termed “Non-destructive and Non-invasive Analytical Instrument,” which can quantitatively determine microbial growth activity [24,25,26]. As a sensor, semiconducting thermopile plates were employed and placed in an aluminum heat sink to detect the thermal change in the sample vessels (Petri dishes) set in each calorimetric unit. The calorimetric signals obtained were analyzed according to a method reported previously [25]. Petri dishes (φ60 mm) were placed on units as calorimetric vessels. When temperature changes occurred in the sample vessels, the sensor detected them, and the differentially generated voltage was proportional to the temperature changes. A mixed solution of 4.9 mL of the spore suspension (1 × 105 spores/mL) prepared in Sabouraud dextrose broth and 0.1 mL of antifungal agent diluted stepwise with ethanol was poured into Petri dishes. The sample dishes were then placed in a calorimetric unit and maintained at 25 °C. Calorimetric output signals associated with fungal growth were monitored for incubation periods of 3–5 d. ## 2.7. Statistical Analysis The results were statistically analyzed using a two-tailed unpaired Student’s t-test (Microsoft Excel 365; Microsoft Corporation, Redmond, WA, USA). Results with $p \leq 0.05$ were considered statistically significant. ## 3.1. Antifungal Activity The susceptibility of A. brasiliensis used in this study to various antifungal agents was measured before evaluating the antifungal activity of gettou EOs. The MICs of the antifungal agents are shown in Table 1. Three azol antifungal agents (MCZ, ITCZ, and VRCZ) and amphotericin B (AMPH-B), a polyene drug, showed high antifungal activities. Fluconazole (FLCZ) and flucytosine (5-FC), a pyrimidine-analog drug, presented lower activities. The antifungal agent TBZ (2-(4-thiazolyl) benzimidazole), which is widely used as a pesticide and food additive, had an MIC of 50 ppm (250 μmol/L). Table 1 also shows the MICs of EOs of various types of gettou and tea tree. Daito-gettou EO exhibited the same level of antifungal activity (MIC = $0.40\%$) as Shima-gettou 1–3 EOs, but it was less active than tea tree oil. The GC/MS analysis of each EO component revealed that Daito-gettou contains γ-terpinene, terpinen-4-ol, 1,8-cineole, 3-carene, and p-cymene (Table 2, Figure 1). In contrast, the composition of Shima-gettou EO was quite different from that of Daito-gettou, and it was dependent on the product. Shima-gettou 1 and 2 EOs contained p-cymene, limonene, and α-pinene as the main components, with no traces of terpinen-4-ol. Tea tree EO contained γ-terpinene and terpinen-4-ol, but the peak area of terpinen-4-ol was considerably larger than that of the other EOs. These results are in agreement with those reported by Ninomiya et al. [ 10]. The antifungal activities of five compounds, which showed large peak areas in Daito-gettou EO, and three compounds (limonene, α-pinene, and camphene) detected in Shima-gettou 1 and 2 EOs were measured. As shown in Table 3, terpinen-4-ol showed the highest activity (MIC = $0.075\%$, 4.9 mmol/L) among the tested components. Therefore, terpinen-4-ol was considered as the major substance responsible for the antifungal activity of Daito-gettou EO. This result is consistent with those of Terzi et al. [ 27] and Roana et al. [ 28], who investigated the antifungal activity of tea tree oil. Terzi et al. described terpinen-4-ol as the most active component of tea tree oil against fungi. 1,8-Cineole also showed antifungal activity, with an MIC of $0.50\%$ (32 mmol/L). Table 4 shows the terpinene-4-ol and 1,8-cineol concentration/level of each EO. The concentration/level of p-cymene, which is a common component of all gettou EOs, is also shown. These components were quantified using the respective standards. The composition of Shima-gettou 1 and 2 EOs was significantly different from that of Daito-gettou. ## 3.2. Effects of Daito-Gettou EO on Mycelial Growth To further understand the mechanism underlying the antifungal action of Daito-gettou EO, its effect on the mycelial growth of A. brasiliensis was measured. Shima-gettou 1 EO, terpinen-4-ol, p-cymene, and 1,8-cineole were used for comparison. The inhibitory rate increased with the increasing concentration ($\frac{1}{4}$ to $\frac{1}{1}$ MIC) of all antifungal agents (Figure 2). When treated at $\frac{1}{1}$ MIC, the inhibitory rate was $63.8\%$ for Daito-gettou EO, $59.7\%$ for Shima-gettou 1 EO, and $100\%$ for terpinen-4-ol. Mycelial growth was observed despite treatment at the MIC, which might mainly be attributed to the obtained MIC value (Table 3), which was determined using the broth dilution method in this study. As mycelial growth inhibition was measured using the agar medium dilution method in which the antifungal agent was added to the agar medium and the MICs measured using the broth medium dilution method and agar medium dilution method may differ, the experimental results were believed to be inconsistent. In contrast, treatment at $\frac{1}{4}$ and $\frac{1}{2}$ MIC of p-cymene and $\frac{1}{4}$ MIC of 1,8-cineole resulted in negative inhibition rates. In the presence of these compounds at MIC, mycelial growth was inhibited, whereas at low concentrations, growth was promoted. The effects of Daito-gettou EO and terpinen-4-ol on mycelial weight were measured in Sabouraud dextrose broth medium. The addition of $0.2\%$ EO to the medium increased the dry mycelial mass of the tested fungi, but the mass decreased as the EO concentration increased (Figure 3a). Furthermore, when $0.4\%$ EO (equal to the MIC) was added, some amount of mycelium growth was observed. Additionally, only a few EOs have been shown to inhibit fungal spore germination and mycelial growth [15]. Pereira et al. [ 29] have also reported that the EO of Cymbopogon winterianus inhibits the mycelial growth of Trichophyton rubrum. It has also been shown that the vaporous phase of the EO of Thymus vulgaris L. (thyme) strongly suppresses the sporulation of fungi in glass chambers [30]. In this study, terpinen-4-ol, the main component of Daito-gettou EO, was measured in the same way (Figure 3b). Terpinen-4-ol at $0.075\%$ (equal to the MIC) completely inhibited mycelial growth. ## 3.3. Growth Thermogram of A. brasiliensis in the Presence of Daito-Gettou EO Figure 4a–d show the growth thermograms for cultures of A. brasiliensis in Sabouraud dextrose broth containing various concentrations of TBZ, AMPH-B, Daito-gettou EO, and terpinen-4-ol, respectively. These figures are termed “g(t) curves.” The vertical axis of each figure represents the thermoelectromotive force indicated by the heat detector (µV), and the horizontal axis represents the incubation time (min). The heat generated in the broth containing each sample is considered to be the metabolic heat associated with the process of fungal spore germination and the subsequent mycelial growth [31]. When spores of A. brasiliensis and TBZ at concentrations below the MIC were mixed in Sabouraud dextrose broth and cultured, the pattern of the growth thermogram changed as the TBZ concentration increased, and the slope of the g(t) curve decreased. Moreover, it was observed that the apparent peak time shifted to the longer side of the culture. Additionally, a delay in growth was observed. Thus, changes in the initial rate and delay in growth time occurred, and TBZ was found to suppress fungal growth in a concentration-dependent manner. In contrast, the growth of A. brasiliensis in the presence of AMPH-B was delayed, but no apparent change was observed in the initial growth rate. As shown in Figure 4c,d, the growth thermogram of Daito-gettou EO was similar to that of terpinen-4-ol. Our results revealed that there were time delays in the fungal growth as the concentrations of Daito-gettou EO or terpinen-4-ol increased; however, their initial growth rates did not change. The thermogram pattern of both Daito-gettou EO and terpinen-4-ol was closer to that of AMPH-B than to the pattern of TBZ. An increase in microbial growth activity can be observed when growth thermograms are transformed into calorimetrically defined growth curves [24,32,33,34]. The growth curves described as “f(t) curves” were obtained by computation based on Equation [1]:f(t) = g(t) + K ∫g(t) dt[1] where t is the growth time and K is the heat conduction constant (Newton’s cooling constant) of each calorific value measuring unit including the Petri dish containing the culture solution [24,25,26,35]. The obtained f(t) curves are shown in Figure 5, which correspond to the growth curves of microorganisms in the presence of different agents. As shown in Figure 5a, we observed that the slope of the curve decreased and the growth rate slowed as the TBZ concentration increased. In contrast, the slope of the curve in Figure 5b did not change even when the concentration of AMPH-B increased, and only a delay in fungal growth was observed. Moreover, when the concentrations of Daito-gettou EO were $0.025\%$ and $0.05\%$, the slope of the curves was almost similar to that of the curve without EO. However, at concentrations above $0.1\%$, the slope of the curves was greater than that of the curve without EO. The f(t) curve of Daito-gettou EO exhibited a unique shape, but it more closely resembled the characteristics of the AMPH-B curve than those of the TBZ curve. In addition, as shown in Figure 5d, the slope for terpinen-4-ol increased with the drug concentration, indicating a time lag. The f(t) curve of Daito-gettou EO showed the characteristics of an AMPH-B curve and was notably similar to that of the terpinen-4-ol. ## 4. Discussion Aspergillus brasiliensis, a common fungus in the soil, often contaminates food. It is a commonly used fungal strain for the preservative efficacy test of ISO11930 (International Organization for Standardization, 2012), the Japanese Pharmacopoeia [2021], and the Standards for Food and Food Additives (Ministry of Health and Welfare Notification No. 370, 1959, Japan). In this study, we evaluated the activity of Daito-gettou EO against A. brasiliensis. The susceptibility of A. brasiliensis used in this study was first confirmed before evaluating the antifungal activity of gettou. The trends of the MICs of antifungal agents were consistent with those reported previously [36,37]. The antifungal activity of Daito-gettou EO against A. brasiliensis was found to be similar to that of Shima-gettou EOs (MIC = $0.40\%$), but it was lower than the activity of tea tree EO (Table 1). In addition, the GC/MS analysis revealed that the composition of Daito-gettou EO was different from that of Shima-gettou EOs (Table 2). Similar to tea tree oil, Daito-gettou EO contained a large amount of terpinen-4-ol (Table 3). Generally, external effects cause changes in the ratios of the constituents of EOs [38]. The composition of gettou EOs is known to fluctuate under the influence of climate parameters (temperature and precipitation) [39]. Moreover, the activity thereof differs depending on the production area or the variety of gettou. According to Ramos et al. [ 40], the major constituents of A. zerumbet var. variegata EO are 1,8-cineole ($39\%$), β-pinene ($11\%$), and β-caryophyllene ($10\%$). As Daito-gettou EO used in this study contained a lesser amount of p-cymene and was abundant in terpinen-4-ol, this result supports the concept that the variety of gettou inhabiting the Daito Islands is different from that present in Okinawa main island. Terpinen-4-ol, which was abundant in Daito-gettou EO, exhibited antifungal effects (MIC = $0.075\%$) against A. brasiliensis (Table 3). As the MIC of Daito-gettou EO, which contains approximately $17\%$ of this component, was $0.40\%$, the activity of Daito-gettou EO could mainly be attributed to terpinen-4-ol. Moreover, Maior et al. [ 41] and Ninomiya et al. [ 42] have reported that terpinene-4-ol possess antifungal activity. These findings strengthen our claim of terpinene-4-ol being the main component responsible for the antifungal action of Daito-gettou EO. Additionally, regarding the activity of p-cymene, which is another component, Aznar et al. [ 43] reported that it completely inhibits the growth of Candida lusitaniae for at least 21 d at concentrations above 1 mmol/L at 25 °C. However, MIC of p-cymene against Rhizopus oryzae is >1024 μg/mL, indicating a poor antifungal effect [15]. Daferera et al. [ 44] postulated that the antifungal activity of EOs is primarily due to their major components; however, other phenomena, such as synergy and antagonism with minor components, are also possible. These findings suggest that the antifungal activity of Daito-gettou EO is most likely attributable to terpinen-4-ol and the combined effect of other components, such as 1,8-cineole and p-cymene. The activity of Shima-gettou 1 and 2 EOs, which are terpinen-4-ol-free, was considered to be mediated by α-pinene and limonene. However, we believe that these components alone cannot explain the antifungal activity of Shima-gettou EOs. It is likely that unknown antifungal components or their synergy with other components are involved in the antifungal activity of Shima-gettou EOs. Moreover, the mechanism underlying the antifungal action of Daito-gettou EO was investigated by measuring its effects on the mycelial growth of A. brasiliensis. Daito-gettou EO showed an inhibitory effect on mycelial growth at different MICs (Figure 2 and Figure 3). Terpinen-4-ol also exerted a significant effect on mycelial growth. This result suggests that Daito-gettou EO inhibits mycelial growth, which could mainly be attributed to terpinen-4-ol. Furthermore, we aimed to elucidate the mechanism underlying the antifungal action in detail. To achieve this, we used a microbial calorimeter to obtain growth thermograms of fungi growing in the presence of various antifungal agents. Growth thermograms showed different heat generation patterns depending on the action of antifungal agents. One of the authors of this study, Takahashi [45], had reported the differences in growth thermograms between bactericidal and bacteriostatic agents. According to this report, imidazolidinyl urea, which shows bactericidal action, caused a delay in the rise of the growth thermogram, but it did not alter the initial growth rate. Propylparaben, which exhibits bacteriostatic action, caused a change in only the initial growth rate. The bactericidal or bacteriostatic action of antimicrobial agents are not classified properly, and most drugs are considered to be bacteriostatic up to a certain concentration and bactericidal above that concentration. Therefore, several drugs are considered to exhibit both bacteriostatic and bactericidal activities. However, we believe that the information obtained from growth thermograms will provide key clues for investigating the mechanisms of action of antifungal drugs. A mechanism of action of TBZ has been reported by Allen and Gottlieb [46]. Per their findings, TBZ inhibits the terminal electron transport system of mitochondria and exhibits highly selective toxicity against fungi. Additionally, Kano et al. [ 31], by measuring the growth thermograms of TBZ, showed that TBZ inhibits the mycelial growth of Aspergillus niger in a concentration-dependent manner. In contrast, AMPH-B, a polyene macrolide antibiotic that selectively binds to ergosterol in cell membranes, is known to disrupt cell membranes, leak intracellular substances, and kill cells [47]. Based on these reports, the theory to quantitatively characterize the antimicrobial action of drugs [45], and the pattern of the f(t) curves obtained in this study, it can be hypothesized that TBZ exerts a fungistatic effect at low concentrations and exhibits fungicidal activity at high concentrations, and that AMPH-B is a fungicidal agent. Furthermore, we analyzed the f(t) curve of Daito-gettou EO and found that there was little change in the slope of the curve between concentrations of $0\%$ and $0.05\%$, and the slope increased with the increase in concentration (>$0.1\%$). The antifungal action of Daito-gettou EO did not change the initial growth rate but caused only a delay. Therefore, these results suggest that the antifungal action of Daito-gettou EO is fungicidal. Additionally, the f(t) curve of Daito-gettou EO was notably similar to that of terpinen-4-ol as shown in Figure 5, thereby implying that the fungicidal action of Daito-gettou EO is mainly due to the action of terpinen-4-ol. In this study, the antifungal activity of Daito-gettou EO was compared by focusing on terpinen-4-ol, which is one of the main components. Terpinen-4-ol is a hydrophobic terpene that can strongly interact with microbial membrane lipids and affect membrane permeability. Polec et al. [ 48] reported that the antifungal activity of terpinen-4-ol is directly related to its incorporation into cellular membranes and is affected by the lipid composition of various pathogenic membranes. Furthermore, Li et al. [ 49] mainly attributed the antimicrobial activity of tea tree oil to the presence of terpinen-4-ol, and tea tree oil penetrated the cell wall and cytoplasmic membrane of the tested bacterial and fungal strains. In addition, their findings suggest that tea tree oil also penetrates fungal organelle membranes and exerts its antimicrobial effects by compromising the cell membrane, resulting in cytoplasm loss and organelle damage, which ultimate lead to cell death. These reports support the results of this study regarding the antifungal activities of Daito-gettou EO and terpinen-4-ol. It is presumed that Daito-gettou EO and terpinen-4-ol interact with the cell membrane of A. brasiliensis, affect the fluidity and permeability of the cell membrane, and substantially inhibit the growth of mycelia. Furthermore, Daito-gettou EO and terpinen-4-ol might disrupt cell membranes and cause intracellular substance leakage, and thereby exert a fungicidal effect against A. brasiliensis. However, it is apparent that the content of terpinen-4-ol in Daito-gettou EO is lower than that in tea tree EO, as determined using GC/MS analysis, and that Daito-gettou EO contains many other trace components in addition to terpinen-4-ol. Therefore, further investigations are necessary to clarify the detailed mechanism of antifungal action of Daito-gettou EO. Daito-gettou EO has been closely associated with the life of people inhabiting Okinawa and the Daito Islands by supporting a safe food environment. It is used extensively and is considered to be a proxy for guaranteed food safety. Daito-gettou EO is considered particularly useful as a natural preservative for food to maintain hygiene. However, it is assumed that the EO composition of gettou fluctuates with the climate factors (such as temperature and rainfall) [39], which can lead to a corresponding fluctuation in its antifungal activity. In addition, Daito-gettou EO may contain unknown antifungal components, similar to those of Shima-gettou EOs, and therefore, further studies (for example, studies on synergistic effects between EO components and the influence of EO extraction methods) are required to elucidate the mechanisms underlying this activity. ## 5. Conclusions In this study, the mechanism underlying the antifungal action of Daito-gettou EO was investigated, and the following major results were obtained. [ 1] Daito-gettou EO showed an antifungal effect against A. brasiliensis (MIC = $0.4\%$). [ 2] The main chemical components of EO were identified as γ-terpinene, terpinen-4-ol, 1,8-cineole, 3-carene, and p-cymene, which differed from the three kinds of Shima-gettou EOs used in this study. [ 3] Terpinen-4-ol, which is present in Daito-gettou EO at $17.24\%$, showed a higher antifungal activity than the other components (MIC = $0.075\%$), and the activity of Daito-gettou EO against A. brasiliensis could be attributed to this component. [ 4] Daito-gettou EO inhibited mycelial growth. [ 5] The pattern of growth thermograms, which were calorimetric observations of fungal growth in the presence of Daito-gettou EO, was similar to that of the fungicide amphotericin B. These findings imply that the mode of action of Daito-gettou EO is fungicidal; however, to confirm this, further studies on the molecular mechanisms of action are needed. ## References 1. Dorman H.J.D., Deans S.G.. Antimicrobial agents from plants: Antibacterial activity of plant volatile oils. J. Appl. Microbiol. (2000.0) 88 308-316. DOI: 10.1046/j.1365-2672.2000.00969.x 2. Arambewela L.S.R., Arawwawala L.D.A.M., Athauda N.. Antioxidant and antifungal activities of essential oil of. J. Ayurveda Integr. Med. 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--- title: Functional Enhancement of Guar Gum−Based Hydrogel by Polydopamine and Nanocellulose authors: - SolJu Pak - Fang Chen journal: Foods year: 2023 pmcid: PMC10048423 doi: 10.3390/foods12061304 license: CC BY 4.0 --- # Functional Enhancement of Guar Gum−Based Hydrogel by Polydopamine and Nanocellulose ## Abstract The development of green, biomedical hydrogels using natural polymers is of great significance. From this viewpoint, guar gum (GG) has been widely used for hydrogel preparation; however, its mechanical strength and adhesion often cannot satisfy the biomedical application. Therefore, in the present study, gelatin and a cellulose nanocrystal (CNC) were first applied to overcome the defects of guar gum hydrogel. Dopamine was self−polymerized into polydopamine (PDA) on the gelatin chain at alkaline condition, and gelatin−polydopamine (Gel−PDA) further cross−linked with guar gum and CNC via the borate−didiol bond, intramolecular Schiff base reaction, and Michael addition. CNC not only interacted with guar gum using borate chemistry but also acted as a mechanical reinforcer. The obtained Gel−PDA+GG+CNC hydrogel had an excellent self−healing capacity, injectability, and adhesion due to the catechol groups of PDA. Moreover, dopamine introduction caused a significant increase in the anti−oxidant activity. This hydrogel was cyto− and hemo−compatible, which implies a potential usage in the medical field. ## 1. Introduction Hydrogel is a semi−solid and soft polymer network that can retain much water and keep its shape well. According to gelation chemistry, hydrogel can be classified into covalent hydrogels and noncovalent hydrogels. Covalent hydrogels can be formed by covalent bondings, such as EDC coupling, click reactions, Michael additions, disulfide bonds, and free−radical polymerization. Electrostatic interactions (mainly by multivalent cations, such as Ca2+ and Fe3+), polymer–polymer interactions, hydrophobic interactions, and π–π interactions (dextran or gelatin) are the main force to form noncovalent hydrogels. Meanwhile, hydrogel formation can be initiated directly or indirectly; so, intrinsic and indirect triggers are defined. Intrinsic triggers, the change of pH or temperature, components mixing, and enzyme addition can directly modify the polymer property or accelerate the cross−linking to initiate the gelation. Indirect triggers include temperature, ultrasound, and electromagnetic radiation which stimulates the cross−linker (cargo) release from carriers or activates the photo−crosslinker [1]. Hydrogel is known to form a physical or chemical barrier to microbial invasion and supply a relatively wet environment around the applied sites [2]. Because of its high hydrophilicity and biodegradability mechanical strength, hydrogel has been remarked as a novel material in biomedical fields including drug delivery [3], bone regeneration [4], wound dressing [5,6], and biosensors [7]. However, the lack of injectability and self−healing ability limits the broad medical application of traditional hydrogels [8]. On the other hand, many biomedical hydrogels have adopted various synthetic polymers, including polyvynyl alcohol (PVA), polyacrylic acid (PAA), and polyacrylamide (PAM), and undesired side effects might be present after application [9,10]. Therefore, hydrogel fabrication from natural and biocompatible polymers has gained great interest all over the world. Guar gum (GG) is a natural polysaccharide isolated from the seed of a legume. Its main chain consists of linear 1, 4 linkages of β−D−mannopyranose, while the branch is composed of 1,6 linkages of α−d−galactopyranose. With abundant hydroxyl groups, guar gum has been applied for the facile fabrication of adhesive and stimuli−responsive hydrogels. Among cross−linking chemistry for guar gum−based hydrogel, the borate−didiol interaction was investigated, leading to the promising functional properties [11]. Nonetheless, the instability and weakness of neat guar gum hydrogel restrained its wide application. Actually, borate−based guar gum hydrogel showed a change in mechanical strength between 25 °C and 37 °C, which is a dissatisfying property for wound healing or cell scaffolding [12]. It was known that galactomannan is sensitive to temperature−based degradation [13]. The rheological value of guar guam solutions (storage modulus and loss modulus) decreased with the storage time, which probably means the depolymerization of macromolecules [14]. Moreover, taken with the reversibility of the borate ester bond, guar gum hydrogel still remains unstable with low strength. In this regard, some researchers tried to overcome this defect through the involvement of other polymers, such as chitosan and PAA [15]. Gelatin, a promising natural protein with various reactive functional groups, is renowned for its good gel−forming capacity. However, gelatin hydrogel obtained by unfolding or other physical cross−linking is quickly dissolved and mechanically weak so it cannot meet the practical requirement. Miscellaneous chemistry including a cross−linker (genipin or metal [16]) and grafting (thiol or amino grafting [17,18]) enables the formation of stable, tough, and stimuli−responsive hydrogels from gelatin. Recently, dopamine inspired by mussels has opened many possibilities to design and synthesize the novel hydrogels with excellent functional properties. Catechol groups of dopamine can endow the hydrogel with good tissue adhesion and self−healing ability [19]. Up to date, two major dopamine chemistries have been introduced for the modification of hydrogel precursors. The first chemistry−grafting is normally performed via the covalent bond between polymers and dopamine, and the most representative method is a (dimethylamino) propyl)−3−ethylcarbodiimide hydrochloride (EDC)/N−hydroxysuccinimide (NHS) coupling reaction [20,21]. Dopamine chemistry coating is more facile way to modify the polymer or metal surface [22]. To the best of our knowledge, surfaces including cells and tissues can be decorated with dopamine directly. A catechol of dopamine can be easily transferred into quinones in an alkaline environment (pH > 8.5). Then, it can be sequentially self−polymerized to form a polydopamine on the substrate surface. Similarly, tannic acid coating on the nanocellulose was reported to participate in the tough and adhesive hydrogels [23,24]. However, dopamine coating chemistry has not been extensively investigated for different polymers, and its functional and biological effect still remains unclear. Nanocellulose, such as a cellulose nanocrystal (CNC) and a cellulose nanofiber (CNF), is one of the versatile and sustainable polysaccharides derived from the most abundant resources—cellulose. The good biocompatibility and mechanical property has enabled its wide application for functional hydrogels through a Schiff base reaction, borate−didiol bond, and ionic interaction with other polymers such as PVA, alginate, and gelatin [25,26]. From the above viewpoint, we aimed to prepare an environment−friendly hydrogel mainly based on the guar gum, which will be utilized as wound dressings or tissue scaffolds. The adhesion and anti−oxidation of guar gum hydrogel were significantly improved with the introduction of the gel−polydopamine complex, which is easily produced. CNC can also contribute to the mechanical reinforcement of guar gum−based hydrogel. The obtained Gel−PDA+GG+CNC hydrogel showed a good cytocompatibility and hemolysis ratio, suggesting great potential for biomedical application. ## 2.1. Materials Gelatin (Type A, 200 g bloom) and dopamine were purchased from Sigma−Aldrich (Shanghai, China). A cellulose nanocrystal (CNC: obtained by sulfuric acid hydrolysis, the degree of polymerization is 240) was supplied by Tianjin Woodelf Biotechnology Company (Tianjin, China). Guar gum (viscosity: 2500–3000 mPa·s) was granted by Beijing Biotopped Science & Technology Company (Beijing, China). Borate (Na2B4O7·10H2O), potassium bromide (KBr), and sodium hydroxide were provided by Sinopharm Chemical reagent Company (Shanghai, China). Phosphate buffer saline (PBS, pH 7.2–7.4), saline solution ($0.9\%$ NaCl), 1, 1−diphenyl−2−picrylhydrazyl (DPPH) free radical, and cell counting kit−8 (CCK−8) were procured from Solarbio Science & Technology Company (Beijing, China). ## 2.2. Synthesis of Gel−PDA The Gel−PDA complex was synthesized from gelatin and dopamine at alkaline conditions [27]. Briefly, 3.5 g of gelatin was fully dissolved in 35 mL of distilled water at 40 °C under stirring, and pH was adjusted to 8.5 with a $5\%$ sodium hydroxide solution. A total of 500 mg of dopamine hydrochloride was added into the gelatin solution and stirred slowly at 40 °C for 6 h to perform the oxidative polymerization of dopamine on the gelatin chain. The obtained gelatin−polydopamine complex (Gel−PDA) was freeze−dried at −60 °C and further used for its characterization and hydrogel fabrication. The Gel−PDA synthesis was performed in triplicate. ## 2.3.1. Ultraviolet−Visible (UV–Vis) Spectroscopic Analysis An ultraviolet–visible (UV–Vis) spectrometer (Beijing Persee General Instruments Co., Ltd, Beijing, China) was used to confirm the PDA formation on the gelatin [28]. Briefly, the as−prepared Gel−PDA solution was diluted to $0.1\%$ consistency with distilled water, while $0.1\%$ gelatin solution was also prepared. Full scanning within the range of 200–700 nm wavelength was performed for gelatin and the Gel−PDA solution. The analysis was repeated three times for each sample. ## 2.3.2. Fourier−Transform Infrared Spectroscopic Analysis Fourier−transform infrared spectroscopy (Thermo Nicolet Corporation, Madison, WI, USA) was applied in order to characterize the PDA coating’s effect on the gelatin matrix [27]. Freeze−dried Gel−PDA was pulverized finely with a fully dried KBr crystal (mass ratio of Gel−PDA:KBr = 1:100) under an infrared ray lamp. Consequentially, this powder mixture was shaped into a thin disc to perform the analysis, which ranged from 400 to 4000 cm−1. Pure gelatin was also analyzed as a control. All procedures were repeated three times for each sample. ## 2.3.3. X-ray Diffraction (XRD) Analysis X-ray diffraction (XRD) analysis (Bruker Corporation, Billerica, United States) was further performed in the range of 2 θ = 10–80° at 5° min−1 speed to elucidate the possible change of gelatin crystallinity by dopamine polymerization [29]. Lyophilized Gel−PDA and pure gelatin were used for the XRD analysis. The experiments were performed in triplicate for each sample. ## 2.4. Fabrication of Gel−PDA+Guar Gum+CNC Hydrogel The Gel−PDA+GG+CNC hydrogel was prepared following the protocols of [12] with a slight modification. A total of 60 mg of freeze−dried Gel−PDA was dissolved in distilled 5 mL of water at 40 °C, and 30 mg of CNC powder (obtained by the freeze−drying of neat CNC $2.5\%$ dispersion) was dispersed in the Gel−PDA solution homogeneously under stirring (500 rpm) for 30 min followed by ultrasonication for 30 min. Afterward, 80 mg of guar gum powder was fully dissolved in the Gel−PDA+CNC mixture dispersion at 45 °C, in which 350 uL of $2\%$ borate solution was dropwise added under stirring to form the hydrogel. Then, the hydrogel was further stabilized at 37 °C for 15 min. Additionally, Gel−PDA+GG, GG+CNC, and neat GG hydrogel were fabricated as a control. ## 2.5.1. Morphological and Structural analysis The morphological profile of hydrogels (Gel−PDA+GG+CNC and GG) was analyzed with a scanning electron microscope (Hitachi High−Technologies Corporation, Tokyo, Japan) at 10 kV, and 100 μm resolution photos were taken. Freeze−dried hydrogel pieces were cut by scissors to reveal the proper cross−section. The piece was fixed on a copper board and sprayed with gold [30]. FT−IR analysis was adopted to illustrate the functional groups of the lyophilized Gel−PDA+GG+CNC, Gel−PDA+GG, GG+CNC, and neat GG in the range of 400–4000 cm−1 at a resolution of 4 cm−1 [18]. ## 2.5.2. Rheology and Self−Healing Test The rheological property of hydrogel was assessed using a TA rheometer (Waters Co., Milford, MA, USA) [31]. All the measurements were performed using a 40 mm parallel plate with a 1000 μm gap at 25 °C. Frequency sweep was performed within the angular frequency range of 0.1–100.0 rad s−1 at a $1.0\%$ strain level, whereas 10.0 rad s−1 and 1.0–$1000.0\%$ were set as angular frequency and strain range, respectively. Storage modulus (G′) and loss modulus (G″) were recorded to evaluate the hydrogel rheology. For the macroscopic self−healing test, hydrogel samples were cut into two pieces and put together without any additional stimulus. After 10 min, pictures were captured to visualize the self−healing property of hydrogel. ## 2.5.3. Tissue Adhesive Test The tissue adhesive property of hydrogel was measured with wet pig skin using an Instron−5943 mechanical tester (Instron corporation, Canton, United states) [31]. The fresh pig skin without extra fat was purchased from a local market and cut into rectangular pieces (10.0 mm × 50.0 mm). Before the test, pig skin pieces were cleaned using saline solution ($0.9\%$ NaCl) and immersed in PBS (pH 7.4) at 10 °C for 12 h to keep the surface wet. A total of 200 μL of as−prepared hydrogel was injected into a 15 mm × 15 mm area of the pig skin piece, on which another piece was put to adhere to each other.. Sequentially, these pieces were immediately pressed together using a constant force and pulled apart with a 50 N load cell at a gauge length of 50.0 mm, as well as a cross−head speed of 5.0 mm min−1. The adhesive strength (Pa) was calculated by dividing the maximum load (N) by the cross−linked area (m2). The analysis was performed three times for each hydrogel to obtain the average value. ## 2.5.4. Anti−Oxidant Activity Assay A 1,1−diphenyl−2−picrylhydrazyl (DPPH) radical was used to evaluate the hydrogel anti−oxidant capacity [32]. A total of 100 mL hydrogel was added into a centrifuge tube containing 1 mL 200 μM DPPH solution (in ethanol) and was shake−incubated mildly at 30 °C for 1 h in the dark. Then, 100 μL of supernatant was transferred into a 96−well microplate to measure the absorbance at 517 nm using a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). The DPPH solution was used as a control, and scavenging capacity was calculated based on the following equation:DPPH scavenging % = (A0 − A1)/A0 × $100\%$ where A0 is the absorption of the DPPH solution in ethanol, while A1 is the absorption of the hydrogel sample+DPPH ethanol solution. ## 2.5.5. Hemolysis and Hemostasis Assay Sprague Dawley (SD) rats (female, 7–8 weeks, 220–240 g) were obtained from Charles River Laboratories Inc. (Wilmington, United States) and raised in a fixed environment (12 h, light/dark cycle, 25 ± 2 °C, $55\%$ ± $10\%$ humidity) so that they can obtain free access to water and feed. Animal experiments were governed by the Animal Protection Professional Committee of China Agricultural University. After adaptive feeding for 1 week, SD rats were anesthetized with chloral hydrate, and the fresh blood was taken into the centrifuge tube. The erythrocytes were immediately separated from the blood by centrifugation at 1500 rpm for 10 min and rinsed with PBS three times. Finally, $5\%$ erythrocyte suspension was prepared by dilution with PBS. A total of 100 μL of hydrogel was added into a 2 mL centrifuge tube containing a 500 μL erythrocyte suspension, and was then shake−cultured at 37 °C at 100 rpm. PBS and $0.1\%$ triton were used as the blank and positive control, respectively. After 1 h, the culture was centrifuged at 1500 rpm for 10 min, and 100 μL of supernatant was taken into a 96−well microplate to measure the absorbance at 540 nm using a microplate reader (Thermo Fisher Scientific). The hemolysis ratio was calculated using the following equation:Hemolysis ratio (%) = (As − Ab)/(Ap − Ab) × $100\%$ where As, Ab, and Ap are the absorbance values for the erythrocyte suspension with samples, PBS, and $0.1\%$ triton solution, respectively [33]. All experiments were repeated three times. For the hemostasis assay, the abdomen of an anesthetized rat was cut to reveal the liver, and filtration paper and plastic film were placed carefully below the isolated liver to prevent body fluids penetration. Bleeding was caused by cutting the liver with scissors, and then hydrogel (300 μL) was quickly applied for the hemostasis. The blood weight on the filtration paper was measured when no more bleeding was shown [34]. All experiments were repeated three times. ## 2.5.6. Cytotoxicity Assay Hydrogel cytotoxicity was measured following a similar protocol to [33,35]. A human hepatocellular carcinomas (HepG 2) cell was procured from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) [35]. The hydrogel samples were exposed to UV radiation for 30 min for sterilization, and then immersed in a DMEM medium (containing $10\%$ fetal bovine serum, 100 unit/mL penicillin, and 100 μg/mL streptomycin) for 24 h at 37 °C. For the cytotoxicity experiment, a vial of frozen HepG 2 cells was thawed and seeded on 25 cm2 flasks. The cells were incubated in a DMEM medium (containing $10\%$ FBS and $1\%$ penicillin/streptomycin) up to $80\%$ fullness and treated with trypsin. The HepG 2 cells were seeded in a 96−well plate at a concentration of 1 × 104 (100 μL), and then the plate was further incubated for 18 h at 37 °C to promote stable cell attachment. Consequently, the culture medium was replaced with 100 μL of hydrogel extract to incubate at 37 °C, whereas untreated cells were a positive control. After 48 h, the culture medium was removed and rinsed twice with PBS. A total of 100 uL of a fresh DMEM medium, including a CCK−8 reagent (10 μL), was added to cell−attached wells and blank wells (blank), and then the plate was kept in the dark for 1 h at 37 °C. Finally, the absorbance of the solution was analyzed at 450 nm using a microplate reader (Thermo Fisher Scientific, Waltham, USA). Each sample had a six−well treatment in parallel. ## 2.6. Statistical Analysis The data of all experiments were expressed as the average ± standard deviation. Statistical analysis was performed by one−way ANOVA. Symbols , , and ** correspond to a significance level of 0.05, 0.01, and 0.001, respectively. ## 3.1. PDA Coating on the Gelatin Matrix Dopamine with an active catechol group is easily oxidized at the alkali condition and further polymerized to form a polydopamine, and this in situ polymerization is one of the promising and facile strategies to modify the polymers [19,36,37]. The gelatin solution was transparent at the beginning; however, it turned light pink and finally became black as the polymerization time increased. The UV−Vis spectrum showed an intrinsic peak at 230 nm for both gelatin and Gel−PDA, while a weak peak at 280–290 nm of gelatin was intensified at the Gel−PDA spectra, which means that there was PDA formation on the gelatin chain [27,38] (Figure 1a). Actually, both gelatin and dopamine have aromatic moieties; therefore, their complex might result in the overlapping of the π–π transition peak. Additionally, a weak peak shown around 420 nm is identified as the representative peak of PDA synthesis on the gelatin, which is time−dependent [28,39]. It was reported that dopamine polymerization might cause the broad alteration ranging from 300–600 nm at the gelatin spectra. Except for the emerging peak at 280 nm and 420 nm, other intrinsic peaks can be found at 395 nm (assigned to yellow chromophore dopamine o−quinone) and 300–475 nm (assigned to aminochrome), respectively [28]. Some researchers also witnessed this kind of interaction. For example, dopamine polymerization with the presence of cinnamaldehyde revealed a new peak at 438 nm in the PDA−cinnamyaldehyde complex [40]. In contrast, dopamine grafting via a EDC/NHS coupling reaction to the gelatin did not generate the peak at 400–420 nm because catechol was not oxidized into quinone [12]. The above results demonstrated the successful polymerization of dopamine on the gelatin chain. XRD analysis showed that both gelatin and Gel−PDA have a wide peak at 21 degrees, which is typical for disordered polymers (Figure 1b). PDA coating did not cause an obvious decrease in the peak strength, implying that PDA coating has little impact on the gelatin crystallinity. At the FT−IR spectra, the peak at 2830 cm−1 corresponding to −CH2 symmetric stretching of neat gelatin was shifted to 2829 cm−1 for Gel−PDA (Figure 1c). The stretching vibration band (3399 cm−1) of N−H and O−H groups in the gelatin was diminished to some extent in the Gel−PDA. Meanwhile, the band for C−H stretching vibration exhibited a big shift (from 3140 cm−1 in gelatin to 3175 cm−1 in Gel−PDA) and was weakened by dopamine polymerization, suggesting that PDA strongly interacted with the functional groups of gelatin. Moreover, a new peak at 1584 cm−1, which is corresponds to the N−H bending vibration, also confirmed the PDA presence on the Gel−PDA complex [41]. A similar trend was also witnessed in the other reports. Researchers found that the broad band at 3500–3000 cm−1 originally assigned to the stretching vibration of −NH, −OH, and aromatic −CH of dopamine might be weakened rather than belonging to the stretching vibration of free amino groups of PDA after polymerization [42]. In the case of nanocellulose (CNC), it was mentioned that the alteration at 1200–1600 cm−1 is mainly associated by the dopamine polymerization. PDA complexation with CNC was identified by some emerging peaks at 1508 cm−1 and 1284 cm−1, which can be associated with N−H scissoring and C−O stretching, respectively [36]. ## 3.2. Fabrication and Characterization of Hydrogel The formation mechanism was illustrated in Scheme 1. The main driving force to form a hydrogel is a borate−didiol interaction between guar gum molecular chains, as well as guar gum and CNC. Interestingly, hydroxyphenol groups of dopamine or tannic acid are also reported to make a complexation with borate, and this fact supported the possible interaction of Gel−PDA and guar gum or CNC [43,44]. Afterward, hydroxyl groups in guar gum or CNC are feasible to form hydrogen bonds with amino or catechol groups in Gel−PDA. Within the Gel−PDA chains, Schiff base or Michael addition might be present between primary amine groups of gelatins and quinones produced from catechols of PDA. Adhesive, self−healing, and tough hydrogel was formed as soon as the borate solution was dropwise added into the polymer mixture dispersions. FT−IR analysis exhibited that all hydrogels have broad peaks around 3412 cm−1 and 1635 cm−1 corresponding to O−H stretching vibration and C=O stretching vibration, respectively (Figure 2a). The GG+CNC hydrogel had no visible difference from the neat GG hydrogel except for the shifting of the C=O stretching vibration band (from 1631 cm−1 to 1611 cm−1) because of structural similarities between GG and CNC. This C=O stretching vibration around 1630 cm−1 might have originated from the amide bond or quinone groups (oxidation form of catechol in PDA). Therefore, the shifting of this band suggested the CNC complexation with GG during hydrogel synthesis. Meanwhile, an absorption band shown at 1362 cm−1 can be assigned to an asymmetric extension of B−OC), confirming the cross−linking between the borate and the catechol group [45]. Gel−PDA addition caused a new peak at 1535 cm−1 which is associated with the N−H bending vibration of PDA. The C=O stretching vibration in Gel−PDA+GG+CNC hydrogel shifted from 1631 cm−1 to 1662 cm−1, implying the probable interaction between Gel−PDA and GG or CNC. The microstructure was observed using SEM. All hydrogels displayed a homogenous and porous network, confirming the formation of typical gel structures (Figure 2b). Neat guar gum had big pores at 50 μm, while that of Gel−PDA+GG+CNC hydrogel was about 30 μm. Moreover, the structure of guar gum hydrogel became significantly tight with the addition of Gel−PDA and CNC, suggesting that cross−linking density might be increased. Previous reports indicated that dopamine polymerization and catechol−based chemistry can improve cross−linking, leading to uniform pore sizes [33]. The higher cross−linking is known to decrease the pore sizes in the hydrogel, while the lower molecular weight of precursor polymer could loosen and enlarge the pore sizes [46]. ## 3.3. Rheology, Self−Healing, and Injectability of Hydrogel The rheological property of hydrogel is very important for biomedical application and the storage modulus (G′), and loss modulus (G″) was measured in two ways: strain sweep and frequency sweep. Within the frequency range of 0.01–100 Hz, the storage modulus (G′) and loss modulus (G″) of neat GG hydrogels was measured. As shown in Figure 3a, G′ of GG was almost the same as G″ at the first step of the frequency range, and this trend was reversed at the remaining steps, finally showing the large difference between the two moduli. Actually, the higher storage modulus than the loss modulus in the whole range is a critical index to determine the viscoelastic property; therefore, neat GG hydrogel prepared in our work might be a weak gel. A similar result was also witnessed by other reporters, who found that borate−guar gum hydrogel prepared at $0.5\%$ consistency has almost the same G′ and G″ values at the low−frequency zone [11]. Considering the relatively high consistency of guar gum ($1.5\%$) in our work, the presence of a low G′ value can be attributed to the difference in the molecular weight and the polymerization of guar gum. Compared with the GG hydrogel, G′ of GG+CNC, Gel−PDA/GG, and Gel−DA+CNC were clearly higher than G″ across the whole frequency range, implying that these hydrogels possess excellent mechanical properties. The storage modulus of GG hydrogel (152 Pa) was significantly enhanced with the addition of Gel−PDA(267 Pa), while CNC also showed a mechanical reinforcement effect. The highest storage modulus was displayed in Gel−PDA+GG+CNC hydrogel (288 Pa) (Figure 3c). The loss factor, tan δ, is a critical index that determines hydrogel behavior, and it was usually regarded that a lower tan δ reflects a more stable elastomer [13]. In our work, the tan δ of all hydrogel samples was smaller than 1 and weakly frequency dependent, implying a gel−like status. The tan δ of the neat GG hydrogel was 0.29 at the final frequency step, whereas this value was clearly reduced to 0.11 with the CNC (Figure 3d). This result confirmed the critical role of CNC in the stability of GG hydrogel behavior. There was no significant difference in tan δ between GG+CNC, Gel−PDA+GG, and Gel−PDA+GG+CNC hydrogel, implying that they are more stable than GG hydrogel. Especially, Gel−PDA introduction in the GG hydrogel brought the obvious improvement of all rheological indexes, suggesting that Gel−PDA participation in the GG hydrogel is a borate cross−link, not a physical unfolding (because hydrogel preparation was performed at 37 °C). From the strain sweep result, the intersection point of G′ and G″ of GG hydrogel was about $290\%$ of strain (Figure 3b). After this critical strain, G′ of GG hydrogel decreased quickly and started to be lower than G″, meaning that the hydrogel network is damaged and turned into a solution status. With the Gel−PDA and CNC, the intersection did not happen even after $1000\%$ strain, and G′ constantly kept a higher value than G″. This result confirmed the enhancement of mechanical properties of the GG hydrogel by the Gel−PDA and CNC. With regards to the biomedical application of hydrogel, self−healing capacity is also important since many body sites are often subjected to bending, twisting, or other movements, which can easily cause hydrogel damage. Due to the reversible nature of borate cross−linking, GG−based hydrogel showed a fast self−healing capacity [10]. Likewise, GG+CNC, Gel−PDA+GG, and Gel−PDA+GG+CNC hydrogel all exhibited excellent self−healing capacities due to the involvement of Gel−PDA and CNC in the borate−mediated chemistry. For the self−healing test, round−shaped Gel−PDA+GG+CNC hydrogel was cut into two pieces and then put together. After 5 min, the two pieces were reattached and perfectly self−healed without any external stimulus. This reformed piece was stretchable (Figure 4a). Furthermore, the separated two pieces on the finger fully adhered to each other in 10 min when they were brought into contact, and this single piece was not damaged by bending (Figure 4b). Our results confirmed the promising application of hydrogel in the biomedical field for wound healing or biosensors. When hydrogel is applied at the subcutaneous sites or intestine, injectability can significantly affect its usage. Therefore, good injectability is also an important index of biomedical hydrogels. However, there exists some contradiction between mechanical property and injectability. If the hydrogel does not have a good shear−shining property, it cannot be output from the narrow needle [31]. So, an excessively tough hydrogel is difficult to be injected. In our study, the neat GG hydrogel was hard to inject with the 23−gauge needle despite its low mechanical strength. However, Gel−PDA+GG and Gel−PDA+GG+CNC hydrogel were successively injected through a 23−gauge needle, implying that Gel−PDA can improve the shear−shinning property of the GG hydrogels. Additionally, both hydrogels still kept their fibrous appearance after they were injected under the water (supplementary material—video S2). ## 3.4. Tissue Adhesion and Anti−Oxidant Capacity The tissue adhesive capacity of hydrogels was measured by the lap shear test using wet pig skin, and a macroscopic adhesive test was performed as well. As shown in Figure 5a, the neat GG hydrogel had 18.78 ± 3.58 kPa of lap shear strength, and this adhesive value was increased to 21.71, 29.74, and 35.23 kPa for GG+CNC, Gel−PDA+GG, and Gel−PDA+GG+CNC hydrogel, respectively. CNC probably does not cause the visible improvement of adhesion to pig skin, whereas Gel−PDA brought a great difference in tissue adhesion. It was reported that the polymerization reaction in the alkaline environment is similar to the catechol oxidation progress in mussel−secreted proteins under seawater, and this process endows the mussel with very high adhesion because it can protect the “adhesive” catechol groups by partially oxidizing into ο−quinones in this way [47]. Abundant catechol groups of PDA are easily deprotonated to become reactive quinone groups, and then consequentially interact with amine, thiol, or imidazole groups of pig skin proteins via the Michael−type addition and Schiff−base reaction [32]. Additionally, some physical interactions originating from catechol groups including hydrogen bonds, π–π stacking, and π–cation interactions can contribute to the good adhesion of the hydrogel. All hydrogel samples showed significantly higher values of tissue adhesion compared with commercial dressing (around 5 kPa), confirming their potential usage as tissue adhesives (Supplementary material—video S1). Excessive production of reactive oxygen species (ROS) in the damaged tissue is one of the critical obstacles to hinder quick recovery. In this sense, the anti−oxidant activity of biomedical hydrogels to remove ROS can determine its treatment efficiency. As shown in Figure 5c, the DPPH free radical scavenging ratio of GG+CNC hydrogel did not have a significant difference from that of GG hydrogel after 1 h of incubation. The anti−oxidant capacity of Gel−PDA+GG and Gel−PDA+GG+CNC hydrogel was increased by about five times, suggesting that catechol groups in the PDA can obviously improve the anti−oxidant activity. The improvement of the ROS scavenging ratio by the catechol group was also mentioned in previous papers [48,49]. ## 3.5. Hemostasis, Hemolysis, and Cytotoxicity of Hydrogels Hemostasis is the crucial property of biomedical hydrogel when it is used as wound dressings. With the rapid stop of bleeding, hydrogel dressing can exert good wound closure. The hemostatic capacity of hydrogel was measured using a rat hepatic hemorrhage model. As shown in Figure 6a, compared with the bleeding of 149.33 mg in the control group, GG and GG+CNC hydrogel showed obviously small bleeding (28.4 and 25.16 mg, respectively). Moreover, the blood loss of GG hydrogel was further decreased with the Gel−PDA addition to a great extent (19.56 and 14.16 mg for Gel−PDA+GG and Gel−PDA+GG+CNC, respectively). This result is well−matched with the rheology and adhesion of hydrogels. Previously, it was already reported that good adhesion and mechanical strength enable the hydrogel to adhere to the injury sites quickly to seal the bleeding. Meanwhile, amino groups of Gel−PDA were found to stimulate erythrocyte aggregation. In addition, the vasoconstrictive activity of dopamine is helpful to reduce blood loss because the blood vessels can be constricted rapidly [50]. A hemolysis assay was performed to clarify the possible effect of hydrogels on the red blood cells (RBCs), and there was no significant difference between hydrogel samples (Figure 7a). Considering that the hemolysis ratio limit for biomaterials is below <$5\%$, all hydrogels exhibited good hemocompatibility, suggesting their medical safety. The cytocompatibility analysis was performed using a CCK−8 assay kit for hydrogel extract liquid, and the result showed that all hydrogel samples have no clear cytotoxicity (Figure 7b). Guar gum and nanocellulose are all nontoxic biopolymers, while dopamine inspired by mussels is also a safe substance with low cytotoxicity, so it is extensively used for the bioactive modification of tissues. Other researchers also demonstrated similar results. GG hydrogel even showed higher cell viability than the control (DMEM medium), suggesting the cell proliferative effect. Additionally, the gel−dopamine introduction in the GG hydrogel also caused the improvement of cell growth after 24 h. However, the Ag loading into the gel−dopamine reduced the cell viability to some extent [12]. In another paper, silk fibrion PDA hydrogel had a higher cell density than culture medium or silk fibrion hydrogel. Nonetheless, the higher consistency of dopamine was not preferable for cellular growth because it might inhibit cell activity [42]. So, a slight decrease in the cell viability of Gel−PDA+GG+CNC hydrogel is probably attributed to the higher dopamine concentration. ## 4. Conclusions In summary, we prepared functional hydrogels which can be used as wound dressing and tissue scaffolding. The Gel−PDA and CNC complexation successively overpassed the shortcomings of guar gum hydrogel. PDA coating on the gelatin is a facile and effective method to improve the gelatin property, leading to the proper involvement of gelatin in the borate−based guar gum hydrogel. Meanwhile, abundant hydroxyl groups of CNC enabled its contribution to the stable hydrogel formation. Therefore, tissue adhesion and rheology of GG hydrogels are obviously enhanced. Additionally, Gel−PDA+GG+CNC hydrogels had good mechanical strength, high ROS scavenging activity, acceptable hemocompatibility, and cytotoxicity. Our study confirmed that PDA coating chemistry can be a promising approach to modifying the hydrogel precursor polymers, broadening its application in the biomedical field. ## Figures and Scheme Figure 1: Characterization of dopamine polymerization on the gelatin chain. (a) UV−Vis spectrum of gel and Gel−PDA, (b) XRD analysis, (c) FT−IR spectrum. Scheme 1: The main mechanism for Gel−PDA+GG+CNC hydrogel. Figure 2: FT−IR and SEM analysis of hydrogels. (a) FT−IR spectrum of GG, GG+CNC, Gel−PDA+GG, and Gel−PDA+GG+CNC hydrogel, (b) SEM image of hydrogel. (left) SEM image of GG hydrogel, (right) Gel−PDA+GG+CNC hydrogel. Figure 3: Rheological analysis of hydrogels. (a) Frequency sweep analysis, (b) strain sweep analysis, (c) the highest storage modulus (G′) of hydrogels, (d) the loss factor (tan δ) based on the frequency sweep (* p < 0.01, *** p < 0.001).* Figure 4: Self−healing test of Gel−PDA+GG+CNC hydrogel. (a) Stretchable self−healed hydrogel, (b) unbreakable self−healing on the finger. Figure 5: Adhesion and anti−oxidant activity analysis of hydrogels. 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--- title: Doxorubicin and Cisplatin Modulate miR-21, miR-106, miR-126, miR-155 and miR-199 Levels in MCF7, MDA-MB-231 and SK-BR-3 Cells That Makes Them Potential Elements of the DNA-Damaging Drug Treatment Response Monitoring in Breast Cancer Cells—A Preliminary Study authors: - Anna Mizielska - Iga Dziechciowska - Radosław Szczepański - Małgorzata Cisek - Małgorzata Dąbrowska - Jan Ślężak - Izabela Kosmalska - Marta Rymarczyk - Klaudia Wilkowska - Barbara Jacczak - Ewa Totoń - Natalia Lisiak - Przemysław Kopczyński - Błażej Rubiś journal: Genes year: 2023 pmcid: PMC10048428 doi: 10.3390/genes14030702 license: CC BY 4.0 --- # Doxorubicin and Cisplatin Modulate miR-21, miR-106, miR-126, miR-155 and miR-199 Levels in MCF7, MDA-MB-231 and SK-BR-3 Cells That Makes Them Potential Elements of the DNA-Damaging Drug Treatment Response Monitoring in Breast Cancer Cells—A Preliminary Study ## Abstract One of the most innovative medical trends is personalized therapy, based on simple and reproducible methods that detect unique features of cancer cells. One of the good prognostic and diagnostic markers may be the miRNA family. Our work aimed to evaluate changes in selected miRNA levels in various breast cancer cell lines (MCF7, MDA-MB-231, SK-BR-3) treated with doxorubicin or cisplatin. The selection was based on literature data regarding the most commonly altered miRNAs in breast cancer (21-3p, 21-5p, 106a-5p, 126-3p, 126-5p, 155-3p, 155-5p, 199b-3p, 199b-5p, 335-3p, 335-5p). qPCR assessment revealed significant differences in the basal levels of some miRNAs in respective cell lines, with the most striking difference in miR-106a-5p, miR-335-5p and miR-335-3p—all of them were lowest in MCF7, while miR-153p was not detected in SK-BR-3. Additionally, different alterations of selected miRNAs were observed depending on the cell line and the drug. However, regardless of these variables, 21-3p/-5p, 106a, 126-3p, 155-3p and 199b-3p miRNAs were shown to respond either to doxorubicin or to cisplatin treatment. These miRNAs seem to be good candidates for markers of breast cancer cell response to doxorubicin or cisplatin. Especially since some earlier reports suggested their role in affecting pathways and expression of genes associated with the DNA-damage response. However, it must be emphasized that the preliminary study shows effects that may be highly related to the applied drug itself and its concentration. Thus, further examination, including human samples, is required. ## 1. Introduction Breast cancer is still among women’s most common leading causes of death worldwide [1]. The main challenge is an early diagnosis as well as personalized therapy adjustment. They both require the identification of specific qualitative and quantitative assessments of reliable parameters that could be used as markers and therapy targets, respectively. The main obstacle to therapy efficacy is the resistance of cancer cells to drugs. This is driven by different pathways, including individual genetic characteristics and multi-drug resistance, cell death inhibition (apoptosis suppression), altered drug metabolism, epigenetics, enhanced DNA repair and gene amplification [2]. As reported below (Table 1), all these processes can be controlled by the specific miRNA-controlled expression of certain genes. Another critical characteristic of breast cancer cells (apart from immortality) is the ability to metastasize associated with altered adhesion, migration and invasion. It is mainly related to signaling pathways controlled by different genes or their phosphorylated/dephosphorylated expression products. Moreover, miRNAs can control genes that regulate adhesion molecules and cell–cell interactions (Table 1). Consequently, it is difficult to identify a specific signature of cancer cells with so many variables. Nevertheless, epigenetic factors can significantly modulate these processes, and microRNA profiling seems to be a promising strategy in diagnostics and therapy response monitoring. Among numerous studies performed so far, some particular miRNAs can be perceived as suitable candidates, especially since most of them are commonly associated with different cancer types, including breast cancer. The idea of miRNA functioning as early biomarkers for the evaluation of drug efficacy and drug safety was proposed not only in cancer but also in other diseases, including multiple sclerosis [3], bipolar disorder [4], diabetes [5] and others [6]. Thus miRNA profiling, supported by bioinformatic analysis, is perceived as a specific and sensitive biomarker for evaluating drug efficacy/resistance and drug safety in patients [7]. Specifically, it was shown that blood-borne miRNA profiles monitored over time have the potential to predict complete pathological responses in breast cancer [8]. Altogether, miRNA assessment shows some potential as a marker in pharmacogenomics (as modulators of pharmacology-related genes). It can be evaluated using low-invasive methods providing high specificity and sensitivity. However, since one miRNA can target different mRNA, a comprehensive profiling study must be performed to obtain an informative and valuable pattern.genes-14-00702-t001_Table 1Table 1Contribution of selected miRNAs to breast cancer metabolism.miRNAEffect and Pathways Mediated in Breast CancerRef.miRNA-21(-3p and -5p)tumor growth, cancer cells proliferation, metastasis, invasion, sensitivity to chemotherapy, modulation of cancer-related gene expression[9,10]miRNA-106a-5pcancer cell proliferation, colony-forming capacity, migration, invasion, breast cancer cell apoptosis and sensitivity to cisplatin, DNA damage response, suppression of the ATM-associated pathway[11,12]miRNA-126(-3p and -5p)cancer cell migration, tumor growth, proliferation, invasion and angiogenesis of triple-negative breast cancer cells[13,14,15]miRNA-155(-3p and -5p)inflammation, apoptosis, adhesion[1,16,17]miRNA-199b(-3p and -5p)cancer aggressiveness, tumor growth, angiogenesis[18,19,20]miRNA-335(-3p and -5p)sensitivity of triple-negative breast cancer cells to paclitaxel, cisplatin and doxorubicin[21] ## 2.1. Cell Culture Three cell lines representing different molecular subtypes of breast cancer were enrolled in the study, i.e., (i) MCF7 (ER/PR+, HER2low, TP53WT), (ii) MDA MB-231—basal-like subtype, also called triple-negative breast cancer (TNBC; ER/PR-, HER2-, TP53mut), and (iii) SK-BR-3 (ER/PR-, HER2+, TP53mut). MCF7 cell line, in comparison to the MDA-MB-231 cell line, is a poorly aggressive and non-invasive cell line. Overall, it is being considered to have low metastatic potential. SK-BR-3 cells are the least invasive cells out of the three studied, according to previous findings [22]. The MCF7 (HTB-22) and MDA-MB-231 (HTB-26) cells were maintained in RPMI-1640 (Biowest, Nuaillé, France), while SK-BR-3 (HTB-30) cells were cultured in McCoy’s 5A (Biowest, Nuaillé, France) media supplemented with $10\%$ fetal bovine serum (FBS) (Biowest, Nuaillé, France) at 37 °C in an atmosphere of $5\%$ CO2 and saturated humidity. All cell lines were obtained from the American Type Culture Collection (ATCC). ## 2.2. Studied Drugs Both studied drugs belong to the DNA-damaging and proapoptotic agents, and both are ABC family substrates (ABCB1 and ABCC3, respectively) [23]. Doxorubicin is commonly used in breast cancer treatment disruption of topoisomerase-II-mediated DNA repair and generation of free radicals and their damage to cellular membranes, DNA and proteins [24]. The mechanism of resistance to doxorubicin results from a reduction in the ability of the drug to accumulate in the nucleus, decreased DNA damage and suppression of the downstream events that transduce the DNA damage signal to apoptosis [25]. In turn, cisplatin effectively blocks breast cancer metastasis and inhibits cancer growth together with paclitaxel in neoadjuvant chemotherapy [26]. Resistance of cancer cells to this drug is associated with decreased drug import, increased drug export, increased drug inactivation by detoxification enzymes, increased DNA damage repair and inactivated cell death signaling [27]. ## 2.3. MTT Cell Survival Assay Cell survival was determined using MTT assay by assessing the sensitivity of cells subjected to drugs, i.e., doxorubicin or cisplatin, as previously described [28]. Drugs selection was based on common use in breast cancer [29]. Briefly, the cells were seeded at a density of 5 × 103 cells per well in 96-well culture plates, incubated overnight to allow for cell attachment, and the next day either DOX was added at a concentration range of 0, 0.05, 0.1, 0.5, 1, 2 or 5 μM or cis-Pt was administered at a concentration range of 0, 1, 2, 5, 10, 20 or 50 μM (DOX was dissolved in water, and cis-Pt in saline). Cells were treated for 24 h and incubated with 10 μL of MTT reagent (5 mg/mL) (Sigma-Aldrich, St. Louis, MO, USA). The cells were incubated at 37 °C for 4 h, followed by the addition of 100 μL of solubilization buffer ($10\%$ SDS in 0.01 M HCl). The absorbance was measured in each well with the Microplate Reader Multiskan FC (Thermo Scientific, Waltham, MA, USA) at two wavelengths of 570 and 690 nm. Each experimental point was determined in biological triplicate (each in 6 technical repeats). IC50 (half-maximal ($50\%$) inhibitory concentration) values were calculated using CompuSyn (ComboSyn, Inc., Paramus, NJ, USA) (Table 2), and the standard deviation was calculated using Excel software (Microsoft, Syracuse, NY, USA). ## 2.4. miRNA Isolation Cells were subjected to treatment with DOX (0.1 μM) or cis-Pt (10 μM) for 24 h. Concentration selection resulted from the survival curve obtained in MTT assay and these specific concentrations were chosen as subcytotoxic but known from previous experiments to provoke apoptosis in a longer incubation time (verified by clonogenic assay, data not shown). miRNAs were isolated using a miRNA Isolation Kit Cell (BioVendor, Czech Rep.) based on optimized silica membrane column according to manufacturers’ instructions. Briefly, dedicated cell lysis buffer (250 μL per sample; $1\%$ β-mercaptoethanol freshly added) was added to cell pellets (1 × 106 cells) followed by vortexing, a brief spin and incubation at 25 °C (room temperature) for 3 min. Next, RCL1 and RCL2 buffers were sequentially added that was accompanied by brief vortexing, spinning and short incubation at 25 °C (room temperature). After centrifugation at 11,000× g for 3 min, the clear supernatant was transferred to a new 1.5 mL micro-centrifuge tube, 330 μL of isopropanol was added and after short pulse-vortexing for 10 s, all content was transferred to the MR13 Column, followed by incubation at 25 °C for 2 min. After another centrifugation at 11,000× g for 1 min, 500 μL of buffer CRW1 was added to wash the column, followed by another centrifugation at 11,000× g for 1 min. After washing the column with 500 μL of Buffer CRW2, the miRNAs were eluted with 30 μL of RNase-Free water (centrifugation at 11,000× g for 1 min), assessed using spectrophotometer (Eppendorf Biophotometer Plus Spectrophotometer, Hamburg, Germany) and stored at −80 °C for further study. ## 2.5. qPCR Assessment of miRNAs Two-Tailed qPCR (BioVendor, Brno, Czech Republic) was performed to assess individual miRNAs according to manufacturers’ instructions. The test is based on primers which consist of two hemiprobes, connected by a folded tether. Complementarity of two hemiprobes provides specific binding and specific cDNA synthesis. The cDNA was obtained using the miR-TT-PRI kit containing set of miRNA-specific primers and the Two-Tailed cDNA Synthesis System using a thermocycler (Eppendorf EP Gradient S Thermocycler, Hamburg, Germany) according to the following protocol: 25 °C for 5 min, 50 °C for 15 min, 85 °C for 5 min followed by cooling at 4 °C. For each sample, 600 ng of total RNA was taken for reverse transcription—altogether, it was a combination of three biological repeats (200 ng of each) in one sample. Next, specific PCR primers (PCR Primer F and PCR Primer R) from the miR-TT-PRI kit were added. The qPCR was performed as follows: 95 °C for 30 s, 95 °C for 5 s, 60 °C for 15 s, 72 °C for 10 s and signal detection. After 40 cycles, melting curve analysis was performed and final, relative expression was evaluated based on Cq (quantification cycle) and thermocycler software (Roche LightCycler 480-II PCR, Basel, Switzerland) as previously described [30]. Quantitative qPCR was done with individual reactions for each miRNA target. Validation was performed against U6 (RNU6-1) expression as previously described [31]. Melting temperature analysis was used (SYBRGreen-based) for verification of specific products detection. ## 2.6. Statistical Analysis Results were expressed as mean ± SD. All statistical analyses were carried out using GraphPad Prism 5 (GraphPad Software, Sandiego, CA, USA). Differences were assessed for statistical significance using repeated-measures ANOVA, followed by the post-hoc Dunnett’s test method. All experiments were performed in triplicates unless specified otherwise. The threshold for significance was defined as $p \leq 0.05$ and are indicated by the (, #, •) symbol for $p \leq 0.05.$ qPCR was performed with pooled samples, as commonly acknowledged [32], giving mean target miRNAs levels. ## 3.1. Doxorubicin and Cisplatin Cytotoxicity Evaluation All three cell lines were subjected to different concentrations of studied drugs to find the range of low toxicity concentrations that could be applied to cells during the evaluation of the association between drug treatment and cell response measured by alterations in selected miRNAs levels. The 24 h time interval selection was based on our previous experience and numerous cytotoxicity assays, as well as more mechanistic assessment of apoptotic pathways, induced by the studied drugs. We observed that longer incubation time (48 and 72 h MTT test were performed, showing more than $40\%$ viability decrease in all three cell lines at the lowest concentrations of both studied drugs; data not included) would provoke cytotoxic effect that might not reflect mechanistic aspect of the specificity of the cancer cells response to the treatment. Additionally, according to our previous studies, many genes are regulated within 24 h after DOX or cis-Pt treatment. Similarly, numerous reports indicate 24 h time interval as a sufficient and optimal time to observe significant alterations in miRNA levels after cancer cells treatment (e.g., [33,34,35,36]). For this reason, we were interested in a rapid response that was supposed to be specific. Thus, we decided to verify our hypothesis concerning monitory potential of miRNAs in breast cancer cells in a 24 h time interval that was supposed to provide an early response analysis. An MTT survival test was involved for DOX (Figure 1A) and cisplatin (Figure 1B) cytotoxicity assessment. Cell treatment was 24 h and, as demonstrated, cytotoxicity effects were concentration dependent. MCF7 and MDA-MB-231 showed a similar survival rate when treated with DOX at 0.05, 0.1, 0.5 or 1 μM (up to ca $20\%$ decrease in survival, relative to control, untreated cells). Simultaneously, treatment of SK-BR-3 cells with the same concentrations of DOX showed significant decrease in survival only at the concentration of 0.5 and 1 μM (up to $20\%$ decrease in survival, relative to control cells). In turn, higher concentration (2 μM) revealed higher resistance of MDA-MB-231 than two other cell lines (survival at 80 vs. $65\%$ in MCF7 and $60\%$ in SK-BR-3), while incubation with 5 μM DOX led to similar survival rate in all three cell lines i.e., $60\%$ (Figure 1A). The same cell lines were treated with cisplatin at the range of concentrations 1, 2, 5, 10, 20 or 50 μM. When cells were subjected to 1, 2, 5 or 10 μM cisplatin, the survival rate of MCF7 and MDA-MB-231 was reduced by circa $10\%$. At the same time viability of SK-BR-3 was unchanged. Increasing the concentration of cis-Pt (20 or 50 μM) provoked decrease of all three breast cancer cell lines survival, with a more dominant effect observed in SK-BR-3 (50 vs. $20\%$ decrease at 50 μM) (Figure 1B). Based on the assessment of toxicity of the drugs, IC50 values were calculated (Table 1). Primarily, time-course experiments were performed, but since after longer time intervals (48 or 72 h; data not shown), the compounds appeared highly toxic, we decided to subject cells to 24 h treatment only. Low-cytotoxicity concentrations were selected based on the survival curves (these concentrations, however, are known from previous experiments and literature data to induce apoptosis). ## 3.2. Target miRNAs Selection First, we used the targetscan algorithm for selection of target miRNAs that were supposed to be evaluated [37]. However, as it gave us enormous, not entirely coherent data, and bearing in mind that miRNAs do not have to be fully complementary to interact with target mRNAs, we performed a selection based on literature data. Additionally, TCGA analysis (GEPIA2 [38], Xena Browser [39] and oncolnc.org (access date: 27 February 2023) [40]) was also performed and discussed below. Consequently, eleven most commonly breast cancer-associated miRNAs were subjected to identification after doxorubicin (DOX) or cisplatin (cis-Pt) treatment of breast cancer cell lines. Additionally, assessment of a synthetic nonmammalian miR-54-3p was performed as a negative control. The target miRNAs (with short justification) were as follows: miRNA-21 (-3p and -5p). miR-21-5p was identified as a typical onco-miRNA. When upregulated, it could promote tumor growth, metastasis and invasion and reduce sensitivity to chemotherapy. It modulates the expression of multiple cancer-related target genes and is dysregulated in various tumors [9]. Decreased expression of miR-21 is known to suppress the invasion and proliferation of MCF7 cells [10]. miRNA-106a-5p. In human breast cancer, miR-106a expression was found to be significantly upregulated. It enhanced breast cancer cell proliferation, colony-forming capacity, migration and invasion. Additionally, miR-106a overexpression significantly decreased breast cancer cell apoptosis and sensitivity to cisplatin [11]. Upregulation of miRNA-106a modified DNA damage response and led to the suppression of the ATM gene and formation of its protein product at nuclear foci [12]. miRNA-126 (-3p and -5p). Studies suggest that miR-126-3p acts as either a tumor suppressor or an oncogene in different types of cancer. Upregulation of miR-126-5p can inhibit the migration of the MCF7 breast cancer cell line [13]. Furthermore, overexpression of miR-126-3p significantly reduced tumor size [14]. miRNA-126-3p overexpression inhibited the proliferation, migration, invasion and angiogenesis of triple-negative breast cancer cells (MDA-MB-231 and HCC1937) [15]. miRNA-155 (-3p and -5p). miR-155 was shown to play a crucial role in various physiological and pathological processes, including inflammation and cancer. It was found overexpressed in several solid tumors, including breast, colon, cervical and lung cancers [1] and it is supposed to mediate the pathway controlled by caspase-3 [16]. The mechanism of action of this miRNA is based on downregulation of the cell adhesion molecule 1 (CADM1) that functions as a tumor suppressor [17]. miRNA-199b (-3p and -5p). miR-199b-5p was reported to play a critical role in various types of malignancy. There are studies suggesting that miR-199b-5p downregulation is correlated with aggressive clinical characteristics of breast cancer [18,19]. Overexpression of miR-199b-5p inhibited the formation of capillary-like tubular structures and reduced breast tumor growth and angiogenesis in vivo [20]. Downregulation of miR-199b-5p is correlated with poor prognosis for breast cancer patients. miRNA-335 (-3p and -5p). The expression of miR-335 depends on the type of cancer. It is downregulated in breast cancers and upregulated in gallbladder carcinoma, endometrial and gastric cancers. Overexpression of miR-335 increases the sensitivity of triple-negative breast cancer cells to paclitaxel, cisplatin and doxorubicin, and improves the effectiveness of chemotherapy. It is also associated with cisplatin sensitivity in ovarian cancer [21]. To summarize the contribution of selected miRNAs to breast cancer metabolism, the justification was collected in Table 2. ## 3.3. Quantitative Assessment of the Basal Levels of Selected miRNAs All three cell lines were subjected to quantitative assessment of the basal levels of selected miRNAs using qPCR and relative quantification. All biological experiments were performed in triplicates followed by RT-PCR and qPCR assessment. Consequently, all target miRNAs levels were shown as relative to respective targets in MCF7 cells, used as a calibrator (value “1” assigned to basal level of each miRNA in MCF7). Additionally, the data were divided into three groups i.e., relatively high miRNA levels, low levels and other, relative to results observed in MCF7 cells (selected as reference cell line; Figure 2A,B and Figure 3C, respectively). As demonstrated, average basal levels of individual miRNAs were different in different cell lines with no specific pattern. All the studied miRNAs (21-3p, 21-5p, 106a-5p, 126-3p, 126-5p, 155-3p, 155-5p, 199b-3p, 199b-5p, 335-3p, 335-5p) were detected in MCF7 and MDA-MB-231, while SK-BR-3 did not show expression of the miR-155-3p (Figure 2A). Reaction designed for detection of a synthetic nonmammalian cel-miR-54-3p was used as a negative control. ## 3.4. Cancer Cells Response to Drugs—Evaluation of the Potential Association between Drug Treatment and miRNA Levels All three cell lines were subjected to selected miRNAs assessment after DOX (0.1 μM) or cis-Pt (10 μM) 24 h treatment. Some alterations of studied miRNAs were observed with no significant pattern but with some consistency between the two applied DNA-damaging drugs within particular cell lines. We decided to focus on the miRNAs that showed at least a $40\%$ change relative to control samples. ## 3.4.1. miRNA Alterations in MCF7 Cells The qPCR showed that DOX treatment of MCF7 cells provoked downregulation of 126-3p, 155-3p, 199b-3p and 335-5p miRNAs (Figure 3). The same cells treated with cisplatin (10 μM) revealed induction of miRNAs 155-3p and 335-5p. At the same time, 126-3p and 199b-3p miRNAs were downregulated by cis-Pt (Figure 3A). ## 3.4.2. miRNA Alterations in MDA-MB-231 Cells When MDA-MB-231 cells were treated with DOX, an induction of 21-3p, 155-3p and 199b-5p was observed. At the same time, treatment of these cells with DOX provoked downregulation of 126-3p and 199b-3p (Figure 3B). Incubation of MDA-MB-231 cells with cisplatin caused increased accumulation of 106a-5p and 155-3p miRNAs, while 21-5p, 126-3p, 126-5p and 199-3p were downregulated (Figure 3B). ## 3.4.3. miRNA Alterations in SK-BR-3 Evaluation of miRNAs alterations in SK-BR-3 cells subjected to DOX treatment showed that cells treated with the drug triggered accumulation of 106a-5p, 155-5p and 199b-3p. At the same time 21-3p, 21-5p, 155-3p, 335-3p and 335-5p were downregulated (Figure 3C). When cells were incubated with cisplatin, upregulation of 106a-5p was observed, while 21-3p, 21-5p, 155-3p, 199-5p, 335-3p and 335-5p were downregulated (Figure 3C). ## 4. Discussion Epigenetics seems to play a pivotal role in the metabolism of all human cells, including cancer cells. One of the mechanisms involved in regulation of gene expression without changing its sequence is provided by miRNA. Although the whole family of miRNAs can show tissue- and time-specific patterns, we believe that we can not only detect but also modulate these small polymers. First, we need to identify the miRNAs that represent a certain metabolic status, e.g., cancer. Such research has been already conducted, but depending on different study groups (different cancer stage or grade) and different methods involved (ELISA, qPCR, detection in serum, exosomes or cancer cells), it may give varying results. Some studies show alterations in seven miRNAs (miR-10b, miR-21, miR-125b, miR-145, miR-155 miR-191 and miR-382) in serum of breast cancer patients compared to healthy controls [41], while other studies reveal more than 50 different miRNAs altered in breast cancer patients [42]. Gene expression control may become a way for cancer cells to overcome therapeutic strategies, as well as an efficient way to eliminate cancer cells or make them more sensitive to therapeutic agents [43]. Thus, we performed a study that aimed to evaluate the alterations in breast cancer cell lines exposed to anticancer drugs i.e., doxorubicin or platin. First, we performed the assessment of basal levels of eleven miRNAs that are most commonly evoked when breast cancer is studied [1,2,9,10,11,12,14,16,18,19,21,44,45,46,47,48,49,50,51,52,53,54,55,56,57]. The data were demonstrated as relative miRNA levels compared to MCF7 cells, indicated as a reference cell line. Since the basal levels were extremely different (data range from 0.1 to 23 arbitrary units) and putting all the data on one graph might be misleading, we divided the results into three groups presented in three independent graphs i.e., relative miRNA levels high, low and other, relative to results observed in MCF7 cells (selected as reference cell line; Figure 2A, 2B and 2C, respectively). Noteworthy, the molecular characteristics of the three studied cell lines significantly differ, which may justify the different basal levels of the assessed miRNAs. However, it is truly difficult to tell if miRNA levels are affected by (or affect) respective features of selected cell lines. Additionally, we should not forget that all three cell lines are derived from three different people and, as commonly known, represent not only different genotypes but also heterogeneous population of cancer cells. However, one of the critical differences between studies cells is the ER/PR/HER2 receptors status. Importantly, these receptors mediate cell proliferation, growth, metabolism and other signaling pathways that, since they are related to the mechanisms affected by the studied miRNAs (Table 2), seem to justify alterations in their basal levels. Similarly, studied cells are characterized by different p53 statuses (i.e., MCF7/wt, MDA-MB-231 and SK-BR-3/mut), which is one of the key players not only in cell proliferation control and apoptosis but also in response to exposition to DNA-damaging compounds. Even with that knowledge, it is difficult to find a pattern regarding basal levels of selected miRNAs in studied cell lines. After evaluation of the cytotoxic activity of doxorubicin and cisplatin, we performed an analysis of the alterations of target miRNAs in cells subjected to selected low-cytotoxicity concentrations of studied chemotherapeutics in MCF7, MDA-MB-231 and SK-BR-3. Although relative changes were observed in the accumulation of most of the analyzed miRNAs after drug administration, we focused on the changes that showed at least $40\%$ change relative to control samples. Thus, essential alterations were observed in a couple of miRNAs that showed a trend in all cell lines subjected to two DNA-damaging agents. The miRNA that was considerably altered in two of the three cell lines (MDA-MB-231 and SK-BR-3) was miR-21. It was already suggested that identification of this miRNA in serum of breast cancer patients can be used for breast cancer diagnosis at an early stage of the disease. Although it was not associated with the status of ER, PR and clinical stages [58], it was reported that miR-21 could be related to the development of Multi Drug Resistance (MDR) in breast cancer [59]. Specifically, miR-21 was shown to contribute to breast cancer proliferation and metastasis by targeting LZTFL1 [60]. As reported in colorectal cancer, an increase in miR-21 expression correlated with resistance to fluorouracil therapy due to lowered expression of the repair protein MSH2 [61]. Thus, it is possible that treatment of cells with ABC substrates may provoke alterations in one of the MDR drivers, i.e., miR-21. It is known that miR-106 is significantly upregulated in human breast cancer, as it can enhance cell proliferation, colony-forming capacity, migration and invasion. Additionally, miR-106a overexpression significantly decreased BC cell apoptosis and sensitivity to cisplatin [62]. It was also shown that upregulation of miRNA-106a modified DNA damage response and led to the suppression of the ATM gene and formation of its protein product at nuclear foci [63]. Thus, it may be concluded that induction of this miRNA in cells treated with DOX or cis-Pt could indicate a response of the cancer cells to the DNA-damaging agent. Consequently, it might enhance the resistance of breast cancer cells to cis-Pt; although we do not know the exact mechanism, we might try to target this miRNA to attenuate the resistance effect. Importantly, mir-106a is known to be involved in DNA damage repair systems and cause sensitization of cancer cells to irradiation by targeting the 3′-UTR of ATM kinase. It was found upregulated in breast cancer cells subjected to DNA damage induction [64]. Importantly, this pathway is associated with checkpoint protein 2 (Chk2), mediating the effects of ATM on DNA damage repair mechanisms and other cellular responses that consequently halt the cell cycle (phosphorylates p53) [12]. We also showed that this miRNA was upregulated in MDA-MB-231 and SK-BR-3 cells as well as in all cell lines after cis-Pt. Lack of significant alteration of miR-106a in MCF7 cells after DOX treatment might be associated with the wild type of p53 in those cells, which has a much shorter half-life than the mutated form [65]. However, such correlation verification would require a certain signaling feedback assessment. Another altered miRNA was miRNA-155. It was not expressed in SK-BR-3 cells, while it was significantly reduced in MCF7 and induced in MDA-MB-231 after DOX treatment, while induction was observed in both cell lines after cis-Pt treatment. In turn, the mir155-5p was downregulated in MCF7 cells after either DOX or cis-Pt treatment. Administration of DOX provoked induction of this miRNA in the other two cell lines, while cis-Pt treatment showed similar effect in MDA-MB-231 and SK-BR-3 cells. Specifically, miR-155 was found to be overexpressed in breast cancers [66]. It is also known to suppress apoptosis in MDA-MB-453 breast cancer cells by blocking caspase-3 activity [67]. It can also promote loss of genomic integrity in cancer cells by targeting genes involved in microsatellite instability and DNA repair, which strengthens the oncogenic features of this miRNA. It was also shown to decrease chemosensitivity to cisplatin in colon cancer cells and caspase-3 activity induced by cisplatin [45]. Additionally, it was found to be upregulated in the doxorubicin-resistant human lung cancer A549/DOX cell line [46]. As demonstrated previously, miR-155-5p accelerated DNA damage repair, which led to resistance to radiation of esophageal carcinoma cells [68]. However, a contrary observation was made in breast cancer, in which it was revealed that miR-155-5p decreased the efficiency of homologous recombination repair and enhanced sensitivity to radiation by targeting RAD51 directly [69]. This may be due to the different interactions of miRNA-mRNAs in different types of cancer that is also a known fact [70]. Thus, it seems that downregulation of this miRNA, which accompanied drug treatment, might be a good prognostic factor that could show high efficacy for the therapy in breast cancer. miR-155 is one of just a few miRNAs studied in the context of response to DNA-damaging drugs in breast cancer [71]. Another miRNA that was significantly altered in all three cell lines was miR-199. Specifically, miR-199b-5p was downregulated in DOX- as well as cis-Pt-treated MCF7 cells. It was upregulated in DOX-treated MDA-MB-231 cells and cis-Pt-treated SK-BR-3 cells. In turn, 199b-3p was downregulated after cis-Pt treatment in MCF7 and MDA-MB-231 cells, while in SK-BR-3, it was upregulated after DOX treatment. This particular miRNA was shown modulated in ovarian and prostate cancers, osteosarcoma and hepatocellular carcinoma but also in breast cancer. There are studies suggesting that miR-199b-5p is involved in the Notch signaling pathway in osteosarcoma and its downregulation is correlated with aggressive clinical characteristics of breast cancer [18,19]. In fact, the relative level of this miRNA was lower in the most invasive cell line studied in our work, i.e., MDA-MB-231 cells. According to the literature, downregulation of miR-199b-5p is correlated with poor prognosis for breast cancer patients [72]. Thus, modification of this miRNA may show some prognostic value in the context of breast cancer therapy monitoring. However, even if it seems to be a very sensitive marker, it may also be a very unstable marker that requires further profiling in a dynamic environment of cancer cells subjected to drugs. In previous reports, miR-199a-3pwas shown to be induced in response to DNA damage mediated by homologous repair system that suggests involvement of mTOR and c-Met [73]. Similarly, miR-199-5p/3p was shown to target DNA-damage inducible 1 homolog 2, which also implies involvement of this miRNA in response to DNA-damaging agents [74]. This miRNA is known to significantly diminish aggressive progression, including cell oxygen consumption, colony formation and mobility of breast cancer cells [21]. Variable changes of this polymer after DOX or cis-Pt treatment suggest important role of this miRNA in the response of breast cancer cells to therapy. Another miRNA modulated after DOX or cis-Pt treatment was miR-335-3p (in SK-BR-3 cells). It is known to be associated with p53 in a positive feedback loop to drive cell cycle arrest indicating its important role in proliferation control of cancer cells [75]. The expression level of miR-335 in tissues and cells varies with cancer types, and miR-335 has been proposed as a potential biomarker for the prognosis of cancer. Besides, miR-335 may serve as an oncogene or tumor suppressor via regulating different targets or pathways in tumor initiation, development and metastasis. Furthermore, miR-335 also influences tumor microenvironment and drug sensitivity [21]. Importantly, overexpression of miR-335 was shown to increase the sensitivity of triple-negative breast cancer cells to paclitaxel, cisplatin and doxorubicin, and improve the effect of chemotherapy, as demonstrated in breast cancer patients [76]. We could not see any significant alteration in the expression of either miR-335-3p or miR-335-5p in MDA-MB-231 (triple negative) or MCF7 cells. However, it is difficult to state if alteration in the level of this miRNA after cancer cell exposure to a drug (with a decrease most noticeable in SK-BR-3 cells) contributes to the protection or toxicity mechanism. However, it was suggested that this miRNA could be a tumor suppressor and could serve as a potential therapeutic target for breast cancer treatment [77]. However, again, further studies on the mechanism involved in different cancer types and metabolic conditions or therapy regimen must be evaluated. ## 4.1. Clinical Relevance The ultimate goal for scientific studies is providing tools for controlling biological processes to achieve the most wanted outcomes that are length and/or quality of life. This approach requires analysis of the data that may significantly contribute to the metabolism of cell and all human body. These data include genetic code as well as gene expression profiling that is covered by The Cancer Genome Atlas (TCGA) [78]. We wanted to evaluate the clinical relevance of the studied miRNAs in breast cancer data panel. Such assessment was performed using the algorithm available at oncolnc.org [40]. Although numerous studies show potential effect of target miRNAs modulation on cell survival or resistance, a general assessment of the survival relative to low or high selected miRNAs levels did not show any significant contribution of studied miRNAs expression to this parameter (Figure 4). Nine out of eleven of all studied miRNAs were found in the base but, surprisingly, no significant association of any of the miRNAs and patients’ survival was found. It may result from still low data numbers in TCGA (high and low—296 cases each group) that may not be sufficient when facing breast cancer, which is a very heterogeneous disease. Especially since, as previously reported, different roles of selected miRNAs in different breast cancer types (ER/PR/HER2 positive vs. negative) were reported [76], presented in Table 2. Although there is no significant difference in the survival time relative to the studied miRNAs’ expression, some apparent trends can be recognized, but it may require larger group studies and evaluation of other parameters, such as cancer stage or grade, p53 variant, etc., to obtain conclusive results. ## 4.2. Study Limitations There are many studies focused on the assessment of the role of miRNA in cancer development and diagnostics. The potential of miRNA to monitor cancer response to therapy is also raised. However, serious challenges must be faced before more unequivocal conclusions are delivered. First, it must be taken into account that breast cancer (as many other cancer types) reveals high heterogeneity that is driven by many factors, including ethnically diverse backgrounds, age at diagnosis, stage at diagnosis and genetic and non-genetic alterations (including genomic, transcriptomic, proteomic and epigenetic). This diversity of tumor cells’ profiles led to distinguishing different classification levels, i.e., based on histology and expression profiles of the molecular markers; estrogen receptor (ER), progesterone receptor (PR) and the overexpression or gene amplification of human epidermal growth factor receptor 2 (HER2). According to the presence or absence of these critical receptors, specific molecular subtypes were selected: Luminal A (ER and PR-positive, HER2-negative, low Ki67), Luminal B (ER and/or PR positive, HER2-positive or high Ki67), HER2-enriched (ER and PR-negative, HER2-positive) and triple-negative (TNBC) (ER, PR, HER2-negative). They all exhibit distinct clinical outcomes and require different treatment strategies [79]. The seminal studies using gene expression profiling have further subdivided breast cancers into molecular and transcriptomic subtypes of prognostic and predictive importance. Thus, we see some limitations of our work that refers to three cell lines only that did not provide representative or coherent data. Additionally, referring to the subject of the study, i.e., miRNA, we are aware that it is commonly known for its non-specific action and broad target profile. This, in turn, requires more advanced studies involving specific miRNA downregulation or induction to observe their specific role in affecting specific molecular pathways that control selected functional mechanisms in cancer cells. We also encountered some technical issues. As commonly acknowledged, there is no perfect reference gene for miRNA evaluation. Even the U6 reference gene is sometimes questioned as not stable enough. Another issue is drug selection and its concentration and treatment time during the study. All these factors may significantly affect the cancer cell response that is followed by the different responses of cells and, consequently, different alterations in cell metabolism and gene expression. Lastly, some experiments enable observation of early response, while others provide information regarding the prolonged effect. Altogether, it seems that the evaluation of the role of miRNAs in cancer response to therapy is based on the assessment of a very subtle and sensitive to changes parameter. Thus, it may be difficult to obtain conclusive results before reaching a broader context and evaluating samples derived from patients with different cancer types and characteristics. Without a doubt, miRNA downregulation, mRNA and protein profiling and functional studies are required that will show how all these modulations affect cancer cell metabolism and, eventually, the patient’s outcomes. ## 4.3. Potential Mechanism Evaluation of the biological potential of selected miRNAs in breast cancer patients was performed using targetscan.org (access date: 27 February 2023) [37] and literature data (as shown in Table 2). Based on these analyses, all selected miRNAs could trigger significant effect on the expression of genes contributing to the most critical features of cancer cells i.e., proliferation, adhesion, DNA damage/repair pathways, apoptosis, autophagy, etc. ( see Table 2). Identification of potential targets for selected miRNAs showed that miR-21-3p could affect numerous genes but some of them were predominantly associated with cancer homeostasis, such as DNA damage-regulated autophagy modulator 1 (DRAM1), DNA damage-inducible transcript 4-like (DDIT4L) and p53 and DNA damage-regulated 1 (PDRG1). A similar analysis also showed that miR-126-5p is associated with DDIT4L and PDRG1, but also with growth arrest and DNA damage-inducible, gamma (GADD45G). In turn, miR-335-3p levels corresponded with the expression of GADD45A and mediator of DNA-damage checkpoint 1 (MDC1). Literature data were even more abundant but mostly referred to in vitro conditions. Although the role of selected miRNAs in tumor development and response to therapy is well documented, it is still difficult to state if the observed associations are the cause or the result of alterations observed during carcinogenesis or therapeutic agent treatment. For many years, it was thought that the expression of miRNA in cancer cells was primarily reduced. Only a comparison of the miRNA profile of normal and cancer tissues showed significant overexpression of some miRNAs [80]. Depending on the function of miRNAs in the development of tumors, they are classified as suppressor miRNAs (inhibiting the expression of oncogenes or genes that induce apoptosis) and oncogenic miRNAs (activating oncogenesis or inhibiting the expression of suppressor genes) [81]. It should be emphasized that this classification is a significant simplification, because in the case of many miRNAs (e.g., miR-155), the effect of their activity depends on the total activity of regulated genes and tissue type [81]. Although miRNA levels seem very variable, we are still convinced that they can be used as a diagnostic marker and a potential target in modern anticancer therapies. However, due to the enormous number of this short polymers and no need for full complementarity to act, it may be difficult to find conclusive remarks. It seems that different conditions, including time and concentration or a type of therapeutic strategy, significantly affect the observed alteration. Nevertheless, they still seem to be a promising target that reflects not only a disease-associated modulation of the metabolism, but could also reflect the response of cancer cells to therapy that can be monitored. 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--- title: 'The Hepatoprotective Effect of Two Date Palm Fruit Cultivars’ Extracts: Green Optimization of the Extraction Process' authors: - Nashi K. Alqahtani - Hisham A. Mohamed - Mahmoud E. Moawad - Nancy S. Younis - Maged E. Mohamed journal: Foods year: 2023 pmcid: PMC10048429 doi: 10.3390/foods12061229 license: CC BY 4.0 --- # The Hepatoprotective Effect of Two Date Palm Fruit Cultivars’ Extracts: Green Optimization of the Extraction Process ## Abstract Date palm fruit (Phoenix dactylifera: Arecaceae) is rich in essential nutrients and possesses several pharmacological and medicinal activities. The current study aimed to optimize a water bath-assisted extraction method for two cultivars of date palm fruits, Anbara (An) and Reziz (Rz), and investigated the protective effect of the optimized date palm fruit extract against CCl4-induced liver toxicity in relation to oxidative stress, inflammation, apoptosis, and DNA integrity. The optimization process of two date palm fruit cultivars was applied, using response surface methodology through adjusting three “factors”; time, temperature, and rotation, to allow maximum contents of total phenolic (TPC), total flavonoid (TFC), reducing power (FRAP) and scavenging activity (ABTS) of the extract “responses”. Extraction factors’ application significantly enhanced TPC, TFC, FRAP, and ABTS responses by 1.30, 1.23, 3.03, and 2.06-fold, respectively in An and 2.18, 1.71, 1.11, and 2.62-fold, respectively in Rz, in relation to the convectional water extraction. Furthermore, co-administered CCl4 with An or Rz optimized extracts enhanced body weight gain, amended hepatic architecture, and diminished collagen fiber accumulation. Furthermore, An or Rz extracts reduced liver enzymes, hydroxyproline, alpha-fetoprotein (AFP), MDA, inflammatory cytokine (TNF-α, NF-κB) levels, and DNA fragmentation, while increasing deteriorated adiponectin (ADP) and antioxidant enzyme (GSH, GPX, NO, and IFN-γ) levels, relative to CCl4-administered animals. The protective effects of An or Rz-optimized extracts were also evidenced by suppressing hepatic fibrosis and improving liver function and structure via modulating oxidative stress, inflammation, and apoptosis, in CCl4-induced hepatic damage. Hence, the optimized extraction process for the two date palm fruits resulted in extracts which are rich in phenolic and flavonoid contents and with an elevated antioxidant power. The presence of these rich extracts could help to explain their proven hepatoprotective activity against CCl4-induced liver toxicity. ## 1. Introduction Date palm fruit (Phoenix dactylifera: Arecaceae) is cultivated throughout the Middle East and other regions worldwide, including Central and South America, Europe, India, and the USA [1]. Date palm fruits are rich in numerous essential nutrients and are one of the most commonly consumed fruits in the Middle East and North Africa [2]. Several medicinal and pharmacological actions have been described for date palm fruits, including antioxidant [3], anti-inflammatory [4], gastroprotective [5], nephroprotective [6], anticancer [7], and immunostimulant [8] activities. Furthermore, a randomized controlled human study investigated the impact of palm date fruit consumption on microbiota growth and large intestinal health and concluded that fruit consumption might reduce colon cancer risk without inducing changes in the microbiota [9]. Another randomized controlled trial explored the effects of daily low-dose date palm fruit consumption on glycemic control, lipid profile, and quality of life in pre-and type-two diabetic patients. The study suggested that date palm fruits could benefit lipid profiles because of their high polyphenolic content [10]. Regarding its hepatic action, date extract ameliorated hepatic injury induced by cisplatin [11], gentamicin [12], and mercury [13], among others. The consumption of date palm fruits can be as fresh or dried fruits; nevertheless, the potential to process these fruits into commercial products such as extracts, syrups, and concentrates is high and gives an added value to the fruit [14]. To process date palm fruits, the fruit should normally be extracted. Extraction methods vary; however, they can be categorized into two main categories, conventional and unconventional. Conventional extraction methodologies such as maceration, percolation, and reflux suffer the use of a large volume of solvents, long application time, low extractive values, and the utilization of non-eco-friendly solvents [15]. “Green extraction” is a term that describes the design of extraction processes that could overcome part of the disadvantages of conventional extraction methods. Green extraction methods permit the moderation of energy consumption, the use of green solvents, and the assurance of high-quality and safe extracts [16]. Using water as a green solvent has tremendous benefits, including inexpensiveness and being environmentally benign. As a green solvent, water is also non-flammable and nontoxic, providing opportunities for clean processing and pollution prevention. However, water is a polar solvent with impaired solubility toward the lyophilic matrices, a problem that can be overcome by manipulating the physical conditions of the extraction process [17]. Examples of physical conditions that could be changed are the temperature, the contact time, and the use of physical aids such as microwaves, ultrasonic sound waves, or rotation. Water bath-assisted extraction (WAE) is a method of extraction that gathers three physical aids with water as a solvent: temperature, time, and rotation. This method is implemented through shaking incubators, which allow temperature, time, and shaking (rotation) control. This method is commonly used to extract herbal and natural products [18,19], although current research has rarely implemented this method in date palm fruit extraction [20]. Carbon tetrachloride (CCl4) is a potent hepatotoxic compound that is widely applied for the initiation of chemically-induced hepatic damage in animal models involving oxidative stress and inflammation [21,22]. The harmful effect of CCl4 can be ascribed to the conversion of CCl4 to highly toxic free radicals (the trichloromethyl and the trichloromethyl peroxyl radicals), with consequent oxidative stress and inflammation [23]. Furthermore, these toxic radicals stimulate lipid peroxidation in structures rich in phospholipids, such as mitochondria and endoplasmic reticulum [24]. Furthermore, these free radicals prompt many catabolic processes, such as apoptosis, necrosis, and autophagy [25]. Hence, this hepatotoxic animal model was used to study the effect of several naturally occurring antioxidants, such as plants or plant-derived phytochemicals [26,27,28,29]. In the current study, the main aim was to apply water, as a green solvent, in extracting two cultivars of date palm fruits by developing a WAE method. The study investigates various factors affecting the extractions of the phenolic compounds and flavonoids as leading indicators for the antioxidant activity of the extract. Furthermore, the study extended to scrutinizing the protective effect of date palm fruit extract against CCl4-induced liver damage, and the development of fibrosis was studied in relation to the interdependence of oxidative stress, inflammation, apoptosis, and DNA integrity. ## 2.1. Date Palm Fruits Two cultivars of date palm fruits, Anbara (An) and Reziz (Rz), were freshly obtained (1 kg) from original date palm orchards at the Date Palm Research Center of Excellence, King Faisal University, Al-Ahsa, KSA. The fruits were at the complete ripening stage (Tamar) and were randomly selected in a medium weight, without any physical, insect, or fungal infection damage. The fruits were collected according to the scale recorded by Abdulhadi [30] and then authenticated by the plant taxonomist at the Date Palm Research Center of Excellence, KFU, KSA. Each fruit cultivar was put in sterilized paper sample bags and deposited in the refrigerator at 5 °C until experimental use. ## 2.2. Conventional Extraction of the Date Palm Fruit Cultivars via Ethanol Ethanolic water extractions were carried out using the procedure described by Siddeeg et al. [ 31]. The 100 g date palm fruits from both cultivars, An and Rz, were pitted, sliced, and then placed in 600 mL ethanol: water (4:1 (v/v)) or distilled water for 12 h. The extracts were filtered and centrifuged (2500× g for 10 min). The extracts were evaporated under reduced pressure, and residues were kept at 4 °C until use. ## 2.3.1. The Experimental Design Design-Expert statistical software (Stat-Ease Inc., Minneapolis, software version 11.1.2.0, MN 55413 USA) was used for the WAE’s experimental design and statistical analysis, using a randomized central composite design as a type of response surface analysis. The design included the implementation of four “factors”, three of which were continuous: extraction “temperature” (included three levels, 30, 45, and 60 °C); extraction “time” (included three levels, 20, 40, 60 min); extraction “rotation” (included three levels, 50, 100 and 150 rpm) (Table 1). The fourth factor was the category factor, the type of date palm fruit “cultivar” (included two categories, An and Rz). The experimental design model related all the “factors” with four different “responses”: total phenolic content (TPC), total flavonoid content (TFC), ferric reducing antioxidant power (FRAP), and the ability of free radical scavenging using ABTS (ABTS) (Table 1). Statistical analyses of these data were carried out using analysis of variance (ANOVA), and the quadratic model was the best fit, with p-values for all responses of less than 0.0005 and R2 and adjusted R2 values scoring no less than 0.85 and 0.89, respectively. Three-dimensional surface plots, through the response surface methodology (RSM) model, were performed using the same software as above (Design-Expert). Optimization was computed using the desirability methods, with all calculated values of desirability varying from 0.5 to 0.6. The goal criteria in numerical optimization were set to “minimize” in the case of factors (time and temperature) and to “maximize” in the case of responses (TPC, TFC, FRAP, and ABTS). The generated statistical models were all validated by comparing experimental and predicted response values. ## 2.3.2. Preparation of Date Palm Fruits for WAE The flesh (pericarp) of the previously mentioned date palm fruit cultivars was sliced (approximate dimensions 1.0 × 1.0 × 0.3 cm), and then from each date palm cultivar, 100 g was mixed with deionized distilled water (200 mL) in a ratio of 1:2 (w/v). ## 2.3.3. Water Bath-Assisted Extraction (WAE) Method A digitally controlled water bath with the orbital vibrator (Lab Companion Reciprocal Shaking Water Baths, Jeio Tech) was used in the WAE implementation. The sample was deposited in a glass flask (250 mL) and partially dipped into the water bath, with the flasks’ bottoms approximately 2.0 cm above the bathtub. The water bath was adjusted to the required temperature (Factor 2, Table 1), and then the rotation was adjusted to the required value (Factor 1, Table 1). Finally, the time was adjusted as required (Factor 3, Table 1), and the experiment was allowed to run. Then, the flask contents were transferred to falcon tubes (50 mL) and centrifuged at 2500× g for 10 min at 5.0 °C. The supernatant was decanted, filtered into a clean test tube, and then stored at 4 °C. All samples were diluted with distilled water (1:20) before assessing all dependent variables (responses), as mentioned below. ## 2.4.1. Total Phenolic Content (TPC) Determination The Folin–Ciocalteau colorimetric assay [32] was used to determine the TPC in the date palm extracts using gallic acid (product number G7384, Merck, Darmstadt, Germany) as a standard. A total of 30 μL of the diluted sample or gallic acid serial dilution (25, 50, 75, and 100 g/mL in methanol) was added to 150 μL of (1:10) dilution of Folin–Ciocalteau reagent (product number 1.09001, Merck, Darmstadt, Germany) in water. The contents were mixed for 5 min, and then $6\%$ Na2CO3 (120 μL) was added to obtain a blue-colored solution after incubating for 30 min at 40 °C. Absorbance was determined at 765 nm using a microplate reader (Shimadzu, Kyoto, Japan). Triplicate samples were utilized for each determination, and TPC values were calculated as gallic acid equivalents (GAE: mg/100 g fresh sample). ## 2.4.2. Total Flavonoid Content (TFC) Determination An aluminum chloride colorimetric assay [33,34] was applied to determine TFC using quercetin (standard, Q4951, Merck, Darmstadt, Germany) as a standard. A total of 40 μL of the diluted samples or quercetin serial dilution (0.25, 0.5, 0.75, and 1.0 mg/mL) was diluted into 100 μL with distilled water, before sodium nitrate solution ($5\%$, 5 μL) was added to the solution and incubated for 5 min. AlCl3 solution ($10\%$, 10 μL) was added, and the solution was left for 6 min, after which NaOH (1 M, 50 μL) and distilled water (100 μL) were added. The absorbance was determined immediately at 510 nm utilizing a microplate reader (Shimadzu, Kyoto, Japan). Triplicate samples were used for each measure, and TFC values were calculated as quercetin equivalents (QE: mg/100 g fresh sample). ## 2.4.3. Ferric Reducing Antioxidant Power Assay (FRAP) The reduction of the ferric 2,4,6-tripyridyl-s-triazine complex (Fe3+-TPTZ) to its ferrous form (Fe2+-TPTZ) was implemented [35] as an indicator of the reducing power of the date palm extracts using L-ascorbic acid (standard, A92902, Merck, Darmstadt, Germany) as a standard. Acetate buffer (10 mL, 300 mmol/L, pH = 3.6), TPTZ solution (1.0 mL 10 mmol/L, product number T1253, Merck, Darmstadt, Germany), and FeCl3 (1.0 mL 20 mmol/L) were mixed in 40 mmol/L HCl to prepare the working solution. An aliquot of 20 μL of the sample, ethanol (blank), or L-Ascorbic acid was added to the working solution (180 μL) for 15 min at 37 °C. Absorbance was determined at 593 nm using a microplate reader (Shimadzu, Kyoto, Japan). The results were expressed as ascorbic acid equivalents (AE: mmol/100 g fresh sample). ## 2.4.4. Radical-Scavenging Antioxidant Ability by ABTS The scavenging capability of the date palm fruit extracts was determined using ABTS (2,2′-azinobis-3-ethylbenothiazoline-6-sulphonic acid-diammonium salt, ABTS, A9941, Merck, Darmstadt, Germany) radicals as an indicator for the antioxidant power of the extract [36] using L-Ascorbic acid (A92902, Merck, Darmstadt, Germany) as a standard. ABTS solution (10 mL, 7 mM) was mixed with K2S2O8 (10 mL, 2.45 mM) solution and maintained in the dark (12–16 h, room temperature). The ABTS/K2S2O8 solution was adjusted to 0.7 ± 0.02 at 734 nm with ethanol utilizing a spectrophotometer (Thermo Spectronic Genesys 20, T165762, $\frac{4001}{4}$) just before application. The adjusted ABTS/K2S2O8 solution (1.95 mL) was added to the sample or L-Ascorbic acid (0.5 mL) solution, vortexed, and then incubated in the dark (30 °C for 5 min). Absorbance was determined (734 nm) against methanol (blank) using a microplate reader (Shimadzu, Kyoto, Japan). The results were calculated as mmol ascorbic equivalents/100 g fresh sample. The radical scavenging assays of all samples were presented as a percentage inhibition of absorbance (percentage of scavenging ABTS radicals) using the following equation: Inhibition (%) or ABTS scavenging effect (%) = (A0 − A1/A0) × 100[1] where A0 is the control absorbance and A1 is the absorbance of the sample. The control used was $100\%$ distilled water. ## 2.5.1. Animals and Ethical Approval Male Wistar rats (weight: 110 ± 10 g, age: 7–8 weeks old) were obtained from the animal house facility at King Saud University. Animals were kept in the laboratory under constant temperature (24 ± 3 °C), under a dark–light cycle ($\frac{12}{12}$ h) at least one week before and throughout the experimental work, and maintained on a standard pellet and allowed free access to water. The animal procedures followed the ARRIVE guidelines. The Institutional Animal Care and Use Committee approved the experimental protocol of King Faisal University (KFU-REC-2022-DEC-ETHICS400). All the experiments were executed in harmony with the relevant procedures and regulations of the Ethical Conduct for the Use of Animals in Research at King Faisal University. ## 2.5.2. Extraction of Date Palm Fruits for In Vivo Experiments The two cultivars of date palm fruit, An and Rz, were extracted according to the optimized WAE developed above using the optimized values of the “factors”, time, temperature, and rotation to produce the maximum TPC, TFC, FRAP, and ABTS activities (Table 1). ## 2.5.3. Experimental Design Rats were randomly allocated into six groups ($$n = 8$$); the control group, in which normal rats were administered olive oil intraperitoneally twice weekly for 16 weeks; the An control group, in which rats were administered An extract (100 mg/kg; orally, daily) for 16 weeks [37]; the Rz control group, in which animals were given Rz extract (100 mg/kg; orally, daily) for 16 weeks; the carbon tetrachloride group (CCl4), in which animals were injected intraperitoneally with CCl4 (2 mL/kg) twice weekly for 16 weeks to induce liver injury [28]; the CCl4 + An and CCl4 + Rz groups, in which rats were administered either An or Rz extracts (100 mg/kg; orally, daily), respectively, with CCl4 (2 mL/kg, IP) in the same regimen for 16 weeks. CCl4 was dissolved in 1.0 mL of olive oil, as mentioned elsewhere [38]. ## 2.5.4. General Health and Body Weight Changes Signs of the animals’ good health were observed, including the animal consumption of diet (appetite), the animal’s physical activity, the color of urine, stool condition, the condition of fur and skin, and the mortality rate. The initial (at zero time) and final (before animal scarification) body weights were measured, and ∆ change in body weight was calculated using the following formula: Δ Change in body weight = ((final body weight − initial body weight)/initial body weight) × 100 ## 2.5.5. Collection of Blood Samples and Hepatic Tissues At the end of the experiment, rats were euthanized with isoflurane ($3\%$), and the blood samples were collected via cardiac puncture. The obtained blood was centrifuged for 20 min at 4000 rpm/min to attain the serum, which was stored until further biochemical analysis at −20 °C. The livers of the euthanized rats were dissected out on an ice bed and divided into two parts; the first part was used for the antioxidant parameters’ investigation, while the second part was washed in saline and immediately fixed into $10\%$ neutral buffer formalin for the histopathological examination. ## 2.5.6. Liver Toxicity Evaluation Histological examination (hematoxylin and eosin (H&E) and Masson’s trichrome (MT)) and hydroxyproline content determination were performed to evaluate CCl4 hepatic toxicity. ## Histopathological Examinations Following fixation of the liver samples in $10\%$ formalin overnight, samples were washed and dehydrated. Tissue specimens were cleared in xylene for 2–4 h to remove the alcohol and embedded in paraffin wax to form blocks. The paraffin blocks were sectioned at 3.0-micron thickness by slide microtome. The obtained tissue sections were collected on the glass slides and stained by H&E stain to be examined using the light microscope. Another set of paraffin-embedded liver tissue sections (5 to 7 mm) was stained with Masson’s trichrome (MT) to measure hepatic fibrosis. The sections were examined under a light microscope (Leica microscope, Berlin, Germany) and photographed. The blue stain reflects the extent of hepatic fibrosis and is employed to identify collagen fibers [39]. Image analysis methods were performed using Image J software. ## Determination of Hydroxyproline Rat ELISA hydroxyproline kits (MBS017427) were purchased from MyBioSource (San Diego, CA, USA) and performed according to the manufacturer’s instructions. ## 2.5.7. Determination of Liver Function Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) activities, alkaline phosphatase (ALP), gamma glutamyl transferase (γGT), and lactate dehydrogenase (LDH) were assayed spectrophotometrically via commercially available kits (Spectrum, Cairo, Egypt) following the manufacturer’s instructions. ## 2.5.8. Enzyme-Linked Immunosorbent Assay Evaluations Rat ELISA kits of alpha-fetoprotein (AFP, MBS267612), malondialdehyde (MDA, MBS738685), glutathione (GSH, MBS265966), glutathione peroxidase (GPX, MBS744364), nitric oxide (NO, MBS2604161), and adiponectin (ADP, MBS285758) were obtained from MyBioSource (San Diego, CA, USA) and accomplished in accordance with the manufacturer’s instructions. IFN gamma (IFN-γ, ab239425), nuclear factor-kappa B (NF-κB p65, ab176648), and tumor necrosis factor-alpha (TNF-α, ab100785) ELISA kits were procured from Abcam Inc. (Cambridge, UK). ## 2.5.9. Apoptosis Assay via the Detection of DNA Fragmentation DNA fragmentation is a characteristic sign of apoptotic cell death. It can be detected as a DNA laddering pattern using agarose gel electrophoresis [40]. Cells were washed with PBS and then lysed in cold lysis solution (5 mmol/L Tris, pH 7.4, 20 mmol/L EDTA, $0.5\%$ Triton X-100) for 20 min. Cell lysates were centrifuged at 27,000× g for 15 min, and DNA was extracted from the aqueous phase with phenol: chloroform: isoamyl alcohol (25:24:1, v/v/v) containing $0.1\%$ (w/v) hydroxyquinoline. DNA was precipitated with 0.3 mol/L of potassium acetate and two volumes of cold $100\%$ (v/v) ethanol. Agarose gel ($3\%$ w/v) electrophoresis proceeded at 30 mA for 2 h, followed by UV fluorescence to determine the degree of apoptotic DNA fragmentation. A picture of the gel was taken via the gel documentation system (BioRad, Berkeley, CA, USA) [41]. ## 2.6. Statistical Analysis Data normality was verified using SPSS software, applying two tests: Kolmogorov–Smirnov and Shapiro–Wilk. Data were presented as mean ± SD. For multiple comparisons, one-way ANOVA was performed followed by Tukey–Kramer as a post hoc test. The 0.05 level of probability was used as the significance level. All statistical analyses were performed using Graph Pad software (version 8, San Diego, CA, USA). ## 3.1. Ethanol and Water Conventional Extraction Date Palm Fruits Cultivars The two date palm fruit cultivars, An and Rz, were conventionally extracted using hydro-ethanol and distilled water, respectively. Other “factors” of the WAE were not applied, and the “time” factor was increased to 12 h, as traditionally applied in conventional extraction methods; the results are stated in Table 2. The results indicated the low power of water as a solvent of extraction related to hydro-ethanol. Water extracted only $29.6\%$ and $25.1\%$ of the TPC which could be extracted by hydro-ethanol in An and Rz cultivars, respectively. Similarly, water extraction represented $28.4\%$ and $32.6\%$ of the hydro-ethanol-extracted TFC in the four cultivars. The extract’s reducing and antioxidant capacity was diminished by applying water as a solvent. The FRAP assay values (representing the extract-reducing power) were lessened to $35.1\%$ and $47.7\%$ of the hydro-ethanol extract in the An and Rz cultivars. Comparably, the ABTS assay values (representing extract scavenging capability) were reduced to $29.8\%$ and $27.9\%$ of that of the hydro-ethanol extract in the same two cultivars, respectively. ## 3.2. Production of Date Palm Fruit Extract Applying the WAE Approach Using solvents based on water, a green extraction technique was implemented to extract the two varieties of date palm fruits with the combination of temperature, extraction time, and rotation values as “factors”. These “factors” were chosen as they are a major influence on extracting phenolic compounds from date palm fruits, including flavonoids. ## 3.2.1. Effect of WAE the Date Palm Fruit Varieties on TPC Figure 1 illustrates the interactions between the three “factors” (temperature, time, and rotation) with TPC as a “response” for the two cultivars under investigation. For the An variety, applying the minimum in all “factors”, i.e., 20 min, 30 °C, and 50 rpm produced nearly no change in the amount of TPC extracted in relation to conventional water extraction (Table 1). Increasing the contact extraction time only (from 20 to 60 min) and applying the minimum of other “factors”, i.e., 30 °C and 50 rpm resulted in an increase of 1.57-fold from conventional water extraction (Table 1). Increasing both times (from 20 to 60 min) and the temperature (from 30 to 60 °C) produced a 1.89-fold increase, while applying the maximum of the three factors resulted in an elevation of 2.52-fold from conventional water extraction (Table 1). A similar status was observed with the Rz variety. Applying the minimum in all “factors”, i.e., 20 min, 30 °C, and 50 rpm produced a 1.6-fold change in the amount of TPC extracted in relation to conventional water extraction (Table 1). When the contact time was increased (from 20 to 60 min, applying the minimum of other “factors”, i.e., 30 °C and 50 rpm), the extracted TPC increased 2-fold from conventional water extraction (Table 1). Elevating both time and temperature to a maximum (60 min and 60 °C) augmented the TPC extracted by 2.7-fold; however, when all the “factors” were elevated to their maximum values, the extraction of TPC reached a 4-fold increase. The maximum extracted values of TPC applying WAE and using water as a solvent reached $74.9\%$ and $96.4\%$ from the extraction value of hydro-ethanol in the An and Rz varieties, respectively. ## 3.2.2. Effect of WAE the Date Palm Fruit Varieties on TFC The effect of the three factors (temperature, time, and rotation) on TFC extraction in the two date palm fruit cultivars, An and Rz, is discussed in Figure 2. Applying the minimum condition of WAE (20 min, 30 °C, and 50 rpm), the TFC extracted was reduced to nearly 0.83-fold of the conventional water extraction (Table 1) in the An cultivar. Increasing the extraction duration only (from 20 to 60 min) while applying the minimum of other “factors”, i.e., 30 °C and 50 rpm, resulted in an increase of 1.32-fold relative to conventional water extraction (Table 1). Increasing both time (from 20 to 60 min) and the temperature (from 30 to 60 °C) produced a 1.48-fold increase, while applying the maximum of the three factors resulted in an elevation of 2.9-fold relative to conventional water extraction (Table 1). Similarly, in the Rz variety, operating the minimum in all “factors”, i.e., 20 min, 30 °C, and 50 rpm, produced a minor increase in the amount of TFC extracted (1.1-fold) relative to conventional water extraction (Table 1). When the contact time was increased (from 20 to 60 min, applying the minimum of other responses, i.e., 30 °C and 50 rpm), the extracted TFC increased by 1.48-fold relative to conventional water extraction (Table 1). Elevating both time and temperature to a maximum (60 min and 60 °C) augmented the TFC extracted by 2.72-fold; however, when all the “factors” were elevated to their maximum values, the extraction of TFC did not increase (2.68-fold). The maximum extracted values of TFC applying WAE and using water as a solvent reached $85.7\%$ and $92.0\%$ compared with the extraction value of hydro-ethanol in the An and Rz varieties, respectively. ## 3.2.3. Effect of WAE the Date Palm Fruit Varieties on FRAP Figure 3 states the influence of the three factors (temperature, time, and rotation) on FRAP for the two cultivars, An and Rz. For the An variety, utilizing the minimum in all “factors”, i.e., 20 min, 30 °C, and 50 rpm reduced the FRAP of the extract (0.93-fold) relative to conventional water extraction (Table 1). Increasing the duration only (from 20 to 60 min) and applying the minimum of other “factors”, i.e., 30 °C and 50 rpm resulted in an increase of 1.55-fold from conventional water extraction (Table 1). Increasing both the “time” (from 20 to 60 min) and the “temperature” (from 30 to 60 °C) factors produced a 1.66-fold increase while operating the maximum of the three factors resulted in an elevation of 2.25-fold relative to conventional water extraction (Table 1). For the Rz variety, applying the minimum in all “factors”, i.e., 20 min, 30 °C, and 50 rpm produced nearly no change in FRAP relative to conventional water extraction (Table 1). When the contact “time” was increased (from 20 to 60 min, applying the minimum of other “factors”, i.e., 30 °C and 50 rpm), the FRAP increased by 1.2-fold relative to conventional water extraction (Table 1). Increasing both time and temperature to a maximum (60 min and 60 °C) did not improve the FRAP (1.2-fold); however, when all the “factors” were elevated to their maximum values, the FRAP improved by 1.46-fold. The maximum extracted values of FRAP applying WAE and using water as a solvent reached $79.3\%$ and $84.2\%$ compared with the extraction value of hydro-ethanol in the An and Rz varieties, respectively. ## 3.2.4. Effect of WAE the Date Palm Fruit Varieties on Scavenging Activity (ABTS Assay) The effect of the temperature, time, and rotation on the scavenging activity (ABTS assay) for the two cultivars An and Rz is explained in Figure 4. For the An variety, using the minimum in all “factors”, i.e., 20 min, 30 °C, and 50 rpm produced no change in the scavenging activity (ABTS) of the extract related to conventional water extraction (Table 1). Increasing the duration only (from 20 to 60 min) and applying the minimum of other “factors”, i.e., 30 °C and 50 rpm resulted in an increase of 1.26-fold relative to conventional water extraction (Table 1). Elevating both the “time” (from 20 to 60 min) and the “temperature” (from 30 to 60 °C) factors produced a 1.49-fold increase, while operating the maximum of the three factors resulted in an elevation of 3.81-fold relative to conventional water extraction (Table 1). For the Rz variety, operating the minimum in all “factors”, i.e., 20 min, 30 °C, and 50 rpm produced a minor increase in the scavenging activity (ABTS) (1.14-fold) when related to conventional water extraction (Table 1). When the contact “time” was increased (from 20 to 60 min, applying the minimum of other “factors”, i.e., 30 °C and 50 rpm), the scavenging power (ABTS) increased 1.35-fold relative to conventional water extraction (Table 1). Increasing both time and temperature to a maximum (60 min and 60 °C) further improved the scavenging activity (ABTS) (1.75-fold), while—when all the “factors” were elevated to their maximum values—the scavenging power (ABTS) improved to a 4.3-fold increase. The maximum extracted values of scavenging power (ABTS) applying WAE and using water as a solvent reached $113\%$ and $121\%$ compared with the extraction value of hydro-ethanol in the An and Rz varieties, respectively. ## 3.2.5. WAE Optimization RSM models were performed through which the optimization of the WAE process was computed using the desirability method; the results are stated in Table 3. The calculated optimized extraction procedures were adjusted to gain the maximum of each response using the minimum of the factors. For the An cultivar, when the rotation was adjusted to 103 rpm, at a temperature of 30.00 °C, and after 20.44 min (time adjustment), a maximum of 344.322 (GAE: mg/100 g fresh sample) TPC, 22.340 (QE: mg/100 g fresh sample) TFC, 1061.692 (AE: mmol/100 g fresh sample) FRAP activity, and $48.173\%$ ABTS scavenging effect percentage could be reached. These values corresponded to increases of 1.30, 1.23, 3.03, and 2.06-fold in the four responses, respectively, compared with conventional water extraction. In the Reziz cultivar, optimization suggested the use of 108 rpm with 38.70 °C and 36.62 min to generate a TPC of 4.11.295 (GAE: mg/100 g fresh sample), a TFC 28.090 (QE: mg/100 g fresh sample), a FRAP activity of 506.509 (AE: mmol/100 g fresh sample), and an ABTS scavenging effect of $54.388\%$. These values gave a fold-increase of 2.18, 1.71, 1.11, and 2.62 for the four responses compared with conventional water extraction. ## 3.3.1. General Health and Body Weight Changes (%) The normal, An, and Rz groups exhibited good overall health and normal weight gain compared with the CCl4 group. However, while animals were administered, CCl4 showed reduced activity and appetite, yellow urine, poor nutrition, mental fatigue, rough and dull fur, lifeless eyes, and symptoms of chronic diarrhea. Additionally, one rat died in the CCl4 group, while the remaining rats survived until the experiment’s end. As regards body weight, CCl4 animals exhibited significantly reduced body weight gain compared with the control group, whereas animals treated with An or Rz showed restored body weight gain relative to CCl4 animals, as shown in Table 4. ## 3.3.2. The Histological Studies Morphologically, the rat livers in the CCl4 group appeared swollen, rough in texture, stiff, and dark in color, with many small nodules on the surface. However, the livers were ruddier in color and the edges were more regular in the normal, An, and Rz groups, and the groups administered CCl4 + An and CCl4 + Rz relative to those in the CCl4 group. ## Histological Examination Using H&E The control groups, including normal, An, and Rz, showed the normal architecture of hepatic lobules, as illustrated in Figure 5, whereas CCl4 resulted in the impaired structural organization of the hepatic lobules with loss of the characteristic cord-like arrangement, congested hepatic portal veins, and hepatocyte degeneration. Inflammatory leucocytic infiltrations were observed as well. The sinusoidal spaces were widened and contained activated Kupffer cells, the intrahepatic vessels were congested with blood, and intracellular hemorrhage appeared. The Kupffer cells displayed a noticeable activation, and an abnormally enlarged portal vein was observed. A considerable number of hepatocytes degenerated, while the others showed marked cytoplasmic vacuolation with micro and macro-vesicular steatosis and fibrosis. CCl4 + An animals showed improved hepatic architecture, despite the portal vein being congested and the presence of degenerated bile ducts. The hepatocytes, cytoplasm, and nuclei mainly showed normal histology, but blood sinusoids were dilated. CCl4 + Rz animals presented lower leucocytic infiltration and a precise central vein without any considerable congestion. However, Kupffer cells were still activated. ## Histological Studies Using Masson’s Trichrome (MT) Stain The control, An, and Rz groups displayed normal lobular architecture with central veins, uniform cells, neat rows of hepatic cords without degeneration, and the absence of fibrous tissue hyperplasia or pseudo-lobules. In addition, hepatic cell necrosis or collagen fibers were rarely observed (Figure 6). However, hepatic sections obtained from the CCl4 group displayed a markedly high number of inflammatory cell infiltrates, pronounced adipose degeneration of hepatocytes, extensive necrosis of hepatocytes around the lobule, and increased deposition of collagen fibers in hepatic lobules, all of which disturbed the lobular architecture and led to the formation of a pseudo-lobule that separated the lobules. On the other hand, histopathological examination of rat liver sections obtained from the CCl4 administered along with the date palm fruit extracts An and Rz groups revealed ameliorated adipose degeneration and necrosis of hepatocytes, reduced migration and infiltration of inflammatory cells, and declining collagen fiber deposition in hepatic lobules. ## 3.3.3. Effect of An and Rz Extract Administration on Liver Enzymes in CCl4-Induced Hepatotoxicity Liver enzymes were used to assess CCl4-induced hepatotoxicity. Table 5 shows the effect of administering CCl4 with An or Rz on liver enzymes. CCl4 administration caused a significant ($p \leq 0.001$) escalation in the mean values of ALT, AST, ALP, γGT, and LDH, compared with those of the negative control group—indicating that CCl4 has induced hepatic injury—whereas consumption of CCl4 with An or Rz significantly ($p \leq 0.001$) decreased the mean values of the liver enzymes ALT, AST, ALP, GGT, and LDH compared with the CCl4-alone group. ## 3.3.4. Effect of An and Rz on Hydroxyproline Content in CCl4-Induced Hepatotoxicity Hydroxyproline is an indicator of collagen deposition. During CCl4-induced hepatic injury, animals exhibited a significantly higher hydroxyproline content compared with the control, An, and Rz groups. Meanwhile, An and Rz administration and CCl4 resulted in a reduced level of hydroxyproline, which directly reflects a lesser amount of liver fibrosis, as shown in Table 6. ## 3.3.5. Effect of An and Rz on Serum Level of Alpha-Fetoprotein (AFP) in CCl4-Induced Hepatotoxicity Serum AFP level was significantly elevated (p ≤ 0.05) in the CCl4 group compared with the normal, An, and Rz groups. However, supplementation of CCl4 with An or Rz resulted in a substantial decline (p ≤ 0.05) of the serum AFP level compared with that of the untreated CCl4 group, as shown in Table 6. ## 3.3.6. Effect of An and Rz Administration on the Hepatic Content of Adiponectin (ADP) in CCl4-Induced Hepatotoxicity Adiponectin has been demonstrated to have an anti-fibrotic action in the liver by blocking the activation of hepatic stellate cell-mediated adenosine monophosphate-activated protein kinase and peroxisome proliferator-activated receptor-alpha pathways, which in turn diminish the expression of pro-fibrotic genes [42]. Rats administered with CCl4 demonstrated a significant decrease in the hepatic content of ADP compared with control, An, and Rz groups. The hepatic content of ADP in animals administered with CCl4 alongside An or Rz exhibited a significant increase when compared with the CCl4 group (Table 6). foods-12-01229-t006_Table 6Table 6Ameliorative effects of An or Rz administration on hydroxyproline, alpha-fetoprotein (AFP), and adiponectin (ADP) in different experimental groups during CCl4-induced hepatotoxicity. Animal GroupHydroxyproline(nmol/L)α-Fetoprotein (AFP)(ng/mL)Adiponectin (ADP)(ng/mL)Control23.43 ± 0.210.53 ± 0.258.07 ± 0.25An19.25 ± 0.210.54 ± 0.057.50 ± 0.14Rz22.67 ± 0.210.52 ± 0.037.70 ± 0.20CCl475.23 ± 0.15 €3.37 ± 0.42 €3.87 ± 0.15 €CCl4 + An30.73 ± 0.21 ₳1.45 ± 0.05 ₳6.04 ± 0.05 ₳CCl4 + Rz 39.33 ± 0.21 ₳1.53 ± 0.03 ₳5.57 ± 0.25 ₳All data are enumerated as mean ± SE, ($$n = 6$$). ( €) describes a statistically significant correlation with the control, (₳) denotes a statistically significant correlation with the CCl4-induced hepatotoxicity group using one-way ANOVA after Tukey’s post-hoc test ($p \leq 0.05$). ## 3.3.7. Effect of An and Rz on Nitrosative and Oxidative Stress and Lipid Peroxidation in CCl4-Induced Hepatotoxicity CCl4 injection induced a significant increase in MDA level (p ≤ 0.05) and significant declines (p ≤ 0.05) in the activity of GSH, GPX, and NO compared with those values in the control group and the An and Rz administered groups. However, co-administration of CCl4 with An or Rz displayed a significant decline in the MDA level (p ≤ 0.05) and a significant increase in the GSH, GPX, and NO activities compared with those in the untreated CCl4-induced hepatic toxicity, as shown in Table 7. An and Rz extracts increased MDA and NO levels relative to those in control animals. ## 3.3.8. Effect of An and Rz on IFN-γ, TNF-α, and NF-κB in CCl4-Induced Hepatotoxicity We further investigated the effect of date palm fruit extracts on inflammatory indicators such as IFN-γ, TNF-α, and NF-κB. NF-κB is a key upstream factor for various pro-inflammatory mediators. TNF-α and NF-κB were significantly elevated compared with the normal groups, whereas IFN-γ level was reduced in the CCl4-induced hepatotoxicity group. By contrast, TNF-α and NF-κB were diminished, whereas IFN-γ level was significantly increased in animals administered with CCl4 along with An or Rz extracts—indicating that IFN-γ plays a key role mainly in the early phase of liver injury induced by CCl4, as shown in Table 8. ## 3.3.9. The Effect of An and Rz on Apoptosis Using DNA Fragmentation Assays in CCl4-Induced Hepatotoxicity DNA laddering is used as a basic feature for apoptosis. Casp-initiated DNase digests DNA at inter-nucleosomal linkers into distinct fragments of approximately 180 bp. The outcomes of the DNA fragmentation assay revealed a significant increase in the DNA laddering in the CCl4-challenged group. An and Rz were proven to be potent protective agents, as they attenuated the fragmentation percentage in the CCl4 + An and CCl4 + Rz groups compared with the CCl4-challenged group, as illustrated in Figure 7. ## 4.1. Optimization of An and Rz Cultivars of Date Palm Fruit Extraction Using WAE Several extraction techniques are used nowadays to reduce the dependency on organic solvents and replace them with more eco- and environmental-friendly solvents, called “green extraction”. These techniques apply state-of-the-art technology to allow the use of less or no organic solvent and increase the yield of the medicinally or pharmacologically active phytochemicals [43]. Moreover, green extraction methods are applied to reduce the disadvantages associated with conventional extraction methods, such as the requirement to use large volumes of solvent and long extraction durations. Most green extraction methods utilize water as a solvent of extraction due to its many advantages; however, the use of water alone does not allow efficient extraction as water is a polar solvent with specific and low extractive powers, especially for lipophilic compounds [44]. Accordingly, the use of water as a green solvent is usually accompanied by a physical aid such as a microwave, rotatory water path, ultrasonics, etc. Date palm fruits are known for their therapeutic and pharmacological activities due to the presence of several classes of active phytochemicals, such as phenolic compounds and flavonoids. Many of these compounds are slightly soluble in water, particularly those of an aglycone nature (i.e., non-glycosides) [43,44]. Consequently, the extraction techniques should be directed to extract as much as possible of these valuable phytochemicals. In this study, the application of water only as an extraction solvent, even with increasing the time to 12 h, afforded low extractive values for TPC ($29.6\%$ and $23.9\%$) and TFC (28.8 and $32.7\%$) when compared with organic solvent (ethanol) extraction for the palm fruit cultivars An and Rz, respectively. Furthermore, extraction with water, without any physical aids, produced an extract with a low antioxidant power; the FRAP values were $35.1\%$ and $48.6\%$, and the ABTS values were $29.9\%$ and $27.9\%$ of the ethanol extract values for each cultivar, respectively. These extractive water values necessitated using a physical method to improve the extractive values for date palm fruits’ phenolic components, flavonoids, and antioxidant values of the extract. Accordingly, the green use of water as a solvent in this study was assisted using three physical “factors”; time, temperature, and rotation to produce a “water bath-assisted extraction (WAE)” method. Generally, using WAE, combined with the three factors mentioned above, is typical for extracting natural products as these physical assists help to lessen the handling time and energy and to enlarge the natural extracts’ quality, quantity, and safety [45]. However, very few studies have applied pure water to extract date palm fruits with or without any physical aids. In the current study, the analysis of RSM (Figure 1, Figure 2, Figure 3 and Figure 4) and Table 3 reveal that the TPC of both An and Rz cultivar extracts was affected by the implementation of “factors” which gave a 2.52- and 4-fold increase in the TPC in the extract when compared with water-alone extraction, achieving $74.9\%$ and $96.4\%$ from the extraction value of hydro-ethanol in the An and Rz varieties, respectively. Applying the minimum of the three “factors” in An extraction resulted in no change in the amount of TPC extracted. However, increasing the time “factor” to the maximum produced a $57\%$ increase in the TPC. Furthermore, increasing the temperature and time to the maximum resulted in a $20.3\%$ increase in TPC related to the increase in time alone. However, when all three “factors” were applied to the maximum values, another $60.5\%$ increase in the extract’s TPC was observed compared with the “time” and “temperature” individual increases. Similarly, applying the minimum of the three “factors” in Rz extraction resulted in a $60\%$ increase in the TPC extracted. Increasing the time “factor” to the maximum produced a $25\%$ increase in the TPC. Increasing temperature and time to maximum resulted in a $35\%$ increase in TPC compared with the increase in time alone. However, when all three “factors” were applied to the maximum values, another $48.1\%$ increase in the extract’s TPC was observed compared with the “time” and “temperature” individual increases. Comparably, the TFC of both An and Rz cultivar extracts gave a 2.90- and 2.68-fold increase in the TFC in the extract when compared with water-alone extraction achieving $85.7\%$ and $92\%$ of the extraction value of hydro-ethanol in both varieties, respectively. Applying the minimum of the three “factors” in An extraction resulted in a reduced amount of TFC extracted (0.83-fold). Increasing the time “factor” to the maximum produced a $59\%$ increase in the TFC. Increasing temperature and time to the maximum resulted in only a $12.1\%$ increase in TPC in relation to the increase in time alone. However, when all three “factors” were applied to the maximum values, another $96\%$ increase in the extract’s TFC was observed compared with the “time” and “temperature” individual increases. Similarly, applying the minimum of the three “factors” in Rz extraction resulted in nearly no change in the amount of TFC extracted. Increasing the time “factor” to the maximum produced a $34.5\%$ increase in the TFC. Increasing both temperature and time to the maximum resulted in an $83.7\%$ increase in TFC relative to the increase in time alone. However, when all three “factors” were applied to the maximum values, nearly no increase was observed in the extract’s TFC in relation to the “time” and “temperature” individual increases. The increase in the “rotation” factor had a very strong effect on the An cultivar; however, it was of nearly no effect in the Rz cultivar when TPC was the response. Regarding the antioxidant activity of the extract, the FRAP activities of both An and Rz cultivar extracts were affected by the “factors”, which gave a 2.25- and 1.46-fold increase in the FRAP activities in the extract when compared with water-alone extraction, achieving $79.3\%$ and $84.2\%$ of the extraction value of hydro-ethanol in the An and Rz varieties, respectively. Applying the minimum of the three “factors” in An extraction resulted in a slight reduction in the FRAP activity (0.93-fold) compared with conventional water extraction. Increasing the time “factor” to the maximum produced a $64\%$ increase in FRAP activity. Increasing temperature and time to the maximum resulted in nearly no increase in FRAP activity compared with the increase in time alone. However, when all three “factors” were applied to the maximum values, another $35.5\%$ increase in the extract’s TPC was observed compared with the “time” and “temperature” increases alone. For the Rz cultivar, applying the minimum of the three “factors” in Rz extraction resulted in no change in the FRAP activity of the extract. Increasing the time “factor” to the maximum produced a $20\%$ increase in the FRAP activity. Increasing temperature and time to the maximum has resulted in no change in FRAP activity relative to the increase in time alone. However, when all three “factors” were applied to the maximum values, another $23.3\%$ increase in the extract’s FRAP activity was observed compared with the “time” and “temperature” increases individually. Changes in the time factor affected FRAP activity in both An and Rz cultivars; however, the temperature change did not affect this, reducing activity in both cultivars. Changes in rotation moderately affected the FRAP activity in both cultivars’ extracts. The scavenging power (ABTS) of both An and Rz cultivar extracts was influenced by the “factors” which gave a 3.81- and 4.3-fold increase in the FRAP activities in the extract when compared with water-alone extraction, achieving $113\%$ and $121\%$ from the extraction value of hydro-ethanol in both cultivars, respectively. Applying the minimum of the three “factors” in An extraction resulted in no change in the scavenging power (ABTS) compared with conventional water extraction. However, increasing the time “factor” to the maximum produced a $26\%$ increase in scavenging power (ABTS). Increasing both temperature and time to the maximum has resulted in an $18.2\%$ increase in scavenging power (ABTS) compared with the increase in time alone. However, when all three “factors” were applied to the maximum values, another $155\%$ increase in the extract’s TPC was observed relative to the “time” and “temperature” individual increases. For the Rz cultivar, applying the minimum of the three “factors” in Rz extraction resulted in a $14\%$ increase in the extract’s scavenging power (ABTS). Increasing the time “factor” to the maximum produced a $16.6\%$ increase in the scavenging power (ABTS). Increasing temperature and time to the maximum has resulted in a $31.5\%$ increase in scavenging power (ABTS) compared with the increase in time alone. However, when all three “factors” were applied to the maximum values, another $145\%$ increase in the extract’s scavenging power (ABTS) was observed related to the “time” and “temperature” individual increases. Changes in the time factor and temperature factors did not strongly affect scavenging power (ABTS) in either An and Rz cultivar; however, the changes in rotation significantly affected the scavenging power (ABTS) in both cultivars’ extracts. ## 4.2. The Hepatoprotective Activity of An and Rz Cultivar Extract This study explored the hepatoprotective potentials of date palm fruit extracts in rats, specifically An and Rz, in CCl4-mediated hepatic injury. CCl4 is metalized by cytochrome P450 to the highly reactive •CCl3 and •CCl3OO radicals, which bind covalently to macromolecules to initiate a chain of events leading to membrane phospholipids’ peroxidative degradation and the accumulation of lipid-derived oxidation products causing a failure of the naturally occurring anti-oxidant defenses with subsequent hepatic injury [24]. In the current study, CCl4 animals exhibited reduced activity and appetite, poor nutrition, chronic diarrhea, and reduced weight gain. These findings were in agreement with Fang et al. [ 46] and Al-Seeni et al. [ 38], who reported that CCl4 caused a reduction in body weight. In addition, CCl4 resulted in the structural organization impairment of the hepatic lobules, congested hepatic portal veins, hepatocyte degeneration, inflammatory cell infiltrations, and activated Kupffer cells. MT staining revealed that the CCl4 group displayed hepatocyte degeneration and necrosis and increased deposition of collagen fibers in hepatic lobules, which led to the formation of a pseudo lobule that separated the lobules. Furthermore, the CCl4 group presented collagen fiber augmentation in the connective tissue surrounding the portal area, well-formed fibrotic bands, and abundant collagen fibers around the regenerative hepatocytes’ nodules and between the hepatocytes. On the other hand, animals co-administered with CCl4 alongside the date palm fruit extracts An and Rz revealed enhanced body weight gain, amended hepatic architecture, a lower leucocyte infiltration in H&E staining, ameliorated hepatocyte degeneration and necrosis, and diminished collagen fiber accumulation in MT staining. Liver enzymes such as ALT, AST, ALP, γGT, and LDH are hallmarks of CCl4 hepatotoxicity [21,38]. In the current study, CCl4 administration amplified ALT, AST, ALP, γGT, and LDH, indicating severe hepatic cell injury which could be attributed to tissue breakdown, permitting the escape of intracellular enzymes from the cytosol into the blood [29,47]. Administering CCl4 with An or Rz extracts significantly reduced the liver enzymes ALT, AST, ALP, γGT, and LDH compared with the CCl4-alone group. Previous studies have shown that date palm fruit extract can deter liver enzyme amplification in hepatic injuries induced by dimethoate [48], mercury [13], and radiation [49]. Histological examination, fibrosis grading, and hydroxyproline content in the hepatic tissue were performed to evaluate the ability of CCl4 to induce hepatic fibrosis. Total collagen, determined by hydroxyproline quantification as hydroxyproline, is an indicator of the collagen deposition [50]. CCl4-administered animals exhibited a significantly higher hydroxyproline content, which is in harmony with previous studies [51]. Meanwhile, An or Rz co-administration along with CCl4 resulted in a reduced level of hydroxyproline, directly reflecting a lesser amount of liver fibrosis. Bahri et al. [ 52] revealed that date palm fruit treatment decreased the hydroxyproline level and morphological lesions in bleomycin (BLM)-induced lung fibrosis and concluded that date palm fruit has a protective effect against BLM-induced pulmonary fibrosis because of its abundance of phenolic compounds and vitamins. Additionally, the serum alpha-fetoprotein (AFP) level was significantly elevated in the CCl4 group compared to the control groups. Supplementation of An or Rz with CCl4 resulted in a significant decline (p ≤ 0.05) of the serum AFP level compared with that of the untreated CCl4 group. Adiponectin (ADP) is one of the most abundant adipocytokines in circulating hormones. ADP has been demonstrated to have an anti-fibrotic action in the liver through the AdipoR1 and AdipoR2 receptors, both of which are present in hepatic stellate cells (HSCs) [53]. Another theory through which ADP inhibits CCl4-induced liver fibrosis is the modulation of liver iNOS/NO. ADP upregulates inducible nitric oxide synthase (iNOS) in HSCs and increases nitric oxide (NO2-/NO3-) concentration in the HSC-conditioned medium [54]. In the existing study, rats administered with CCl4 demonstrated a significant decrease in ADP, whereas animals administered with CCl4 alongside An or Rz extracts exhibited a significantly increased ADP level signifying the anti-fibrotic action of date palm fruit extract. Nitric oxide (NO), a paracrine-acting soluble gas, possesses a wide range of biological effects, including anti-fibrotic actions [55]. In vivo studies revealed that NO has an inhibitory role in the development of liver fibrosis as it inhibits HSC proliferation and migration, promotes HSC apoptosis, downregulates the stellate cell activation marker αSMA, and suppresses collagen I gene expression [54]. The current study showed that CCl4 caused NO deterioration, whereas An and Rz extracts recovered the NO levels. The outcomes of this study revealed that liver enzyme amplification is coupled with excessive lipid peroxidation and oxidative stress, as demonstrated by augmented MDA and diminished antioxidant enzymes. Lipid peroxidation-induced damage caused an upsurge in the hepatocytes’ cellular permeability with protein leakage, including aminotransferases, into serum, indicating hepatic injury and necrosis [56]. GSH is a vital protein thiol, which coordinates the body’s defense system against oxidative stress. In addition, GSH effectively scavenges free radicals and reactive oxygen species [57]. The present study revealed that CCl4 injection caused a substantial reduction in GSH contents and significant depletion in the activity of phase II metabolizing enzymes such as GPX. On the other hand, co-administration of An or Rz extracts with CCl4 presented a significant decline in MDA level and an escalation in the GSH and GPX. The elevation in GSH content by An and Rz observed in the current study may be due to the suppression of lipid peroxidation and protein oxidation. An earlier study showed that date fruit extract mitigated lipid peroxidation and enhanced anti-oxidant status in the liver of rats sub-chronically exposed to trichloroacetic acid [58]. Date fruits are a good source of natural antioxidants, and thus may be used as a functional food to manage oxidative stress-related disorders [59,60]. Altogether, the results of the current study demonstrated the potent effect of An and Rz extracts in alleviating CCl4-induced hepatic oxidative stress and lipid peroxidation. The inflammatory response has been reported as a critical process in CCl4-induced liver damage [56]. As previously described, CCl4-induced acute liver injury is closely associated with inflammation by elevating pro-inflammatory cytokine secretion [38,61]. Interferon-gamma (IFN-γ) effectively destroys activated HSCs (aHSCs); thus, it is a core cytokine related to the NK cell-dependent anti-fibrotic immune response [62]. In line with previous studies, the present study confirmed that the inflammatory cytokines TNF-α and NF-κB were elevated, whereas IFN-γ was diminished in the CCl4-induced hepatotoxicity group. By contrast, animals administered with CCl4 alongside An or Rz extracts exhibited decreased TNF-α and NF-κB and increased levels of IFN-γ. A previous study assessed the effect of date palm seeds’ dietary supplementation on broilers’ growth and carcass performances and revealed that date palm seeds exhibited escalations in IFN-γ, signifying the immune-stimulant constituents of the seeds [63]. Date palm fruit extract has exhibited anti-inflammatory actions in several hepatic models, such as mercury [13] or cisplatin [11] induced hepatic injury, and in non-hepatic models, such as isoproterenol-induced cardiomyopathy [64] and cisplatin-induced nephrotoxicity [65]. Similar to other macromolecules, nucleic acids are also confronted by free radicals causing oxidative DNA damage. DNA laddering is used as a fundamental feature for apoptosis. Casp-initiated DNase digests DNA at inter-nucleosomal linkers into distinct fragments of approximately 180 bp fragments. The outcomes of the DNA fragmentation assay exposed a significant increase in the DNA laddering in the CCl4-challenged group. Similar outcomes were reported by Alkreathy et al. [ 66] while studying the protective effects of Sonchus arvensis against CCl4-induced genotoxicity and DNA oxidative damage in the liver. On the other hand, co-treatment of An or Rz with CCl4 appreciably reduced the DNA fragmentation, indicating lesser DNA damage. ## 5. Conclusions Two date palm fruit cultivars, An and Rz, were extracted using a newly developed green WAE method. The obtained extracts were optimized, using RSM modelling, toward the low values of “factors”: temperature, time, and rotation, and the high production of “responses”: TPC, TFC, FRAP, and ABST scavenging activity. As a result, conventional water extraction failed to extract phenolic and flavonoid compounds from the fruits’ cultivars relative to ethanol extraction (up to $25.1\%$ and $32.6\%$, respectively). Similarly, conventional water extracts produced minimal antioxidant capabilities, calculated as FRAP and ABTS activities, when compared with ethanol extraction (maximum $47.7\%$ and $29.8\%$). However, applying all optimization “factors”; temperature and time, and rotation, succeeded in improving the “responses” of the extraction process to reach up to 1.30, 1.23, 3.03, and 2.06-fold, respectively, in An and 2.18, 1.71, 1.11, and 2.62-fold, respectively, in Rz in relation to convectional water extraction. Furthermore, the optimized date palm fruit extracts of the two cultivars, An and Rz, suppressed CCl4-induced hepatic injury, as demonstrated by mitigating liver function parameters and improving the liver histological structure. 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--- title: 'Reticulocyte Hemoglobin as a Screening Test for Iron Deficiency Anemia: A New Cut-Off' authors: - Abdullah I. Aedh - Mohamed S. M. Khalil - Alaa S. Abd-Elkader - Mohamed M. El-Khawanky - Hamdan M. Alshehri - Amr Hussein - Ali A. Lafi Alghamdi - Abdulkarim Hasan journal: Hematology Reports year: 2023 pmcid: PMC10048437 doi: 10.3390/hematolrep15010021 license: CC BY 4.0 --- # Reticulocyte Hemoglobin as a Screening Test for Iron Deficiency Anemia: A New Cut-Off ## Abstract Introduction: Latent iron deficiency (LID), in which iron stores in the body are depleted without incidental anemia, poses a key diagnostic challenge. Reticulocyte hemoglobin content (Ret-Hb) is directly correlated with the functionally available iron for heme synthesis in erythroblasts. Consequently, Ret-Hb has been proposed as an efficient iron status marker. Aim: To assess the importance of Ret-Hb in detecting latent iron deficiency as well as its use in screening for iron deficiency anemia. Materials and Methods: A study involving 108 individuals was conducted at Najran University Hospital, 64 of whom had iron deficiency anemia (IDA) and 44 of whom had normal hemoglobin levels. All patients were subjected to complete blood count (CBC), reticulocyte percentage, Ret-Hb, serum iron, total iron binding capacity (TIBC), and serum ferritin measurements. Results: A significant decrease in Ret-Hb level was observed in IDA patients compared to non-anemic individuals, with a cut-off value of 21.2 pg (a value below which indicates IDA). Conclusion: The measurement of Ret-Hb, in addition to CBC parameters and indices, provides an accessible predictive marker for both iron deficiency (ID) and IDA. Lowering the Ret-Hb cut-off could better allow for its use as a screening parameter for IDA. ## 1. Introduction Reticulocyte hemoglobin content (Ret-Hb) correlates directly with the functionally available iron for heme synthesis in erythroblasts. Consequently, Ret-Hb has been proposed as an efficient marker of iron status [1]. Several studies have suggested that a Ret-Hb measurement in peripheral blood samples is useful for the diagnosis of iron deficiency and the development of iron therapy response [2]. Latent iron deficiency (LID) is a diagnostic challenge in which body iron stores are deficient without incidental anemia. LID may go unrecognized for a long time and is suspected to be due to a decrease in serum ferritin levels [3,4]. It is important to identify cases of LID as most of them develop into iron deficiency anemia (IDA) if the iron condition is not corrected. In addition, individuals who suffer from LID usually complain of mysterious symptoms, including intense fatigue, epithelial cell devitalization (e.g., cheilitis), pica, hair loss, restless legs syndrome, thinner central cornea, decreased cognitive performance, behavioral disturbances, and enhanced osteoporosis in women [5,6,7,8]. In young people, there is evidence of an association between iron deficiency and cognitive function impairment. It is well known that cognition is crucial for quality of life (QoL) and encompasses various functions including attention, memory and concentration [4,9]. The exact mechanism by which IDA affects the brain is still not well understood; however, there are some supposed possibilities including abnormalities in neurotransmitter metabolism, alterations in brain energy metabolism and decreased myelin formation [4,10]. Leonard et al. in 2014 used an easy tool (IntegNeuro) to administer the assessment of cognitive function in young women and concluded that some cognitive change scores were significantly higher for ferritin improvers than non-improvers (irrespective of treatment group) and for women who had LID at the baseline and were treated with iron supplements [4]. LID is also known as “nonanemic iron deficiency” or “subclinical iron deficiency” where the transition from normal iron leveling to the state of IDA development entails two sequential processes including depletion, followed by exhaustion, of the iron storage compartment and the consequent depletion of the functional compartment. The development of IDA is a consequence of functional compartment depletion [11]. The mean intracellular hemoglobin content of the erythrocytes (MCH) is considered an inclusive measurement for both the availability of iron over the preceding 90–120 days and for the proper introduction of iron into intracellular hemoglobin [12]. By directly measuring the mean hemoglobin content (MHC) of the red blood cell precursors (reticulocytes), early stages of IDA may be identified at a time when other traditional biochemical parameters appear to be non-informative [13]. The measurement of ret-hemoglobin content is a known direct assessment of the iron incorporation into erythrocyte hemoglobin, so it is a direct estimate of the recent functional availability of such iron in the erythron [14]. A single biomarker is important to use for the diagnosis of IDA, but the use of a serum marker that can be easily identified as a screening marker is necessary [15,16]. Ret-He has already been suggested to be an additional marker for the screening of IDA [15]. Hence, in this study, we review the role of Ret-Hb in the diagnosis of LID and as a screening parameter for IDA. ## 2.1. Patients The study involved 108 randomly selected individuals from the outpatient clinics of internal medicine at Najran University Hospital, Saudi Arabia, after obtaining the approval of the IRB ethics committee of Najran University and the informed consent of the participants. Patients with known hereditary hematological disorders or known hematological malignancies were excluded. ## 2.2. Sample Collection and Biochemical Analyses Blood samples for CBC and Ret-Hb were collected in K3EDTA tubes and analyzed using an automated hematology analyzer (Sysmex XS 500i, Tokyo, Japan; Sysmex, https://www.sysmex.com/ (accessed on 1 October 2022). Serum biochemical analyses (iron and ferritin) were performed using a COBAS C311 (Roche, Basel, Switzerland (https://www.roche.com/ (accessed on 1 October 2022) automated chemical analyzer. Patients with known hematological diseases or who were on a long-term drugs (especially chemotherapy or radiotherapy) were excluded to avoid ferritin variation. Pregnant women were also excluded. ## 2.3. Assessment of Anemia and Iron Deficiency We classified anemia according to the WHO definition for anemia, as follows: Hb < 12.0 g/dL in females and Hb < 13.0 g/dL in males. Iron deficiency was defined as transferrin saturation (TSAT) < $20\%$ and ferritin level in serum < 100 ng/mL, according to Muñoz [17]. A serum ferritin level of <30 ng/mL with normal Hb was considered to indicate insufficient iron store or iron deficiency. The endpoint of this research was the validation of Ret-Hb as a screening marker for LID and IDA in adults, considering its correlation with other parameters. Being overweight or obese can affect iron and ferritin levels as these conditions may leave the patient at risk of developing subclinical inflammation and certain chronic diseases or complications such as obstructive sleep apnea, ischemic heart disease, cor pulmonale, and many others. At the same time, they can increase the likelihood of iron deficiency and iron deficiency anemia. According to previous studies, there is an increasing trend in the prevalence of obese and overweight individuals in Saudi Arabia. We included participants suffering from obesity or being overweight after exclusion of the associated complications. We repeated testing of some samples twice or sent samples to another laboratory for assurance when applicable or in the case of unusual results. Cancer patients were excluded from this study as ferritin testing is a less useful or accurate tool in oncology patients, especially patients with solid tumors due to ferritin elevation in patients with different solid tumors and the association with more progressive diseases and shorter survival when elevated. ## 2.4. Current Cutoffs Diagnosis of IDA and LID with the use of Ret-Hb is dependent on comparing patient results with supposed diagnostic cutoffs. However, Ret-Hb cutoffs currently recommended by several studies demonstrate considerable inconsistency. Between 25–29 pg cutoffs are used for the diagnosis of iron deficiency and around 21 pg are used for iron deficiency anemias. Herein we tested a cutoff in our institution. ## 2.5. Statistical Analysis Data were analyzed using the IBM SPSS 20 software. An independent sample t-test was performed for normally distributed variables, and the results are presented as mean ± standard deviation. Receiver operating characteristic (ROC) curve analysis was performed to identify the optimal Ret-Hb cutoff value for predicting IDA. The statistical significance was set at $p \leq 0.05.$ ## 3. Results No statistically significant difference was observed between anemic and non-anemic groups in terms of RC; otherwise, significant differences were seen in all other items, as shown in Table 1. Patients with IDA showed significantly ($p \leq 0.05$) decreased levels of hemoglobin, MCV, MCH, MCHC, serum iron, and ferritin, along with significant increases in RDW and platelet count, when compared with normal individuals of both sexes. However, these decreased amounts were still above the lower normal limit of Hb (12 g/dL in female and 13 g/dL in male patients) and the limit of MCV (80 for both sexes). There was a highly significant ($$p \leq 0.0001$$) decrease in reticulated hemoglobin level in anemic patients of both sexes, in comparison to non-anemic patients, but the reticulocyte count did not show any significant difference. In individuals with normal hemoglobin, Ret-Hb levels were significantly decreased in males with low ferritin levels and showed a tendency to decrease in females, but the reticulocyte count (RC) did not show any significant difference among groups in both sexes. The low ferritin level group showed a significant increase in UIBC level and significantly lower hemoglobin levels in both sexes. MCV and MCH decreased significantly in normal males with low ferritin levels, and there was a tendency towards decreased levels in females of the same group (Table 2). Male IDA patients showed significant decreases in Ret-Hb, RBCs, Hb, MCHC, and serum iron levels, but showed no significant difference ($p \leq 0.05$) in MCV, MCH, RDW, PLT, UIBC, ferritin, and RC, when compared to normal Hb individuals with low ferritin levels. Female IDA patients showed significant decreases in the levels of Ret-Hb, RBCs, Hb, MCV, MCH, MCHC, serum iron, and ferritin, as well as significant increases in RDW, PLT, and UIBC, but without any significant difference in RC, when compared to the other groups (Table 3). Reticulocyte hemoglobin showed positive correlations with hemoglobin level ($$p \leq 0.0001$$, $r = 0.819$) and serum ferritin levels ($$p \leq 0.0001$$, $r = 0.540$) and tended to correlate with serum iron levels ($$p \leq 0.065$$, $r = 0.232$; Table 4). We observed a Ret-Hb cutoff value of 21.2 pg ($100.0\%$ sensitivity, $64.1\%$ specificity), values below which can predict IDA. We considered $100\%$ sensitivity to apply this parameter as a screening value for IDA. The results of the receiver operating characteristic (ROC) analysis for reticulocyte hemoglobin levels in normal individuals and patients with IDA are shown in Figure 1. Regarding samples that were repeated either in the sample laboratory or a different one, all results were compatible with the first results from our institution. ## 4. Discussion Iron deficiency anemia may be detected relatively late when considering classic laboratory parameters such as Hb, mean corpuscular volume, and mean corpuscular hemoglobin. A single biomarker to detect either IDA or LID is rarely used; however, markers that can be easily used for screening are necessary and are still required for further research worldwide [7,18]. The lifespan of circulating erythrocytes is approximately 120 days. Changes in Hb and MCV values usually occur at a later time point, when the IDA is already fulminant [14]. In CBC, the MCV was measured at under 80 fL, but its normal value is between 80 to 100 fL. This microcytic anemia can be observed in anemia related to chronic disease, thalassemia, sideroblastic anemia, and mainly in chronic iron deficient anemia. Microcytic cells in the setting of iron deficient anemia may appear to have larger areas of central pallor [18]. Anemia can be a consequence of absolute iron deficiency which is due to chronic blood loss; however, in many patients of chronic diseases, enhanced formation of pro-inflammatory cytokines particularly interleukin 1 (IL-1), interferon gamma (IFN-γ) or tumor necrosis factor alpha (TNF-α) leads to the development of functional IDA and anemia of chronic disease. These pro-inflammatory types of cytokines suppress the production of renal erythropoietin, but also directly inhibit bone marrow erythropoiesis [19,20]. The key diagnostic parameters routinely used for β- and α-thalassemia are ferritin and hemoglobin analysis (HbA2 and Hb abnormality) in addition to DNA analysis; however, several simple screening indices are highly recommended in endemic areas to differentiate between IDA and thalassemia traits, and recently these were encouraged along with other potentially better performing indices [21]. The sideroblastic anemias are a group of acquired and inherited bone marrow disorders defined as a pathological iron accumulation in erythroid precursor mitochondria. The abnormal, iron-laden mitochondria are seen encircling erythroblast nuclei, giving rise to characteristic morphological features of the sideroblastic anemias. The ring sideroblast was originally recognized in the 1940s and codified as a class of anemias in the 1960s. Similarly to most hematological diseases, understanding the molecular genetic basis of anemias is important for better understanding of their pathogenesis and to face the diagnostic and therapeutic challenges presented by them [22]. Reticulocytes are formed in the bone marrow and then develop into mature erythrocytes two days later, at which point they are seen in the peripheral blood [13]. Therefore, using blood samples to determine Hb content in reticulocytes is helpful to analyze and assess iron levels via reticulocytes [23]. The assessment of hemoglobin content in reticulocytes can accurately reflect iron levels [24]. Our findings demonstrated that the levels of Ret-Hb in IDA were decreased significantly ($$p \leq 0.000$$), in comparison with healthy individuals of both sexes, thus establishing that low Ret-Hb is a good indicator of iron deficiency anemia, which is consistent with many previous studies [25,26,27]. The importance of incorporating Ret-Hb into the diagnostic or screening panel of iron deficiency is due to the bioavailability of iron in the synthesis of hemoglobin in newly formed red blood cells (reticulocytes), because Ret-Hb levels are not as influenced by other conditions (e.g., inflammation) as those of acute phase reactants such as ferritin and transferrin [28], or the fluctuations caused by the diurnal variation and/or the quality of food intake, unlike serum iron [29,30]. In addition, the Ret-Hb under B anemias is similar [31,32], whereas Ret-Hb deficiency and high or normal levels in thalassemia have been observed, as the latter coincides with an increase in iron levels as a result of multiple blood transfusions or enhanced iron absorption secondary to ineffective erythropoiesis and hepcidin suppression [33,34]. Our results indicated significant decreases in Ret-Hb ($$p \leq 0.018$$), MCV, and MCH in males with low ferritin but normal hemoglobin concentrations, whereas females presented a non-significant decrease in mean Ret-Hb level, when compared to normal individuals. Therefore, we consider Ret-Hb to be a good indicator of decreased iron levels in males in general, especially in the presence of hypochromia and microcytosis. In contrast, when measuring Ret-Hb in women, we did not observe a significant difference between women with low and normal ferritin levels. The most common cause of LID is menstrual blood loss among women of reproductive age (mean age of 30.2 ± 10 years) [35]. Women of reproductive age have two different periods per month: (a) the menstrual period, in which blood loss occurs causing iron store depletion. During non-menstrual days (b), the female compensates for the deficiency of iron by increasing the reticulocyte hemoglobin as a reaction to blood loss and not only to an absolute iron deficiency, despite the joint presence of the two conditions. This can be demonstrated by the relatively high percentage of reticulocytes with normal hemoglobin content in females of reproductive age with low levels of ferritin. Our study emphasized the importance of reticulocyte hemoglobin content (Ret-Hb) as a predictive marker of iron deficiency anemia, and as an early indicator of iron deficiency without the incidence of anemia, thus emphasizing the importance of using Ret-Hb measurement as a screening test for iron deficiency. We observed a Ret-Hb cutoff value of 21.2 pg ($100.0\%$ sensitivity, $64.1\%$ specificity), values below which can predict IDA. We considered $100\%$ sensitivity to apply this parameter as a screening value for IDA. Another study has stated that the cut-off value of Ret-Hb is 29.3 pg ($90.6\%$ sensitivity, $66.7\%$ specificity) in female patients with IDA [36]. This difference in the cut-off value might be due to the difference in the degree of sensitivity, as we used $100\%$ sensitivity to apply the Ret-Hb measurement as a screening test, as opposed to $90.6\%$ in the previous study. Uçar et al. used a Ret-Hb cut-off value of <$90\%$ with a sensitivity of $49.1\%$ [37]. Toki et al. used a Ret-Hb cut-off value of 28.5 pg and had a specificity of >$90\%$ and $68\%$ sensitivity. With a higher cut-off (30.9 pg), the sensitivity increased to $92\%$, whereas the specificity dropped back to $81\%$ for the diagnosis of iron deficiency [38]. For LID, Tiwari et al. assessed the diagnostic usefulness of Ret-Hb in blood donors with respect to sTfR and revealed that with a cut-off value < 28 pg, a higher sensitivity ($92\%$) and specificity ($97\%$) were reported [39]. Ferritin can play a role when C-reactive protein and IL-6 (a critical cytokine)act as pro-inflammatory cytokines by mediating fever and the acute inflammatory response to lower the fever and critical cytokine levels, which in turn reduces the hyperinflammatory response [40,41]. In most laboratories currently, the serum ferritin level is used to indicate the body’s iron store as it is a non-invasive method that provides reliable results, near to the invasive gold-standard method for body iron store; however, the difference in cut-off of ferritin levels to define depleted iron stores can also be seen [24,41]. The stores of ferritin in the human body are predominantly found in the macrophages of the reticuloendothelial system and in hepatocytes. Macrophages phagocytose damaged or aged erythrocytes by recycling the iron contained in heme using heme oxygenase-1 to release the iron; this recycling accounts for about $90\%$ of the body’s daily needs from iron, with only around $10\%$ being met by intestinal absorption [42]. Iron is released from those storage sites as ferritin (II) via ferroportin in the cell membrane, then reoxidation of ferritin (II) to ferritin (III) by the ferroxidase enzyme ceruloplasmin occurs, followed by the loading of ferritin (III) onto transferrin for its systemic distribution to other sites [43]. Transferrin saturation is a marker for testing the amount of iron available for the process of erythropoiesis or other cellular requirements [44]. Serum ferritin level testing is widely used for the detection of IDA and LID; however, certain physical and demographic characteristics alter the iron homeostasis process and affect serum ferritin levels. These conditions include old age, obesity, and specific inflammatory conditions such as congestive heart failure. Patients who are overweight or obese have increased levels of hepcidin, likely due to adiposity-related inflammation, resulting in restricted dietary iron absorption and reduced transferrin saturation levels [45,46]. Serum ferritin levels are higher in some chronic inflammatory diseases even in non-obese individuals or in cases of low-grade inflammation [44,47]. Inadequate iron for erythropoiesis, as mainly determined by bone marrow aspiration and is frequently found in elderly people, even those with serum ferritin levels up to 75 μg/L [48,49]. Striking increases in serum ferritin levels can also occur in infectious events and in cases of acute inflammatory process [44]. Identifying accurate parameters to detect LID is always of interest and non-invasive tools are preferred, which raises the importance of hemoglobin tests such as Ret-Hb. New hematology analyzers can measure the hemoglobin content within the reticulocytes through the principles of fluorescence flow cytometry combined with reportable/diagnostic parameters to provide valuable diagnostic information. Such information is evaluated internally in the laboratory to check the results and to complete the diagnosis findings [24,48]. We excluded pregnant women from this study, as it is supposed that describing the iron status of pregnant women is often challenging in similar research due to the limited nature of available data and the different cutoffs, diagnostic criteria and pathophysiology of anemia. During pregnancy, a marked physiologic increase in demand for absorbed iron is present to expand the red blood cell mass of the woman and to secure the required iron supply for the placental function needed to grow the fetus and to complete a normal pregnancy without developing iron deficiency or IDA or taking iron supplements, the woman should have iron stores in her body at conception of ≥500 mg, which corresponds to serum ferritin concentrations of 70–80 μg/L [49,50,51]. The limitations of the present study include the low number of studied patients (as the study was performed in a single institutional center) and the exclusion of pregnant women and patients with some chronic diseases. In addition, we did not evaluate other factors which might be affected in the context of IDA or LID, such as soluble transferrin receptor, transferrin, and sTfR/lF index. As such, these and similar factors should be taken into consideration in future research. ## 5. 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--- title: Production and Analytical Aspects of Natural Pigments to Enhance Alternative Meat Product Color authors: - Allah Bakhsh - Changjun Cho - Kei Anne Baritugo - Bosung Kim - Qamar Ullah - Attaur Rahman - Sungkwon Park journal: Foods year: 2023 pmcid: PMC10048459 doi: 10.3390/foods12061281 license: CC BY 4.0 --- # Production and Analytical Aspects of Natural Pigments to Enhance Alternative Meat Product Color ## Abstract Color is a major feature that strongly influences the consumer’s perception, selection, and acceptance of various foods. An improved understanding regarding bio-safety protocols, health welfare, and the nutritional importance of food colorants has shifted the attention of the scientific community toward natural pigments to replace their toxic synthetic counterparts. However, owing to safety and toxicity concerns, incorporating natural colorants directly from viable sources into plant-based meat (PBM) has many limitations. Nonetheless, over time, safe and cheap extraction techniques have been developed to extract the purified form of coloring agents from raw materials to be incorporated into PBM products. Subsequently, extracted anthocyanin has displayed compounds like Delphinidin-3-mono glucoside (D3G) at 3.1 min and Petunidin-3-mono glucoside (P3G) at 5.1 277, 515, and 546 nm at chromatographic lambda. Fe-pheophytin was successfully generated from chlorophyll through the ion exchange method. Likewise, the optical density (OD) of synthesized leghemoglobin (LegH) indicated that pBHA bacteria grow more rigorously containing ampicillin with a dilution factor of 10 after 1 h of inoculation. The potential LegH sequence was identified at 2500 bp through gel electrophoresis. The color coordinates and absorbance level of natural pigments showed significant differences ($p \leq 0.05$) with the control. The development of coloring agents originating from natural sources for PBM can be considered advantageous compared to animal myoglobin in terms of health and functionality. Therefore, the purpose of this study was to produce natural coloring agents for PBM by extracting and developing chlorophyll from spinach, extracting anthocyanins from black beans, and inserting recombinant plasmids into microorganisms to produce LegH. ## 1. Introduction PBM are food products generated from plant-based ingredients and designed to resemble conventional meat in terms of taste, texture and apperance, without the integration of animal components [1,2]. The goal of PBM is to provide a meat-like experience for those who want to reduce their consumption of animal products for ethical, health, or environmental reasons [3]. Some popular examples of PBM include burgers, sausages, and chicken nuggets produced from ingredients such as soy, pea protein, and mushrooms [4,5]. Nevertheless, the widening gap between the existing supply of meat and its imminent demand has increased the need to produce PBM analogs [6,7]. Therefore, demand for PBM products to replace conventional meat is increasing due to issues linked with extensive agriculture including livestock production, animal welfare, and potential climate change have made it unsustainable to increase meat production to meet future demand. Moreover, previous reports suggest that replacing animal protein with plant-based protein in the daily diet reduces the risk of cardiovascular complications, stroke, and type 2 diabetes [8,9,10,11]. Hence, developing better plant-based diets would address the current protein crisis and positively impact the planet and human health [10,12]. Despite ongoing technical developments, the appearance, flavor, taste, and texture of PBM differ from those of traditional meat products. The most critical feature of PBM anlalogs to be mimicked and resemble traditional meat is visible appearance, particularly color attributes [6,11,12]. Consequently, the integration of natural pigments into PBM products has been developed due to deleterious agents associated with their synthetic counterparts [13,14]. Natural pigments cannot be used directly from renewable sources, and incorporating raw materials as coloring agents in PBM has many limitations [15]. These limitations include the unstable nature of natural pigments during high-pressure and temperature processing, low bioavailability, chemical degradation due to oxidation, and deprivation of quality characteristics during storage, and preservation [16,17,18]. However, the addition of natural pigments has remarkable health benefits and strongly promotes the technological and sensory profile of PBM products [6,13,19]. Anthocyanins constitute the most important group of water-soluble natural pigments. Anthocyanins are reddish-purple color and flavonoids that exist in various plants, fruits, flowers, stems, and leaves [20,21]. The daily intake of anthocyanins is estimated to be 180–215 mg per day per person [22,23]. Nonetheless, the Joint Food and Agriculture Organization (FAO)/World Health Organization (WHO) Expert Committee on Food Additives (JECFA) established that anthocyanin-containing extracts had very low toxicity [24]. The fundamental structure of anthocyanins is a flavan nucleus attached to two aromatic rings, including benzopyrylium and phenolic rings, connected by glucose at carbon atom NO. 3 of benzopyrylium [13]. The structural variation of this ring structure comes from various patterns of substitution in the B ring with OH and OCH3 groups, different sugar substituents at the 3 and 5 positions, and the potential acylation of sugar substituents with cinnamic and aliphatic acids. Furthermore, anthocyanin-based compounds are susceptible to heat and light, and their degradation causes the conversion of anthocyanins into polymeric pigments. Additionally, the structural changes in anthocyanins are closely linked to pH variations, which ultimately cause color changes [25,26]. Additionally, anthocyanins as natural pigments possess remarkable anti-inflammatory, potent antioxidant, and anticarcinogenic properties [22,27,28]. Similarly, chlorophyll is a pigment abundant in green vegetables and green plants, and plays an important role in photosynthesis [21]. Moreover, chlorophyll- and chlorophyll-related compounds have protective and anticancer effects [29]. Chlorophyll has an Mg2+ ion in the middle of the porphyrin ring structure that plays a role in the coloring and energy-absorption processes [30]. Zn-chlorophyll, in which Mg ions are substituted with Zn ions to stabilize color, has also been developed [31]. Moreover, depending on high temperature and oxygen presence, chlorophyll is converted into several derivatives, such as pheophytins, chlorophyllides, and phephorbides. The formation of pheophytin and other derivatives of chlorophyll is irreversible; however, pheophytin is treated with Cu and Zn ions to form a more attractive and stable residue [13]. Likewise, myoglobin, a meat pigment, also has a ring structure with a Fe ion in the middle of the ring structure. The ring structures of chlorophyll and myoglobin are similar, except for the differing central ions. To mimic the structure and chemical properties of myoglobin, the current study focused on replacing Mg with Fe using an ion exchange process [29]. LegH is a pigment present in the roots of legumes that is structurally similar to myoglobin [3,6]. The gene encoding soy LegH was inserted into the genome of the yeast Pichia pastoris, enabling the production of high levels of soy LegH. The corresponding evidence suggested that the whole protein fraction of this LegH preparation (Prep) comprises at least $65\%$ LegH, and the remaining residual proteins are from the Pichia host. P. pastoris is a nontoxigenic and nonpathogenic methylotrophic yeast that has been used in the recombinant expression of both Generally Recognized as Safe (GRAS) and US Food and Drug Administration (FDA)-approved proteins [4,32]. The existing literature regarding the production, extraction and analytical aspects of natural pigments developed for PBM is very limited; however, some corresponding studies have been reported [13,22,29,32,33,34]. Therefore, researchers are taking a profound interest in utilizing natural pigments as replacements for their synthetic counterparts, particularly by developing sustainable extraction techniques for the efficient retrieval of bioactive compounds for use in PBM products and nutraceuticals. To the best of our knowledge, this is the first report describing the extraction and analytical aspects of natural pigments to be incorporated into PBM products. ## 2.1.1. Chemicals The chemicals used in this study were anthocyanins, ethanol, extraction solvent (Bioextrax AB, Lund, Sweden), distilled water, Milli-Q water, chlorophyll, acetic acid, petroleum ether (PE), diethyl ether, silica gel, ferric chloride (FeCl2), sodium hydroxide (NaOH), sodium acetate, propanol, agarose, Tris-acetate-EDTA (TAE), methanol, acetonitrile, formic acid, and sodium dodecyl sulfate-polyacrylamide (SDS-PA). ## 2.1.2. Biological Agents The biological agents used in this study were black beans, spinach, leghemoglobin (LegH), Luria-Bertani (LB) broth, yeast extract peptone dextrose (YPD) medium, plasmid BHA, plasmid JAN, ampicillin-resistant sequence, and sequences of pBHA, LegH, and pBHA + LegH. ## 2.1.3. Instruments The instruments used in this study were a biosafety level 2 cabinet (BSC), CO2 incubator, culture dishes, flasks and plates, colony counter, Buchner funnel with No. 1 Whatman filter paper, rotary evaporator, column chromatography assembly, agar plates, spectrophotometer, Milli-Q water purification apparatus, electrophoresis equipment, colorimeter, high-performance liquid chromatography (HPLC), and a 0.45 μm filter. ## 2.2. Chemical Structure and Extraction of Anthocyanins Figure 1 shows the chemical structure of anthocyanins. Black beans were purchased from Morning Crops Co. (Gimpo, Republic of Korea), and anthocyanins were extracted from 500 g of black beans using an extraction solvent (Bioextrax AB, Lund, Sweden). The anthocyanins were extracted at 25 °C with acetic acid which was dispensed to a hydroalcoholic solution ($60\%$ ethanol) used for extraction to reach a $0.1\%$ acetic acid concentration in the solvent extraction mixture. The resulting mixture was stirred at 250 rpm for 4 h, followed by sedimentation for an additional 1 h. Using vacuum filtration, the supernatant was recovered using Whatman paper No. 1. To eliminate the ethanol, the resultant extract was concentrated using a rotary evaporator by setting the water bath temperature to 50 °C. Subsequently, the concentrated extract was lyophilized, and the resulting lyophilized powder was stored at −80 °C [35]. ## 2.3. Chemical Structure and Extraction of Chlorophyll The chemical structure of chlorophyll is presented in Figure 2. Spinach (Spinacia oleracea) was purchased from Gomgom (Seoul, Republic of Korea). Under controlled conditions, a sample of 500 g was dried at 50–60 °C for 24 h to eliminate moisture. The dehydrated sample, weighing 37.86 g, was extracted with ethanol at a mass: solvent ratio of 1:10 w/v at 60 °C for 30 min. The crude chlorophyll filtrate was separated using a Buchner funnel with No. 1 Whatman filter paper. A rotary evaporator was used to concentrate the extract. Following concentration, petroleum ether (PE) was added to sonicate and dissolve the extracts. Thereafter, the extracts were stored at −20 °C with no influence of light [36]. ## Synthesis of Fe-Pheophytin by Ion Exchange Chromatography The process of synthesis of FePhe derivatives has been adopted from Nelson and Ferruzzi [29], and the detection of various FePhe derivatives samples extracted from spinach was separated by ion exchange chromatography through an established procedure of Moustafa and Morsi [37]. Consequently, through the ion exchange method, the extracted chlorophyll sample was added with 1 N NaOH to adjust the pH to 8.5, followed by heating, stirring, and dissolving in distilled water. The sample dissolved in distilled water was adjusted to pH 3 using 3-acetic acids and heated at 60–80 °C for 30 min. A solution of 1.3 M FeCl2 and 0.25 M sodium acetate was prepared and dissolved in acetic acid. The prepared acetic acid solution was added five times to the sample, mixed with the heated sample, and further heated at 60–80 °C for 30 min. The pH of the sample was titrated to 8.5 by adding NaOH and distilled water, and then heating and stirring the extract. The sample extracted in distilled water was freeze-dried to form a powder and stored at −80 °C. Subsequently, a column was prepared by mixing silica gel with PE. After filling with silica gel, spinach extract and sand were added to protect the sample layer. PE was added until color separation into green (chlorophyll), yellow (pheophytin), and orange (Fe-pheophytin); when this separation was confirmed, the yellow and orange sample layers were extracted with the addition of PE alongside $10\%$ diethyl ether. The green sample layer was extracted using PE with $5\%$ 2-propanol until the sample was not dissolved in the extraction solvent. After extraction, the sample was stored at −20 °C and protected from light (Figure 3). ## 2.4.1. Plasmid The sequence for producing LegH was custom-made by Bioneer Co. (Daejeon, Republic of Korea). This sequence was inserted into the plasmid BHA (pBHA) at a size of 1987 bp [38]. In the case of pBHA cloning, only pBHA recombinant microorganisms can be obtained from an ampicillin-containing medium, as this plasmid confers an antibiotic and ampicillin-resistant sequence. The sequence that produces LegH is 528 bp in size (Bioneer Co.; Daejeon, Republic of Korea) (Table 1). ## 2.4.2. Microbial Culture The extraction technology for LegH Prep was adopted from Impossible™ Foods (Redwood City, CA, USA, Private limited) P. pastoris production strain, MXY0291 (GRAS notification for soy leghemoglobin protein preparation derived from Pichia pastoris) (patent approved, 2017) [39]. This LegH Prep does not contain the production organism or antibiotic resistance genes. Impossible™ Foods has conducted mass spectrometry to identify the P. pastoris protein that is present in LegH Prep at >$1\%$ of the total protein fraction. The sequence of each protein was analyzed to ensure that the P. pastoris proteins present in the LegH Prep did not contain significantly similar homology to known allergens [40]. The production of LegH from legumes using microorganisms was adopted from a previously established procedure described by Jin et al. [ 41]. The microbial culture was produced using LB broth (Sigma-Aldrich, St. Louis, MO, USA) and LB agar plates (Sigma-Aldrich, St. Louis, MO, USA) as the bacterial culture medium. Ampicillin (Roche Holding AG, Basel, Switzerland) was procured from Sigma-Aldrich (St. Louis, MO, USA). The Escherichia coli (DH5a) with or without pBHA (with LegH) were incubated with LB broth in an incubator at 37 C for 24 h. The cultured bacteria were diluted from 101 to 109 and the optical density (OD) was measured at 600 nm every hour for 24 h using a Multiskan SkyHigh Microplate Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). E. coli were then separated into media with and without ampicillin, added to an LB plate, and spread to check for growth. Subsequently, pJAN, with an ampicillin resistance sequence, was inserted into P. pastoris. Similarly, P. pastoris was cultured in yeast extract peptone dextrose (YPD) medium containing 0.1 g/L of ampicillin. The culture process was conducted for up to 48 h at 30 C. Through this methodological process, the generated LegH sequence extracted from pBHA was inserted into pJAN to grow P. pastoris (Figure 4). Moreover, pBHA contains a promoter that drives gene expression, a selection marker for identifying cells that have taken up the plasmid, and a multiple cloning site for inserting the gene of interest. The pBHA vector plasmid is named after its components, which include the promoter from the human beta-actin gene (B), the hygromycin selection marker (H), and the polyadenylation signal from the bovine growth hormone gene (A). Similarly, pJAN had a size of 4329 base pairs (bp). The pJAN is a custom-designed or proprietary plasmid developed by a specific research group. ## 2.5. Electrophoresis The procedure was conducted by mixing $1\%$ agarose gel with 1× TAE buffer (Bioneer, Daejeon, Republic of Korea), and 1× TAE buffer solution. The 50× TAE buffer (Bioneer, Republic of Korea) was diluted 1× with water produced using a Milli-Q water purification system (Millipore, Billerica, MA, USA). A 1 kb ladder from GeneDireX (Taiwan, China) was used. Electrophoresis was conducted at 100 V for 60 min (Mupid-One; Takara, Japan). The sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) (Bio-Rad Laboratories, Hercules, CA, USA) method was adopted, as described by Bakhsh, Lee, Bakry, Rathnayake, Son, Kim, Hwang and Joo [19]. ## 2.6. Color Measurement The chromaticity and color coordinates of anthocyanin, Fe-chlorophyll, and myoglobin samples were tested after adjusting the absorbance to 0.7 at 535 nm. Each pigment sample (30 mL) was poured into a 50 mL conical tube (Φ 3 cm). Color indices were measured using a colorimeter (Color Flex EZ colorimeter, Hunter Associates Laboratory Inc., Reston, VA, USA). Before the measurement, a white plate ($X = 80.59$, $Y = 85.72$, $Z = 91.97$, Illuminant D65) was calibrated, and the sample was measured for brightness (L), redness (a), and yellowness (b) using the Hunter scale [42,43,44]. ## 2.7. Absorbance Measurement The absorbance of the anthocyanin, Fe-chlorophyll, LegH, and myoglobin samples was set to a concentration of $0.7\%$ at 535 nm absorbance. Absorbance was measured in the 400–700 nm range using a Multiskan SkyHigh Microplate Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) [45]. ## 2.8. High-Performance Liquid Chromatography (HPLC) Analysis Based on the determination of the standard, the experiment was performed using HPLC under the same conditions. Methanol, water, and acetonitrile (Sigma-Aldrich, Steinheim, Germany) were used as the HPLC-grade products. Samples were prepared by filtration using a 0.45 μm filter (GVS, Bologna, Italy). Agilent 1100 HPLC (Agilent, Santa Clara, CA, USA) and an Agilent C18 column were used for HPLC. For the mobile phase, solvent A was water: ACN: formic acid (45:45:10, vol/vol), and solvent B was $10\%$ formic acid water. The flow rate used was 1 mL/min, the analysis time was 60 min, and the injection volume was 20 μL. The following wavelengths were detected: 277, 515, and 546 nm [46]. ## 2.9. Statistical Analysis Shapiro–Wilk and Levene’s tests were performed to measure the normality and homoscedasticity assumptions. One-way analysis of variance (ANOVA) was used to analyze the data using R Studio (Ver. 4.0.2, Software & Tech Services, Boston, MA, USA). Duncan’s multiple range tests were performed to assess the differences between means. Data are expressed as mean ± standard deviation. Significant differences among the groups were determined using a significance level of $p \leq 0.05.$ All experiments were conducted in triplicate. ## High-Performance Liquid Chromatography Recently, a sustainable methodology based on green extraction technologies has been adopted for the extraction of bioactive compounds from plant sources. Based on the current green extraction technologies, anthocyanin is a plant-specialized water-soluble compound that confers red, violet, and blue colors to various food products [47]. Anthocyanin samples were analyzed by HPLC at 277 and 546 nm using a c18 column. The standard peaks of Del-delphinidin-3-mono glucoside (D3G) and Petunidin-3-mono glucoside (P3G) were observed at 3.1 and 5.1 min, respectively (S1 and S2). This phenomenon has been documented previously that characterized anthocyanin profiles of red table grape cultivars [48]. Previously, Lianza et al. [ 49] determined that the efficiency of anthocyanin extraction from untreated black beans was better than that of anthocyanin extraction experiments performed after peeling and drying black beans. The possible reason for this observation is that anthocyanin is water soluble; therefore, rehydration may lead to pigment deprivation during the extraction process. Moreover, it has been proven that anthocyanin extraction is positively affected by the incorporation of hydrochloric acid, rather than acetic acid. Additionally, Teixeira et al. [ 50] have extracted anthocyanin from broken black bean hulls using optimum conditions with a 30:70 (v/v) ratio of ethanol and citric acid solution 0.1 mol L−1, at a flow rate of 4 mL min−1 at 60 °C. This study further indicated that Delphinidin-3-glucoside was the primary anthocyanin that has been linked to numerous health benefits, such as antidiabetic and antiradical activities. Furthermore, Pomar, Novo and Masa [48] estimated a typical HPLC chromatogram of anthocyanin at 546 nm. The authors calculated the chemical structure of anthocyanins by hydrolysis and chromatographic analysis. Various peaks were numbered which correspond to mono glucosides of five anthocyanins found in grapes. In addition, the amount of anthocyanin extracted varied according to the concentration of acid added to the extraction solvent. When hydrochloric, acetic, formic, phosphoric, and citric acids were compared, hydrochloric acid showed the highest yield. The concentration-dependent extraction (0.1, 0.2, and $0.3\%$) revealed that $0.3\%$ concentrations of these acids resulted in a higher yield of anthocyanin; however, higher or lower concentrations than $0.3\%$ resulted in lower yields [51]. Similarly, based on the current observations, Pappas, Athanasiadis, Palaiogiannis, Poulianiti, Bozinou, Lalas and Makris [34] conducted a proportional evaluation of diverse novel technologies for the extraction of total anthocyanins from freeze-dried saffron tepals using aqueous solutions of citric acid and the lactic acid at various concentrations. Contrastingly, these results showed that no specific configuration was observed for the type of acid or concentration. The authors further clarified that the best performance for anthocyanin extraction and antioxidant capacity was obtained using a stirred-tank extraction with $1\%$ (w/v) lactic acid solution; this yielded 3.25 g of cyanidin-3-O-glucoside equivalents (CGE)/kg in the dry weight of tepals. For anthocyanins, quantitative analysis has been widely conducted in terms of the total anthocyanin content. In recent years, however, an increasing number of studies report individual anthocyanin concentrations, and influencing factors have also been investigated. The major contribution to the extraction of anthocyanins from natural sources for food health applications is described in detail by previous authors [16,52,53]. However, because the aim of extracting anthocyanin as a pigment is for the manufacture of PBM as a food product, it is necessary to determine the residual amount of acid that is harmful to the human body and conduct a corresponding safety test. Furthermore, additional experiments on the anti-inflammatory and antioxidant effects of anthocyanins are required. Nonetheless, regarding the toxicity of anthocyanins, previous studies have indicated that this compound is widely used as a natural colorant in food and beverages in Europe, Japan, and the United States [22]. Additionally, JECFA concluded that anthocyanin-containing extracts had a very low or negligible level of toxicity upon consumption [24,54]. ## 3.2. Chlorophyll and Fe-Pheophytin Figure 3 describes the conversion of chlorophyll into its derivatives pheophytin and Fe-pheophytin. Heme protein is considered the preferred protein for consumption [29]. However, the consumption of heme protein has several limitations, including cultural, socioeconomic, and environmental issues [55]. To counter these societal complications, an alternative strategy has been adopted by replacing the heme protein with a structurally similar tetrapyrrole. Chlorophyll molecules can be modified by replacing centrally chelated Mg with other metals, such as Fe, to generate different metalloporphyrins [56,57]. Furthermore, lipophilic and hydrophilic derivatives have been produced and applied in the food and pharmaceutical industries; these derivatives include Zn- and Cu-pheophytins, pyropheophytins, and Cu- and Fe-chlorophyllin as food colorants and nutritional supplements [58]. Inconsistent with the current study FURUYA et al. [ 59] synthesized and purified the Fe-pheophytin from crude spinach extract successfully. Similarly, the degradation of natural chlorophylls to pheophytins occurs instantly in natural plant extracts [60] and thermally treated vegetables [61]. Moreover, the extraction and incorporation of chlorophyll and its derivatives have been demonstrated in detail [62,63]. In the supporting literature, chlorophyll derivatives, pheophytins, and Zn-pheophytins from chlorophylls were extracted from spinach, characterized, and evaluated for their antioxidant and anti-inflammatory activities [63,64]. Additionally, Nelson and Ferruzzi [29] utilized gastric and small intestine digestion simulation trials with Fe-chlorophyll; overall, this study indicated that the recovery rate of Fe-chlorophyll was $52.3\%$, the recovery rate of Fe-chlorophyll derivative was $58.7\%$, and digestion results were stable. Therefore, the development of chlorophyll-derived heme mimetics offers opportunities to expand the current Fe fortification strategies. Previous studies have indicated that the chlorophyll molecule can be modified by replacing centrally chelated Mg with other metals, such as Cu, Zn, and Fe, to form new metalloporphyrin complexes [56,57,58]. A successful trial was conducted for assessing the quantitative yield of chlorophylls into pheophytins and Fepheophytins, supporting these findings, and FePhe derivatives were synthesized at 78.5 ± $0.1\%$ efficiency [29]. Due to the limited available literature, further discussion on this area of research is difficult. ## 3.3.1. Microbial Culture In the current study, the extracted LegH underwent a series of experiments to determine the safety level regarding toxic and allergic ingredients. E. coli culture confirmed that pBHA-containing bacteria proliferated in LB medium containing ampicillin (Figure 5A). From the results of culturing P. pastoris with and without LegH sequences in YPD medium, a color difference could be observed when observed with the naked eye. Furthermore, based on the OD results, bacteria containing pBHA were grown in a medium containing ampicillin. Bacteria with a dilution factor of 10 were confirmed to grow 1 h after inoculation, and observable growth of bacteria with a dilution factor of 109 started after 18 h. Thus, it was confirmed that the growth rate was slow in samples with a high dilution factor because of the low number of initial bacteria. P. pastoris containing pJAN was able to grow in YPD medium containing ampicillin because of ampicillin resistance conferred by the pJAN plasmid (Figure 5B). As shown in Table 1, the LegH sequence used did not match the sequences of allergens and toxins that cause allergies. Additionally, 11 P. pastoris proteins with allergen and 13 proteins with toxic protein homology showed similarity to the protein of Saccharomyces cerevisiae, a microorganism previously used for food. In P. pastoris, LegH digestion occurs by the proteolytic enzyme pepsin, at pH 2, similar to that of the stomach [41]. Heme has a long history of safe consumption, while soy LegH is a novel food ingredient. Therefore, soy LegH was subjected to rigorous safety testing, including tests for allergenicity, mutagenicity, chromosome damage, and toxicity [32]. In addition to being rapidly digested by pepsin, soy LegH does not share any meaningful similarity to known allergens or toxins [41,65]. Furthermore, LegH produced by P. pastoris and a 28-day trial on animal subjects indicated that no mortality was associated with LegH administration. This further clarified that there was no LegH-associated adverse clinical evidence, including metabolic, ophthalmological, or histopathological alterations [32]. Therefore, the consumption of LegH using P. pastoris as a food ingredient has no adverse effects, particularly allergenic and toxic reactions. Additionally, Impossible™ Foods incorporated soy LegH protein using the yeast P. pastoris. LegH generated from the nodules of soy plants acts as a plant-based heme molecule. Moreover, LegH has tremendous Fe-binding capabilities, with regulatory functions in oxygen tension and diffusion. The broad-range resemblances of LegH and hemoglobin are red coloration, Fe-binding capability, and flavor [32]. Impossible™ Foods transferred the soy LegH gene into yeast to produce large quantities of LegH protein as sustainably as possible. Production of this ingredient by yeast fermentation has a smaller environmental footprint than digging up soybean root nodules and extracting the protein; however, it is identical to the LegH protein found in such root nodules [41] In addition, the detection and purification of modified LegH were extracted from soybean root nodules. Nodule extracts were first chromatographed on hydroxylapatite at pH 6.8. Over $98\%$ of the peptidases bound to the column and the LegH were recovered in the wash fraction, and subsequent ion-exchange chromatography of the wash fraction at pH 8.0 yielded several fractions [66]. The previous supporting literature indicated a successful extraction of LegH from soy roots [67,68,69]. ## 3.3.2. SDS-PAGE The results for the extracted pBHA were confirmed by electrophoresis. The basic pBHA sequence size was 1987 bp, the LegH-producing sequence was 528 bp, and when the LegH sequence was inserted into pBHA, the size was 2515 bp. The corresponding size was inferred using the 1 kb reference ladder, the sample could be identified at approximately 2500 bp (Figure 6). ## 3.4. Color Measurements Pigments The color coordinates of the extracted pigments for the PBM are listed in Table 2 and pigment weight and concentration are demonstrated in Table 3. Myoglobin, as a control, revealed color coordinates with the L (6.84), a* (10.95), and b* (6.23) values. Similarly, the Fe-chlorophyll sample showed the highest L* value (22.59), whereas a* exhibited 10.60 and b* value exhibited 26.29; these values were all significantly different ($p \leq 0.05$) from the control. Based on the color indices, the L* value of anthocyanin was 3.90 and the a* value was 5.85, which was the fourth highest value, except for the control. However, LegH showed no significant difference with the control. The extraction and generation of natural pigments for PBM is a challenging task, despite the availability of different renewable sources and various approaches [70]. A possible reason for this difficulty could be that the optimal pH of the colorant is dissimilar. This limitation of natural colorants is more likely to be overcome by adding acidulants, such as acetic acid, citric acid, or lactic acid [13]. In the current study, myoglobin, Fe-chlorophyll, and anthocyanin extracts were optimized to the level used in PBM products (Figure 7). For instance, some companies, such as Beyond Meat and Light life, use beet juice or powder to imitate a “bleeding” state for their products, whereas Impossible™ Foods uses soy LegH to confer a red meat-like appearance in its burger products [3]. Various food colorants from natural sources have been incorporated into various foods, such as anthocyanins [13,20,21,22], chlorophyll derivatives [29,30,62,63], and LegH [32,41]. The incorporation of natural colorants into patties were recently reported: the authors synergistically integrated beetroot pigments, laccase, and pectin to enhance the color coordinates of PBM patties [6]. Similarly, decolonization and detoxication of plant-based proteins were reported [11]. Previously, our team optimized and extracted two natural colorants (red yeast rice and lactoferrin), and thereafter synergistically incorporated these colorants into PBM analog patties; this ultimately resulted in the production of PBM analog patties that resembled real meat in many aspects [19]. ## 3.5. Absorbance Level The concentrations of anthocyanin, chlorophyll and LegH extracted were determined at an absorbance of 0.7 at 535 nm. The absorbance at 0.7 was measured at 0.54 g of anthocyanin, 1.00 g of Fe-chlorophyll, and 1.76 g of LegH. The absorbance measurement spectrum and pigment weight with the concentration per colorant are shown in Figure 8. In addition, ideal extraction conditions were determined for each particular dye by determining the maximum absorbance level or optical density value at a precise absorbance wavelength using a UV-visible absorbance spectrophotometer [33]. By measuring the absorbance of the samples, it was confirmed that the absorbance increased from 400 nm to 535 nm and then decreased after the maximum value of 535 nm. Anthocyanins and Fe-chlorophyll showed a decreasing pattern after a 400 nm wavelength. These results correspond to those previously described by Nelson and Ferruzzi [29], and Furuya, Inoue, and Shirai [59]: both studies confirmed the effective synthesis of Fe-pheophytin derivatives in crude spinach extracts. Moreover, He and Giusti [22] describe in detail the incorporation of anthocyanins as natural colorants into food additives, and their absorbance and toxicity levels. The absorbance of the level of various natural pigments has been reported previously by various authors [18,71,72]. ## 4. Conclusions Natural pigments are exceptional sources of bioactive combinations that are considered harmless for human consumption. In this study, anthocyanins extracted from black beans, Fe-chlorophyll derived from spinach, and LegH were produced from microbial culture after the recombination and insertion of a modified plasmid. The effectiveness of anthocyanin extraction is the result of a combination of the solvent used and the applied technique. The chromatographic results reveal successful indications of anthocyanin-related compounds in the extracted sample from beans. Through microbial culture, the leghemoglobin was successfully synthesized by plasmid insertion and the sequence indicated negligible homology to toxic agents. The chlorophylls extracted from spinach and their synthesized derivatives including Fe-pheophytins were characterized by the ion exchange method. Furthermore, extraction technologies should be optimized to provide environmentally feasible and low-cost colorants from traditional and novel sources. 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--- title: Threat Assessment and Risk Analysis (TARA) for Interoperable Medical Devices in the Operating Room Inspired by the Automotive Industry authors: - Andreas Puder - Jacqueline Henle - Eric Sax journal: Healthcare year: 2023 pmcid: PMC10048460 doi: 10.3390/healthcare11060872 license: CC BY 4.0 --- # Threat Assessment and Risk Analysis (TARA) for Interoperable Medical Devices in the Operating Room Inspired by the Automotive Industry ## Abstract Prevailing trends in the automotive and medical device industry, such as life cycle overarching configurability, connectivity, and automation, require an adaption of development processes, especially regarding the security and safety thereof. The changing requirements imply that interfaces are more exposed to the outside world, making them more vulnerable to cyberattacks or data leaks. Consequently, not only do development processes need to be revised but also cybersecurity countermeasures and a focus on safety, as well as privacy, have become vital. While vehicles are especially exposed to cybersecurity and safety risks, the medical devices industry faces similar issues. In the automotive industry, proposals and draft regulations exist for security-related risk assessment processes. The medical device industry, which has less experience in these topics and is more heterogeneous, may benefit from drawing inspiration from these efforts. We examined and compared current standards, processes, and methods in both the automotive and medical industries. Based on the requirements regarding safety and security for risk analysis in the medical device industry, we propose the adoption of methods already established in the automotive industry. Furthermore, we present an example based on an interoperable Operating Room table (OR table). ## 1. Introduction Today, hospitals are increasingly equipped with Internet of Things (IoT) devices, but are not entirely aware of the security and privacy implications thereof [1]. Although a hospital may be certified for Health Insurance Portability and Accountability Act (HIPAA), which is a 1996 U.S. law that governs the security and privacy of Protected Health Information (PHI) and patient access to their medical records [2], they are not prepared for a shared network of IoT and other medical devices [1]. In addition, prior risk management for medical devices mainly addressed functional safety and therefore did not include cybersecurity [3]. Cybersecurity in the healthcare industry, including hospitals, is a relatively new topic [4] since it has been slow to prioritize cybersecurity and is lagging behind other industries in protecting their systems and patient data. To address this issue, hospitals must allocate significant resources toward improving their cybersecurity defenses [5]. A ransomware attack that first increased public awareness of cybersecurity issues in hospital environments happened in 2016 in the Hollywood Presbyterian hospital [6], followed by further ransomware attacks. During the COVID-19 pandemic, these attacks have continued to increase [7], and COVID-19 was the predominant lure in attacks via e-mail [8]. In 2020, a patient had to be transported to another hospital due to a ransomware attack on a German hospital. Even though it could not be entirely proven that this delay caused the patient’s death, this incident represents the first case where the ransomware attack was suspected of having led to a patient’s death [9]. As cybersecurity threats and risks evolve, so do their countermeasures; still, no device can be fully protected [10]. Furthermore, several agencies also see the need to take action in the medical device industry. Accordingly, standards, guidelines, and regulations have been published that deal with the potential harm and life cycle risks from cybersecurity incidents [3]. Thus, threat modeling is recommended by several of these (Section 2). Cybersecurity risks are also a serious issue in the context of software-dominated Electric/Electronic architectures (E/E architectures) in the automotive industry. In 2010, a security analysis exposed a way of attacking vehicle Electronic Control Units (ECUs) with the goal of embedding malicious software [11]. Furthermore, in 2015, a hacker demonstrated how to remotely start a vehicle engine by attacking a connected mobile app [12]. Alongside the rising importance of Vehicle-to-Everything Communication (V2X) and updatable Service-Oriented Architectures (SOAs), the development of secure E/E architectures and data privacy has become increasingly significant. Furthermore, the goal of developing highly automated vehicles uncovers the growing significance of functional safety being guaranteed during the whole product life cycle. Therefore, standards and regulations, as well as methods, were published to enable the assessment of safety, security, and privacy-compliant development processes. With the goal of measuring the fulfilment of these requirements, models were developed. However, while the trends regarding connectivity, Software Over The Air (SOTA) updates, and automation are not yet established in the industry, these methods need to be adapted and enhanced constantly. Furthermore, vehicles have a growing number of internal and external interfaces that enable connectivity and communication with other devices or infrastructure. Alongside these developments, the importance of security rises constantly. Risks and security attacks have been consistently demonstrated over at least the past 15 years [13]. Ensuring quality in the face of risks and threats is a mandatory requirement for businesses in healthcare. According to a 2017 study by McKinsey [14], the direct costs associated with poor quality worldwide in the medical device industry in 2016 were estimated to be between USD 18 billion and USD 22 billion. These costs included the labor required for remediation efforts, internal and external quality failures, and non-routine external failures. The study also found that the direct costs of poor quality accounted for a significant portion of total sales in the medical device industry, with estimates ranging from $11.6\%$ to $16.3\%$ of every sale’s USD spent on these costs in 2016. Thus, improving quality throughout the life cycle of a medical device by implementing effective processes and methods can have a positive economic impact. In addition to traditional concerns around quality and safety, the growing importance of cybersecurity in the medical device industry means that companies must also prioritize quality in this area to ensure the security and protection of patient data. Problem: As more and more devices in the Operating Room (OR) are connected with each other and are becoming part of the IoT, devices and networks in hospitals need to be secured against potential attackers. There is little knowledge about the security threats in the OR today and most manufacturers still rely on traditional security measures, such as security by obscurity [15] or Defense-in-Depth strategies [16]. While the first is already proven as a non-efficient measure, the latter is still a prevalent strategy, although it is considered outdated and is being increasingly replaced by a zero-trust security model in other fields [17]. In addition, the responsibility for hospital security is not clear, as it is shared among device manufacturers, healthcare providers, security experts, patients, and governing bodies [18]. Cybersecurity is still in the process of being recognized as vital in the whole healthcare industry (Section 1). This is reflected by the numerous collections of standards and guidelines that exist around the world, which are constantly being renewed or reworked (Section 2.5). As a result, there are not yet sufficient processes and methods in place that are comparable to those in other industries, such as the automotive sector. Additionally, cybersecurity must be approached differently in different sectors of the healthcare industry. The growing share of software in the healthcare sector has to be distinguished. While, on the one hand, smartphone apps for healthcare make it easier for patients to communicate with their attending physician and collect health data, medical devices such as surgical robots operate in a different environment. Therefore, these fields face different threats and risks and need to be regarded in other ways. Contribution: In [19], we showed that the main trends in both the automotive and medical device industries face challenges such as higher connectivity, SOA, and SOTA updates. We presented a mixed E/E architecture for OR tables in order to face the challenges of future medical devices and also addressed security risks by introducing Identity and Access Management (IAM). Following up on this, we investigated automotive security processes and methods for improving the security of connected medical devices. By using the existing threat models in the automotive industry, we evaluated their suitability for exposing security risks and examined their relation to safety. Furthermore, we examined the threat modeling recommended and required by different standards and guidelines in the medical context. Here, risk evaluation is important and already well-established, but the industry is just starting to adopt methodological approaches for security risk analysis. We focused on models originating from the automotive industry that are applicable to medical devices. Therefore, an OR table represents an appropriate representative for the execution of a Threat Analysis and Risk Assessment (TARA) in order to identify the threat landscape for OR equipment. Outline: In this article paper, we first provide the background and state of the art on the topic of security and safety in development processes and research regarding medical device communication in ORs. We then compare and contrast safety and security standards, guidelines, and methods in these fields (Section 2). In Section 3, we review related work in the automotive and medical fields. Based on the overview of security threat models from Section 2, we analyze necessary adoptions to the medical context (Section 4). Furthermore, we present a TARA for an interoperable OR table in an OR network and combine it with risk analysis approaches (Section 5). Lastly, we summarize our work and provide an outlook on future directions for research in this area (Section 6). ## 2.1. Medical Communication Systems in the Operating Room The communication of medical devices in the past few decades has been dominated by proprietary communication protocols that have been bilaterally developed by medical device manufacturers [19]. In terms of security, they often relied on countermeasures such as security by obscurity, which is considered as insecure today (Section 1), or limiting the invocable functionality via the network interfaces [20]. However, in particular, robotic medical devices in the OR will need to be competitive in the future regarding their connectivity interface [21]. These developments led to several projects with the aim of introducing Cyber Physical Systems (CPSs) and improving manufacturer-independent interoperability in the OR. The Smart Cyber Operating Theater® (SCOT®) project, started by the Tokyo Women’s Medical University, focuses on the use of CPSs in the Hybrid Operating Room (HOR) [22], which allows imaging procedures to be performed during surgery in a single OR. The Medical Device Plug and Play (MDPnP) project aims to enable the use of heterogeneous medical devices from different manufacturers in a medical device system and has introduced the concept of the Integrated Clinical Environment (ICE) to describe this environment [23]. Finally, the ISO/IEEE 11073 Service-oriented Device Connectivity (SDC) is a set of communication standards designed to enable manufacturer-independent medical device interoperability in the OR [24]. Like MDPnP, it uses web services and is based on an SOA. A comprehensive overview and comparison of the presented projects and protocols can be found in [22]. ## 2.2. Safety and Security in Life Cycle Processes There are several development models, but one of the most popular in software-dominated industries is the V-model [25] (Figure 1). It provides a structured approach to the development of systems, including mechanics, electronics, and software. An analysis of the requirements and specifications for the system is the first step in the application of the model. Afterwards, the development, integration, and validation of the mechanical, electronic, and software-based components of the system are executed. Alongside the formulation and discovery process of requirements, the elicitation of a hazard and risk analysis, as well as functional and technical analyses addressing safety and security, are conducted. The goal of these analyses is to identify risks and threats in order to define requirements for system development. In the automotive industry, models for the analyses conduction have been established, such as TARA and Failure Mode and Effects Analysis (FMEA). A comprehensive cybersecurity process is proposed by the National Institute of Standards and Technology (NIST) "Framework for Improving Critical Infrastructure Cybersecurity" [26], which is recommended and adapted by the U.S. Food and Drug Administration (FDA) guidance documents [10,27]. Medical device manufacturers should assess and address the risks posed by vulnerabilities in their devices, considering the magnitude of the problem and the risks encountered. They should also evaluate the residual risk, benefit/risk ratio, and risk introduced by the remediation. Changes to address controlled risk vulnerabilities are generally considered as product improvements and not recalls by the FDA. Therefore, routine cybersecurity updates are usually considered as device enhancements. Ref. [ 27] Five core functions (Identify, Protect, Detect, Respond, and Recover) should be adopted and utilized [26,27,28], and Draegerwerk has implemented a cybersecurity process that includes similar actions [29]: Identify: Manufacturers should define the security and key performance characteristics of their products and the potential severity of patient harm in the event of a compromise, and use threat models to assess the exploitability of vulnerabilities and determine the effectiveness of proposed or implemented remedies. Additionally, they should also analyze various sources of quality data, actively seek out and address sources of cybersecurity signals, and develop strategies to improve their ability to detect them. The activities of the identification function are essential for the other functions and can be considered as the basis of the framework. Protect: Supporting the ability to contain the impact of a cyberattack is the objective of this function. Manufacturers should characterize and assess identified vulnerabilities, conduct cybersecurity risk analyses and threat modeling for each of their devices, and update these analyses over time. Furthermore, they should implement countermeasures such as IAM or awareness training for users. Detect: Manufacturers should analyze possible threat sources and consider incorporating design features that enhance the device’s ability to detect threats and produce forensic evidence in the event of an attack. They should also have a process in place to assess the impact of a cybersecurity signal on all devices within their product portfolio and on specific components within a device. Respond: Medical device manufacturers should implement device design controls to take action in case of a detected cybersecurity incident. They should assess and provide users with compensating control mechanisms to mitigate the risk of patient harm and ensure the cybersecurity of their devices. Manufacturers should address identified cybersecurity vulnerabilities by developing and implementing remedial actions. Recover: Manufacturers should take steps to support the timely restoration of normal operations to minimize the impact of a cybersecurity incident. This can include the timely delivery of security updates. Moreover, they should inform users and implement a coordinated vulnerability disclosure policy and practice. In this paper, we focus on the identification of vulnerabilities in medical devices. ## 2.3. Threat Modeling To model security analysis, different automotive-specific and non-industry-specific approaches exist. According to Figure 1, the security analysis is part of the system’s requirement step in architecture development. TARA is one method used to identify security risk and is based on an attacker-centric approach. Premised on historical information such as incident reports or contemporary security measures, threats are analyzed. After their identification, the methods and objectives of the potential attackers are listed and the exposure and vulnerability toward these risks are identified and documented. The risks are assigned with necessary protection procedures and compared to those existing in the company. Thereby, the security strategy and development steps are pointed out [30]. TARA and FMEA are two different methodologies for security-related risk analysis and risk management. While the TARA takes place in an early development phase for system requirements formulation, the FMEA focuses on identifying and evaluating potential failures and their impacts on a system thereafter (Figure 1). These requirements and the system design that resulted thereby is the basis of the FMEA. It is used with the goal of identifying and evaluating potential failures within the system or product. The Process for Attack Simulation and Threat Analysis (PASTA) [31] is a risk-based threat modeling framework that aims to integrate business objectives and technical requirements, involve key decision makers, and produce an asset-centric output in the form of threat enumeration and scoring. PASTA consists of seven stages of analysis, including defining objectives and technical scope, decomposing the application, conducting a threat and vulnerability analysis, modeling attacks, and analyzing the risk and impact. To facilitate these stages, PASTA employs various design and elicitation tools, such as high-level architectural diagrams, Data Flow Diagrams (DFDs), attack trees, and use and abuse cases. PASTA is widely recognized as a risk-based framework that adopts an attacker-centric perspective. The Operationally Critical Threat, Asset, and Vulnerability Evaluation (OCTAVE) [32] is a risk-based approach to cybersecurity assessment and planning that aims to evaluate organizational risks and identify vulnerabilities in an organization’s information infrastructure. It consists of three phases: building asset-based threat profiles, identifying infrastructure vulnerabilities, and developing a security strategy and plans. OCTAVE was originally designed for large organizations, but a version called OCTAVE-S has been developed specifically for small organizations. While the method is comprehensive and flexible, it requires a significant time commitment and the documentation can be large and vague [33]. There are plans to update OCTAVE, which may address these issues [34]. In this context, further methods exist. The STRIDE model (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privilege) is a qualitative approach by Microsoft using a system’s DFD as the base for an evaluation [35]. Security-related system properties are labeled and checked regarding security characteristics, and threats are identified. Automotive-specific methods that are based on the STRIDE model are HEAling Vulnerabilities to ENhance Software Security and Safety (HEAVENS) for all systems of the E/E architecture and Security Aware Hazard Analysis and Risk Assessment (SAHARA) for embedded systems. The SAHARA model checks for confidentiality, availability, and integrity attributes and enables threat and risk identification to extract a threat level and a security level [36]. HEAVENS is an approach combining Microsoft’s STRIDE with Evita [37], a further attack-scenario-based method. HEAVENS includes authenticity, authorization, non-repudiation, privacy, and freshness, on top of the previously mentioned attributes. It evaluates the whole E/E architecture and provides a risk matrix as a result that includes threat as well as impact levels, but also high-level security requirements [35]. The extension HEAVENS 2.0 is improved according to gaps that could be identified when comparing HEAVENS 1.0 to the requirements of ISO/SAE 21434. It includes an attack path analysis and risk treatment decisions with the result of identifying cybersecurity goals, and claims to be compliant with the regulation [13]. HEAVENS 1.0 and 2.0, as mentioned in [13], have the potential to be used in industries with similar characteristics, such as the medical device industry, with some slight modifications. There are more threat-modeling approaches than those described here and each of them has its dedicated application area. Shevchenko et al. provide a comprehensive overview of twelve different threat-modeling methods [34]. ## 2.4. Safety and Risk Classification for Medical Devices According to standard IEC 62304 [38], the software can be classified into three categories based on the potential risk level that it poses (Figure 2). Class A software poses the lowest risk, and can only be classified as such if no hazardous situations can occur due to software errors, or if any hazardous situations can be adequately controlled to prevent unacceptable risks. If the measures put in place to control risk are not sufficient to prevent unacceptable risks, the software is classified as B if it could potentially cause non-serious injuries, or C if it could potentially cause serious injuries or death. An injury is considered serious if it requires medical intervention to prevent permanent harm or is life-threatening. Any risk that could result in serious injury is considered unacceptable. Furthermore, the IEC 60601-1 standard for medical devices [39] requires that the devices are designed in such a way as to prevent the first failure of a system from causing significant risks. This means that the device should be designed with sufficient safeguards and redundancy to ensure that a single failure or malfunction will not result in an unacceptable level of risk to the patient or user. The European Medical Device Regulation (MDR) 2017 [40] provides guidance that determines the class of a medical device based on its risk profile. The classification of a medical device determines the level of regulatory oversight and the requirements for conformity assessment and market surveillance. Whereas the MDR classifies into four different categories, the FDA uses three different categories (Table 1). The classification of a product is based on the type of product and the risk that it poses to patient health. It helps to determine the necessary regulatory requirements for each product and to ensure safety and effectiveness. The risk criteria are metrics such as the application time or degree of invasiveness. The provided examples (Table 1) are meant to be general and are not intended to be exhaustive or definitive, as the classification of a medical device can vary based on its specific characteristics and intended use. The classification is therefore always determined for a specific, individual product [41]. In addition, if a medical device controls data from a higher-classified device in an interoperability case, it inherits that classification according to MDR [40]. ## 2.5. Medical Device Standards and Regulations The IEC 62304 [38] is a standard that specifies the software development process for medical device software, including requirements for the design, testing, and validation of software. According to the IEC 62304 [38] standard, medical device manufacturers are required to implement a risk management process in accordance with ISO 14971 [42]. The FDA also recommends using the qualitative severity levels outlined in ISO 14971 to assess the impact on health when evaluating the severity of risks [3] (Table 2). The MDR and relevant standards such as ISO 13485 [43], ISO 14971 [42], and ISO 24971 [44] outline specific requirements for risk management in the life cycle of medical devices. For safety risks, FMEA is a tool that has been commonly used by medical device manufacturers for risk management [45], but it does not meet all of the requirements on its own and is not designed for security risk analysis. While the term “risk” is defined differently in ISO 14971 and in FMEA, it can still be useful for risk management when used in combination with other tools and methods [46]. In the course of this paper, only the Software Failure Mode and Effects Analysis (SFMEA) will be of relevance. After the identification of risks, a risk matrix (Table 3) can be used to determine a software item’s safety classification (Section 3.1) by mapping its risk and function to a severity level, which is then used to assign the classification. In this case, this can be, as an example, low risk corresponding to class A, medium risk corresponding to class B, and high risk corresponding to class C. IEC 81001-1 [47] and IEC 81001-5-1 [16] provide guidelines for the management of cybersecurity in healthcare technology. IEC 81001-1 is the general introduction to the IEC 81001 series and provides an overview of the principles and concepts related to cybersecurity in healthcare technology. It outlines the scope and purpose of the series of standards, as well as the main terms and definitions used in the standards. IEC 81001-5-1, on the other hand, provides specific guidance on how to manage cybersecurity risks in healthcare technology. It outlines a structured approach for identifying and evaluating cybersecurity risks, implementing protection measures, and responding to and recovering from cybersecurity events. It also provides recommendations for the design and development of secure healthcare technology, as well as for the procurement, maintenance, and decommissioning of such technology. The IEC 81001-5-1 complements the IEC 62304 [38] with cybersecurity requirements. Together, these standards provide a comprehensive framework for ensuring the safety and effectiveness of medical devices by addressing both cybersecurity risks and the software development process. They can be used in conjunction with each other to ensure that medical devices are developed and used in a way that is safe and reliable for patients and users. The IEC 81001-5-1 standard does not explicitly require the use of a TARA, but it is recommended by IEC 81001-5-1 to follow the processes outlined in the IEC 62304 standard, which addresses safety in the life cycle process, in order to identify the necessary activities for implementing security measures. Thus, by applying common TARA processes, it is possible to address both standards simultaneously. Due to the rapidly changing environment in the medical device industry, the FDA released premarket guidelines in 2014, a draft update in 2018, and another draft update in 2022 (Figure 3). The new revision recognizes the need for a continuous, iterative approach to device cybersecurity throughout the product life cycle [10]. One of the articulated security objectives in these guidelines pertains to the ability to secure and timely update and patch devices. The FDA’s proposed security risk management strategy for the product life cycle also advises manufacturers to have the necessary resources to identify, assess, and mitigate cybersecurity vulnerabilities as they emerge throughout the device’s lifespan. Documentation that is updated throughout the product life cycle, such as threat models, can facilitate the rapid identification of the impact of vulnerabilities once a device has been released, and can support timely Corrective and Preventive Action (CAPA) activities [10]. To support manufacturers in the creation of threat models, the FDA funded the “Playbook for Threat Modeling in Medical Devices” [48]. The FDA postmarket guideline [27] recommends that manufacturers proactively address cybersecurity risks in their products and monitor, identify, and address any vulnerabilities or exploits as part of their postmarket management. The guidelines also outline a risk-based framework for determining when changes to address cybersecurity vulnerabilities in medical devices should be reported to the FDA and specify circumstances in which the agency does not require advance notification or reporting. The guidelines recommend that manufacturers assess the risk of patient harm based on the likelihood of exploitation, the impact of exploitation on the device’s safety and essential performance, and the severity of patient harm if exploited. The International Medical Device Regulators Forum (IMDRF) has released a draft document outlining principles and practices for medical device cybersecurity [49]. The aim of this guidance is to identify and mitigate potential risks to patient safety by analyzing the impact of cybersecurity threats on the device performance, clinical operations, and diagnostic or therapeutic errors, and does not address issues related to data privacy breaches or the manufacturer’s enterprise. The guideline also advises the use of a threat model for medical devices as part of risk management. As it is provided by the IMDRF, it aims to support regulatory processes by phrasing out related requirements. In contrast, the Canadian “Pre-market Requirements for Medical Device Cybersecurity” [50] only mandates a risk analysis and management for certain high-risk medical devices. Furthermore, the MDR calls for cybersecurity risk management according to the state of the art, which is not further elaborated, without mentioning threat modeling [40]. In addition, privacy in terms of HIPAA becomes a greater concern for medical device manufacturers (Section 1), as the HIPAA has the purpose of establishing national standards for the protection of PHI. HIPAA applies to all entities that handle PHI, including healthcare providers, insurance plans, and healthcare clearinghouses. HIPAA requires these entities to implement appropriate physical, technical, and administrative safeguards to maintain the confidentiality, integrity, and availability of PHI. Additionally, it includes provisions that allow individuals to access, modify, and control the use and disclosure of their PHI [2]. Figure 3: Updated evolution of medical device cybersecurity regulations based on [51,52]. ## 2.6. Automotive Systems Evaluation and Safety Integrity Level In the automotive industry, the development of applications requires the functional safety evaluation thereof. The standard ISO 26262 “Road vehicles—Functional safety” defines the product development processes that must be followed depending on the criticality of an application [53]. ISO 26262 classifies four different Automotive Safety Integrity Levels (ASILs) based on the risk exposure, severity, and controllability (see Table 4). The levels are Automotive Safety Integrity Level (ASIL) A, ASIL B, ASIL C, and ASIL D, where D defines the highest level of initial hazard and A resembles the lowest [54]. The additional Quality Management (QM) level represents a category where systems or components can be managed by established QM methods. The ASIL level is accompanied by goals for the identified hazards according to system safety requirements. It is necessary to prove the fulfilment of safety requirement compliance during architecture development, which is especially important due to the multilayered supplier structure typical in the industry [55]. ## 2.7. Automotive Standards and Regulations For safety analysis and quality management, the FMEA is often used throughout several industries. In the automotive industry, the FMEA is part of the standard SAE J1739 as Potential FMEA including Design FMEA, Supplemental FMEA-MSR, and Process FMEA. As shown in Figure 1, the FMEA is part of the system’s design in architecture development and is based on functional safety. The method is used to evaluate the potential of a failure of a process, a system, and subsystems, services, or designs [56]. The goal is to identify risks and problems that result in the deviation of a specific function from its intended functionality. In order to achieve that, the FMEA is used to identify the types of failures and their causes and effects to determine, evaluate, and reduce risks. The steps involved are structural analysis, functional analysis, failure analysis, risk analysis, optimization, and documentation [57]. Updating automotive application software, firmware, or other software packages within the Electric/Electronic (E/E) are prevailing challenges to be solved and secured in the industry [58]. While some OEMs have already demonstrated SOTA updates [59], there is no standardized procedure thus far. Challenges to be solved are related to safety, security, and privacy. In addition, there is uncertainty regarding process frameworks to comply with standards relevant to the release of an update [60]. Related to SOTA updates and automotive cybersecurity, the drafts of the UN vehicle regulation 155 and 156 [61] were published. In Regulation 156, SUMSs are defined as process models for update delivery. These systematic approaches applied by OEMs need to be certified to fulfill security requirements in the SOTA update context. Furthermore, a risk assessment as well as methods related to cybersecurity attacks are specified in Regulation 155. It defines a CSMS as a legislative prerequisite for every vehicle OEM. It requires the OEMs to ensure and document all demanded processes and the capabilities in the near future to be certified by authorities. These regulations are not yet mandatory. The prevailing trends are followed by an architecture evolution toward dynamic SOAs or mixed architectures that combine signal-based and service-oriented communication. A key challenge to be solved is to realize SOTA updates to regularly add functions but also to update security countermeasures. For the latter, security-related measures from IAM over intrusion detection and firewalls are required [62]. Focusing on automotive updates, UN Regulation No. 156 [61] specifies certificates and general documents for update conformity within the industry. This standard, as well as the ISO 24089 [63] draft, describes requirements for update engineering and approval. The OTA update development is specified and several recommendations are given. Still, there is no detailed update development and deployment process model to serve as a blueprint for integrating update engineering for OEMs. In the automotive industry, safety and security have become more and more important, not only over the vehicle life cycle but already during development. Approaches to prevent threats in the context of functional safety and cybersecurity are gaining importance. Since 2011, the ISO 26262 standard has provided guidelines for automotive safety, while the automotive security guideline SAE J3061 was published in 2016 [64]. The latter recommends the usage of TARA methods. These models are supposed to discover threats, assess the risk of these threats, and analyze a risk level accordingly. Besides the SAE J3061 Cybersecurity guidebook, the ISO/SAE 21434 regulation defines an automotive-specific cybersecurity engineering standard concerning the whole vehicle life cycle [65]. A key aspect of the standard is the TARA, which is used to identify security risks and threats, with the purpose of developing countermeasures and mitigation strategies [13]. ## 3.1. Medical To address vulnerabilities in medical devices that monitor patients’ vital signs, Luckett et al. suggested using attack graph modeling to identify these, assess risks, and develop strategies for protecting medical devices from attackers [66]. The researchers examined common vulnerabilities and attack strategies related to these devices, including Bluetooth-enabled sensors and Android applications. They provided an example of attack graph modeling for a theoretical device to highlight vulnerabilities and potential mitigation techniques for designing similar devices. Since the integration of SOA in the automotive and medical industries is increasing, the shift in communication patterns will also impact information security measures. In [67], the authors compared different SOA protocols in these industries and explained the underlying communication patterns, showing that both domains can exploit synergies. They also presented a methodology for developing an SOA-based Intrusion Detection System (IDS) by deriving relevant features. Furthermore, they contributed to the understanding of SOA protocols and their potential use in proposing an IDS for both the automotive and medical industries. Based on a use case for medical devices in an OR connected via SDC, the authors analyzed threats in network communications in the context of anomalies. Vakhter et al. provided an elaborate overview of threat modeling applicable to miniaturized wireless biomedical devices and proposed a domain-specific qualitative and quantitative threat model [68]. This threat model focuses on noninvasive direct attacks against telemetry interfaces and uses them for risk analysis. In their position paper, Sion et al. discussed the strengths and weaknesses of security threat modeling that is based on DFDs, and motivated their research with a DFD for an Health Information System (HIS) [69]. Despite advantages such as technology independence, complexity management, and simplicity of notation, they pointed out disadvantages such as a single level of abstraction, data modeling only by labels, and a lack of a set of security concepts. Ahmed et al. provided an evaluation model for the cybersecurity of hospitals [4]. The goal of their research was to create a model that helps healthcare facilities understand and assess their current cybersecurity status, identify potential risks, and implement measures to mitigate those risks. This model can be used as a tool to help hospitals understand their current cybersecurity situation and make informed decisions about how to improve it. In addition, proposed cybersecurity measures can be incorporated into the design of new healthcare facilities before they become operational. In [70], the authors proposed a use case approach for assessing the cybersecurity and privacy requirements of Point of Care (POC) medical devices. Furthermore, they detailed the use case approach in the context of a real healthcare IT infrastructure that includes various components, such as an HIS, application servers, and medical devices, as well as interactions with different participants. This approach can also be used to analyze cybersecurity and privacy risks in various threat scenarios and provide information for decision making and regulatory compliance. POC medical devices are typically used by clinicians to provide near-patient care and/or diagnosis and treat many patients after appropriate preparation. In contrast, Personal Health Devices (PHDs) are used in a private or domestic setting by a single person and are generally assigned to that person (Ref. [ 22]). Nevertheless, Jofre et al. focused on smartphone apps as POC devices, which should be rather considered as PHDs in the sense of this article and the clinical Information Technology (IT) infrastructure. In this research, we focused on the analysis of POC medical devices in OR, an area that has not been adequately addressed in previous studies. Many of these previous studies have concentrated on wearable and PHDs or the clinical IT infrastructure, but have not included a formal process using a TARA approach or considered necessary medical standards for safety and security. The authors aim to fill this gap in the literature by examining POC medical devices in OR networks and considering these important factors. The results can also be applied to other areas of the hospital, such as Intensive Care Units (ICUs). Fernandes et al. investigated the use of techniques based on Threat Artificial Intelligence, Chaos, Entropy and Security (TAICE&S) for solving cybersecurity problems in cryopreservation laboratories [71]. Their research aimed to address General Data Protection Regulation (GDPR) issues in this type of laboratory using techniques derived from the relationship between TAICE and cybersecurity. In addition, the authors used logic programming and AI-based techniques for knowledge representation and reasoning, as well as artificial-neural-network-based computational frameworks. They also included a case study of data collection and processing on security policies in cryopreservation laboratories. Radanliev et al. proposed a concept for a healthcare system supported by autonomous artificial intelligence (AutoAI) [72]. The aim was to use edge health devices with real-time data to prepare and adapt the health system for future pandemics. The authors developed two scenarios for the application of cybersecurity with AutoAI, namely a self-optimizing predictive cyber risk analysis of health system failures during a disease X event, and a self-adaptive prediction of medical production and supply chain bottlenecks during future pandemics. These scenarios were developed to address the logistical challenges and disruptions of complex vaccine distribution production and supply chains with optimization algorithms. The new methodology presented in this paper provides a practical application for designing a self-optimizing AutoAI capable of predicting cyber risks in healthcare systems through real-time algorithmic analysis. Furthermore, it can be applied to the design of a self-adaptive AutoAI specifically suited for predicting bottlenecks through the autonomous analysis of digital healthcare systems. The authors highlight the need for interdisciplinary research to address concerns about IoT risks and security and propose solutions that promote the safe development of digital health systems by integrating AI algorithms into vaccine supply chains and cyber risk models. Silvestri et al. conducted a study using machine learning models to analyze natural language documents related to healthcare cyber threats and vulnerabilities [73]. Using BERT and XGBoost neural language models for a threat and vulnerability analysis, the authors conducted experiments using cybersecurity news from Hacker News and Common Vulnerabilities and Exposures (CVE) vulnerability reports. In addition, they demonstrated the effectiveness of the proposed approach, which provides a realistic way to assess threats and vulnerabilities using natural language text, and enables it to be applied in real-world healthcare ecosystems. It also recognized the challenges of analyzing threats and vulnerabilities in healthcare due to a large amount of unstructured natural language data and the complexity of the language used in cybersecurity. ## 3.2. Automotive In the automotive industry, security risks related to highly connected vehicles and V2X have received much attention for several years [74,75]. The threats include the risks caused by attacks on the vehicle network [76]. An external party gaining access to this network may also cause deaths or massive damage [77]. The focus of prevalent research is therefore on generating security by design in E/E architecture development to minimize the risk of attacks. Standards such as the ISO 26262 [53] and the influence thereof on SOTA are studied in [78,79,80] among others. Security risks permanently increase in the context of Advanced Driver Assistance Systems (ADAS) or V2X, bringing it to the fore of research [76]. With the purpose of extracting potential risks and vulnerabilities for vehicles and E/Es, security analysis models exist, e.g., [81]. One of the numerous models and frameworks for automotive TARA is known as the HEAVENS security model [81] (Section 2.3). In 2017, an analysis was conducted by Kreissl [82] to assess the security of the Scalable service-Oriented MiddlewarE over IP (SOME/IP) protocol within an automotive onboard communication system. This evaluation identified a total of 18 potential threats within an automotive onboard communication system using SOME/IP. Using the HEAVENS [83] risk analysis method, 11 of these threats were classified as high risk and 3 as a critical risk. The main issue identified was the lack of security features in SOME/IP, leading the author to propose various use cases and associated security properties, as well as discuss potential security mechanisms to address these issues. ## 4. Threat and Risk Assessment (TARA) Adoption HEAVENS 1.0 has been successfully implemented in the automotive industry, which has comparable safety and security requirements to the medical device industry. Since HEAVENS 1.0 has certain shortcomings, such as counter-intuitive threat values [13], a low possibility for customization, and a low process efficiency, HEAVENS 2.0 was created to address these issues. In addition, the creators of HEAVENS 2.0 declared the model as suitable for medical devices (Section 2.3). Furthermore, it fulfils the threat modeling as well as the risk analysis in a single process (Figure 4). Therefore, we chose HEAVENS 2.0 as the most suitable TARA for medical devices and chose to apply it to our interoperable medical device use case (Section 5). Both HEAVENS models are based on the evaluation of functional use cases. Based on that, the framework is used to perform a threat and risk analysis in a joint process. The output of the model execution is a risk matrix accompanied by security requirements and methods [35]. The HEAVENS application (see Figure 4) starts by defining the item under examination. The specific use case needs to be defined precisely and system boundaries are required to be set. The following asset and threat scenario identification follows the STRIDE model and uses the data flow of the specific item to identify potential threats. For HEAVENS 2.0, new steps are to be carried out after these activities. At first, attack paths for the identified threats are examined by creating attack trees to identify the root of a threat. Afterwards, a feasibility rating is associated with the threats to display the attack potential. The result of this task is an attack feasibility rating that takes into account access means, asset exposure, and the knowledge of an item, for example. At the same time, an impact rating is calculated based on safety and privacy, as well as operational and financial characteristics. The ratings are used to conduct a risk analysis starting with a risk determination of the risk. This, in turn, is the basis for the risk treatment decision being either its avoidance, if possible, or its acceptance, reduction, sharing, or transferal. While the reduction is followed by cybersecurity goals to be determined, the reduction, sharing, or transferal results in cybersecurity claims. The latter describes statements constituting reasons for risk acceptability [13]. In conclusion, the HEAVENS 2.0 model is a comprehensive qualitative and quantitative approach to identifying risks and threats with the purpose of identifying, preventing, or reducing them. ## 4.1. Differences and Similarities in the Automotive and Medical Fields Although the medical and automotive industries have similar safety and security risks, there are differences that make an unchanged transfer of methods not fully appropriate. A key factor distinguishing the industries is the operating environment. Whereas medical devices operate in a more static and easier-to-isolate area, vehicles are exposed to other surrounding conditions as they are moving in a rather unrestricted, open environment and interact with each other as well as infrastructure and further systems. In an OR, it is rather unlikely that unknown devices, which are not operating in the hospital network, interact with existing ones. To introduce and test new equipment in the OR, there is a commissioning process; after this, it can be used in surgery [24]. In that sense, an OR can rather be compared to a restricted area such as a car workshop for vehicles. Considering the case of SOTA updates, the necessity of in-use updates is not strictly relevant for medical devices. The controllability of the less mobile devices exposes the SOTA update process, according to the previously mentioned boundary conditions, to fewer safety and security risks than vehicles. Real-time systems can additionally be seen as a slight difference. An example is ADAS functions. To enable highly automated driving, the vehicle systems need to fulfill strict time constraints and respond to changing environmental circumstances in real time. Regarding medical devices, Real-Time Operating System (RTOS) are necessary and in use; still, the time constraints are less strict as the connections to other devices are foreseeable. Other distinctive features are computing units and the backend infrastructure. In a hospital, these entities can be hosted within the private network that the medical device operates in. In the automotive industry, vehicle fleets need to be controlled and protected; therefore the connection between these vehicles and a backend infrastructure happens in various public as well as private networks. Even if there are differences between the industries, security risks that arose and cyberattacks that happened in the past are similar to a high degree. It has been observed that the technology used in the automotive and medical industries are partially similar [19]. Due to this technological overlap, it is likely that vulnerabilities that have been identified in the automotive industry could also be present in the medical field. This highlights the need for both industries to be proactive in securing their systems and protecting against potential cyber threats. This technological overlap with similar requirements, especially regarding safety and security, makes similar processes and measures applicable in both industries. The security guidelines and standards are not as concrete in the medical field because ISO/SAE21434 has a defined TARA workflow that must be met step by step, whereas the medical device guidelines and standards only require a threat model and a corresponding risk analysis according to ISO 14971. This leaves manufacturers greater room for (mis)interpretation. It may result in potentially inadequate analyses and measures. Nevertheless, procedures and methods are proposed that are unfortunately not adapted to the medical field and mostly originate from the IT sector. The software safety classification imposed by IEC 62304 and the corresponding process requirements are comparable to ISO 26262, although three different classes are to be distinguished rather than four different classes. However, a risk classification for the entire product, as required by the FDA and MDR, is not applied in the automotive industry. In terms of processes and methods associated with safety risks, such as FMEA, both areas appear to be at a similarly high level, with slight differences in the individual areas. ## 4.2. Heavens 2.0 in Medical Context Due to the previously mentioned differences, some adoptions find it necessary to use HEAVENS 2.0 in the medical context. Medical devices are more heterogeneous and face different threats depending on their intended use and application. Therefore, the external threat landscape must be determined prior to the first steps “Item Definition” and “Asset Identification”. Furthermore, for an effective “Risk determination” and “Damage Scenario Identification”, the medical device class of the device itself and the connected devices needs to be taken into account. For example, the dosing of an infusion pump that is affected by a cybersecurity threat may pose a different risk in a medical devices operation than as a vulnerability in a thermometer [27]. Once the threat landscape is identified, it can be reused for other medical devices in the same context, such as devices in an OR such as OR tables or angiography systems, which are C-shaped devices for interoperative imaging with X-ray technology. The steps from HEAVENS 2.0 can generally be applied for medical devices, but the differences in automotive and medical contexts need to be considered (Figure 4). ## 4.3. Threat Landscape in Operating Rooms The FDA recommends that manufacturers fully consider cybersecurity risks when designing devices by evaluating the potential safety and security risks within the context of the system in which the device will be used. This involves making assumptions about the system and environment, such as hypothesizing that a hospital network may be hostile and that an adversary may have the ability to alter, drop, or replay packets [10]. In the past, there have been several instances of cyberattacks and reported vulnerabilities in hospital equipment and medical devices, highlighting the importance of identifying potential threats. Threat 1: A ransomware attack in 2016 on the Hollywood Presbyterian Medical Center in Los Angeles led to the shutdown of the hospital’s computer systems [84]. Later that year, two additional hospitals in California [85] and one in Canada [86] were targeted by ransomware attacks, and the tendency of this kind of attacks is rising (Section 1). The 2017 Wannacry ransomware attack affected specific gantry and robot imagers, as it could be transmitted through various means, such as the use of infected memory sticks or the opening of malicious emails on the system by clinicians [87]. Threat 2: In 2017, it was discovered that some cleaning and disinfection equipment could potentially be accessed and have its data manipulated during an attack on a hospital, laboratory, or practice’s internal network. A hacker could potentially exploit this vulnerability by attempting to misuse the data to gain illegal access and manipulate program control. They could also try to forge batch protocols through data analysis and knowledge of instrument preparation in order to hide any manipulations. This potential vulnerability also applies to unauthorized actions by individuals with legitimate access to the relevant network [88]. Threat 3 In 2019, insulin pumps were recalled due to the potential for attackers to remotely adjust the dosage of insulin delivered to a patient [89,90]. Threat 4: In 2019, the FDA issued a warning about a potential cyberattack on certain models of implantable cardiac devices, clinic programmers, and home monitors resulting from a wireless telemetry protocol [91]. Threat 5: In 2019, the German Federal Institute for Drugs and Medical Devices (BfArM) issued a warning about certain sterilizers, stating that an attacker could potentially manipulate the system to influence the efficiency of the sterilization process via remote access [92]. Threat 6: In 2020, the FDA issued a warning about vulnerabilities in certain models of central stations and telemetry servers, which are used to track vital signs of patients [93]. Attackers could remotely control the device and interfere with alarms, e.g., by silencing them or generating false alarms. There are other potential threat landscapes to consider, such as the manufacturing line. However, for the purposes of this research, we focused on the OR (Figure 5). By including the threats (Threat 1–6) listed before, the following threat sources can be derived: Threat Source 1—Clinical IT-Infrastructure: In case the clinical IT infrastructure was compromised, these attacks can also affect the ORs (Threat 1, Threat 2, Threat 5). Threat Source 2—External Storage Devices: External storage devices may introduce malware or other malicious software into the system (Threat 1). Threat Source 3—Diagnostic and Maintenance Tools: The diagnostic and maintenance interfaces could be compromised and provide access to update and configuration functionality. In the automotive context, interfaces such as On-Board Diagnostics (OBD) represent a gateway for attackers to gain access to vehicle systems and data. In the case of SOTA updates, this can be realized by malicious malware sent to the vehicle [95]. Threat Source 4—Over The Air (OTA)-Communication: Since physical access is no longer required for OTA communication such as Bluetooth, the attack surface increases and unauthorized access from outside the OR or hospital is possible (Threat 3, Threat 4). Threat Source 5—Backend Systems and Internet Connection: Devices in the OR may be connected to backend systems via the Internet, which could potentially provide an entry point for attackers (Threat 5). Threat Source 6—Connection to Compromised Devices: Other connected medical devices may already be compromised. Due to a shared attack surface, this compromise could spread to other devices (Threat 1, Threat 5, Threat 6). Manufacturers of devices that have not previously processed patient data may now also need to consider compliance with the HIPAA in case they are theoretically able to process the data in a SDC network. In addition, devices in this network will need to be compliant with HIPAA in order to establish a connection through SDC to the clinical IT infrastructure. ## 5. Heavens 2.0 Use Case With the purpose of demonstrating the security model HEAVENS 2.0 in the medical context, we use the requirements and use case as described in [19]. It can be described as an interoperable, flexible OR table for the run-time adaption of other medical devices. It is based on a mixed E/E architecture incorporating service-oriented and signal-based communication. For external communication, an SDC interface is considered with which network participants may control the OR table motion, as well as read its current joint positions. The subsequent threat landscape is described in Section 4. ## 5.1. Data Flow Diagram for Item Definition and Asset Identification Based on the architecture presented in [19], we performed a per-element STRIDE with a DFD (Figure 6). ECUs controlling joints of the OR table are handled generically here since their main task is the control of their joint positions. Furthermore, the reference positions can be set by a service technician via a service ECU. A movement for the joint can then be either invoked via remote control by the clinical staff or by an SDC participant such as an angiography system. The resulting positions are then communicated to consumers in the SDC network. ## 5.2. Threat and Damage Scenario Identification Since a high-risk results from the communication of erroneous positions causing collisions or asynchronous movements with other devices, we chose it as a damage scenario. There are various possible attack paths that could be used in this scenario (Figure 7). An attack path is a series of steps or actions that an attacker takes to exploit vulnerabilities in a system or network to gain unauthorized access. For example, an incorrect position could be communicated if the Communication Gateway (Com. GW) signal input is spoofed once the attacker has physical access to the system’s internal network (AP1). If the height of the OR table is incorrectly communicated to a connected device such as an angiography system, serious damage can occur to both systems or even the patient in the case of a collision (Figure 8). ## 5.3. Risk Assessment Based on the sub-parameters expertise, knowledge of item, window of opportunity, and equipment proposed by Lautenbach et al. [ 13], the Attack Feasibility Rating (AFR) can be determined (Table 5). In attack path AP1, for example, only some specialized equipment, as well as proficient expertise, is required to open the system and connect to the physical network. However, the window of opportunity is very small since the attacker needs to obtain access to the hospital and ORs, which is very restricted to the clinical staff. Furthermore, the knowledge of the item is not publicly known as manufacturers protect their development documents and the corresponding source code. To calculate the final AFR, the normalized sum for all sub-parameter values a can be calculated as follows [13]:[1]Asum=wxax+wkak+wwaw+weae3·(wx+wk+ww+we) This example with equal weighting (wx=wk=ww=we=1) and a range from 0 to 3 for each parameter a leads to an AFR of $42\%$ for attack path AP1. ## 5.4. Treatment Decision Table 6 sums up the risk values for the different threat scenarios based on the AFR and impact rating of the threat scenarios. The AFR of a threat scenario is based on the corresponding attack path with the highest AFR, and the impact rating is severe in all three cases as an erroneous position information of the OR table may lead to the serious harm of the patient (Section 5.2). Thus, each of the examined threat scenarios has a risk value of 5. Software measures for security threats should be assigned an appropriate software safety classification based on their risk level (Section 2.4). For instance, risk values from 1–2 can be treated as class A, 3–4 treated as class B, and 5 treated as class C. ## 5.5. Cybersecurity Goals The cybersecurity goals result in the following requirements (see also [19,67]):R1 Security properties (authenticity, integrity, confidentiality) must be ensured for network communication. R2 Patient harm resulting from the misuse of connectivity interfaces needs to always be avoided by the system. R3 The system must detect if the communicated joint positions are plausible. R4 The system must be able to detect unknown attacks. The inclusion of the expected connected devices and the purpose of the data that they consume are critical in determining risk. Clinical decisions or the behavior of other systems, such as the movement of an angiography system, may depend on the published data. Thus, it should always be determined in what context a medical device, and, in particular, its external interface, has been evaluated. This is partly in contrast to the Plug and Play (PnP) vision of projects such as SDC or MDPnP. Due to the long life cycles of medical devices in OR, it is imperative for future networked medical devices to adapt the design and documentation accordingly in order to PnP. ## 5.6. Relation to SFMEA The DFD (Figure 6) was used here as an input for a SFMEA. Table 7 lists exemplary risks resulting from failures in the system. Based on Table 3, a risk determination can be applied to the risks identified in Table 7. For example, R3 may happen due to data corruption in the service or signal of the joint positions provided by the joint control ECUs. A simple risk mitigation for this kind of failure is a second channel, such as a Cyclic Redundancy Check (CRC), that is sent along the payload. Failures during the parsing of incoming services/signals (P1) nevertheless cannot be detected by these measures, since the same value is sent over and over again during a movement. Thus, the positions must be checked for plausibility in the system context, e.g., a static position is not possible if the motors are moving. Safety measures can also serve as security measures and vice versa. Thus, the previous example for position plausibility can be used for checking abnormal behavior during an ongoing attack resulting in implausible joint positions. Anomaly detection, such as through the use of an anomaly-based IDS, can also be used to identify these kinds of anomalies caused by system failures, as demonstrated by Grimm et al. [ 96]. In the use case examined here, by supervising the plausibility of the joint positions, security and safety risks can be mitigated. This can be achieved, e.g., by using redundant sensors and/or sensor fusion, or by checking the plausibility of signals with static or machine learning checks as proposed by Weber et al. [ 97]. ## 6. Results and Future Work We have shown the suitability of the HEAVENS 2.0 TARA from the automotive domain for interoperable medical devices in the OR based on a threat landscape derived from known vulnerabilities and attacks in the medical field. In addition, this approach helps to meet the key requirement of threat modeling with appropriate risk analysis and management, which is required by the vast majority of medical device standards and regulations. This contributes mainly to the first step (identify) of the NIST cybersecurity framework. Since the attack path analysis is rated as quite time-consuming by [13], the corresponding TARA needs to be extensive before the product release in order to be able to react more quickly to security events. It should be noted that the risk analysis provided does not purport to be comprehensive as per ISO 14971 standards, as it primarily focuses on identifying and analyzing the risks associated with security-related software and how they relate to the safety-related risks present in the software, as well as the corresponding software safety classification. It does not cover risks arising, for example, from the unintended or improper use of the equipment as required by the standard. To follow the NIST cybersecurity framework, the next steps are the protection from and detection of cyberattacks (Section 2.2). However, complicated processes such as safety or security measures may result in even more harm to the patient due to decreases in availability and greater stress toward the clinical staff. Efforts to improve security in health IT systems following a breach can introduce changes in clinical work environments, potentially disrupting patient care processes and leading to a decreased quality of treatment [98]. Thus, protection cannot be achieved simply by applying known security measures from the IT sector, such as password authentication. Furthermore, as security threats evolve over time (Section 1) and medical equipment in the OR often have a life cycle of over 15 years [19], preparation is needed for new and unknown threats. A common approach for detecting unknown attacks is anomaly-based IDS [99], which is also a proposed measure by the FDA [10]. Therefore, both protection and detection measures must be properly coordinated. Furthermore, the detection of anomalies in particular can also contribute to safety in the event of system malfunction. The use of legacy devices in hospital networks can pose security risks due to their long life cycle. For modular devices such as OR tables, this also involves legacy modules used over several product generations [19]. According to the IEC 62304 standard [38], it is important for medical device manufacturers to implement risk management measures when using legacy software. This includes incorporating the software into the device’s overall architecture and evaluating and addressing any potential security hazards through appropriate risk control measures. In the automotive industry, a recent challenge connected to safety and security risks is SOTA updates and the development and testing thereof. Process models, data-sharing methods, and security measures need to be established adequately to ensure a safe and secure realization of these updates [62,95]. In the medical industry, it still needs to be evaluated how important the wireless updatability of devices will become. 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--- title: Risk Factors for Delayed-Onset Infection after Mandibular Wisdom Tooth Extractions authors: - Ryo Miyazaki - Shintaro Sukegawa - Ken Nakagawa - Fumi Nakai - Yasuhiro Nakai - Takanori Ishihama - Minoru Miyake journal: Healthcare year: 2023 pmcid: PMC10048475 doi: 10.3390/healthcare11060871 license: CC BY 4.0 --- # Risk Factors for Delayed-Onset Infection after Mandibular Wisdom Tooth Extractions ## Abstract Wisdom tooth extraction is one of the most commonly performed procedures by oral maxillofacial surgeons. Delayed-onset infection (DOI) is a rare complication of wisdom tooth extraction, and it occurs ~1–4 weeks after the extraction. In the present study, risk factors for DOI were investigated by retrospectively analyzing the cases of 1400 mandibular wisdom tooth extractions performed at Kagawa University Hospital from April 2015 to June 2022. Inclusion criteria were patients aged >15 years with a wisdom tooth extraction per our procedure. The exclusion criteria were patients with insufficient medical records, a >30-mm lesion around the wisdom tooth shown via X-ray, colonectomy, radiotherapy treatment of the mandible, the lack of panoramic images, and lesions other than a follicular cyst. The DOI incidence was $1.1\%$ (16 cases), and univariate analyses revealed that the development of DOI was significantly associated with the Winter classification ($$p \leq 0.003$$), position ($$p \leq 0.003$$), hypertension ($$p \leq 0.011$$), and hemostatic agent use ($$p \leq 0.004$$). A multivariate logistic regression analysis demonstrated that position (OR = B for A, 7.75; $$p \leq 0.0163$$), hypertension (OR = 7.60, $$p \leq 0.013$$), and hemostatic agent use (OR = 6.87, $$p \leq 0.0022$$) were significantly associated with DOI development. Hypertension, hemostatic use, and position were found to be key factors for DOI; long-term observation may thus be necessary for patients with these risk factors. ## 1. Introduction Wisdom tooth extraction is the most common surgery for oral surgeons, and it is necessary that surgeons minimalize the uncomfortable complications of these extractions. Potential intraoperative complications of wisdom tooth extraction are bleeding, damage to adjacent teeth, injury to surrounding tissue, displacement of teeth into adjacent spaces, fracture of the root, and maxillary tuberosity of the mandible. The common postoperative complications are pain, swelling, trismus, fever, and a dry socket, each of which can cause difficulty in chewing, speaking, and swallowing. Rare postoperative complications include postoperative infection and sensory alterations of the inferior nerve (IAN) or lingual nerve. Postoperative infection is one of the rare complications, and in the maxilla it is extremely rare [1]. However, postoperative infection occasionally occurs in the mandible, and such infections can involve abscess, pain, fever, swelling, and trismus [2,3]. Many studies have investigated frequent postoperative complications of wisdom tooth extractions such as nerve damage, a dry socket, and wound infection, but there are few reports related to delayed-onset infection (DOI) after wisdom tooth extraction. DOI is a rare complication that develops approx. 1–4 weeks after the extraction. Even though oral surgeons take precautions, such as prescribing antibiotics and advising patients about the importance of not smoking and maintaining good oral hygiene, DOIs still occur; the reported incidence of DOI has ranged from $0.5\%$ to $1.8\%$ [4,5,6,7]. The following factors were reported to be associated with an increased rate of total complications after wisdom tooth extraction: increased age, a positive medical history, and the position of the wisdom tooth to the inferior nerve [8]. Clinical studies have indicated the depth and the tilt of the tooth axis of the mandibular third molar as risk factors for local DOI [4], and another investigation demonstrated that the development of DOIs is related to the space distal to the second molar [9]. However, there are few reports regarding the identification of DOI risk factors from among comprehensive factors such as systemic conditions, local factors, and surgical factors related to mandibular third molar tooth extraction. The present study was conducted to identify clinical and radiological features associated with DOI. The null hypothesis of the study was that each factor was not related to the incidence of DOI. Few studies have evaluated the multivariate relationship between clinical features and DOI, and the present study thus sought to identify DOI risk factors through performing both univariate and multivariate analyses. ## 2.1. Study Design A retrospective clinical study of the incidence and risk factors for DOI in patients with extracted mandibular third molars at a single-center university hospital, Kagawa University Hospital, during the period from April 2015 to June 2022, was performed. ## 2.2. Ethics Statement This study was approved by the Institutional Review Committee of the Faculty of Medicine, Kagawa University (approval no. 2022-157, approved 25 November 2022), and was conducted in full accordance with the Declaration of Helsinki. Informed consent was obtained from all patients in this study. All data were anonymized before being analyzed. ## 2.3. Patient Selection The inclusion criteria for the patients were [1] age >15 years and [2] having undergone a wisdom tooth extraction following the described procedure. The following exclusion criteria were applied: [1] insufficient medical records, [2] a >30-mm lesion around the wisdom tooth shown via X-ray, [3] colonectomy (the removal of only crown of the tooth), [4] radiotherapy treatment of the mandible, [5] lack of panoramic images, and [6] a lesion other than a follicular cyst. With the use of these criteria, 1400 patients were enrolled in the study (Figure 1). ## 2.4. Surgical Procedure and Postoperative Management The protocol used for managing each patient’s general condition for wisdom tooth extraction was as follows. Blood pressure was measured first before surgery and a second time after conduction and infiltration anesthesia was administered. After the surgery, the patient’s blood pressure was measured again by a specialist nurse. Oxygen saturation and pulse rate were also monitored continuously. In addition to panoramic images, corn-beam computed tomography (CBCT) images were obtained from the patients with a wisdom tooth close to the IAN. All tooth extraction procedures were performed by residents or oral surgeons who had passed the Japanese Society of Oral and Maxillofacial Surgeons board examination for oral and maxillofacial surgery, under guidance by three experienced oral and maxillofacial surgeons (SS, FN, and MM). The surgeries were conducted with the patient under local anesthesia with 1:80,000 adrenalin with $2\%$ lidocaine (ORA Injection Dental Cartridge, GC Showayakuhin Corp., Tokyo, Japan), with or without intravenous sedation or under general anesthesia following the patient’s preference. All surgeries were performed with sterile instruments and materials. To close the wound, 3-0 silk sutures (Alfresa Pharma Corp., Osaka, Japan) were used. Primary closure was performed whenever possible, but secondary healing was performed if this was not possible. After the extraction, an antibiotic (amoxicillin 250 mg every 8 h for 2 days, or clarithromycin 200 mg every 12 h for 2 days for patients with penicillin allergy) and a nonsteroidal anti-inflammatory drug (loxoprofen sodium hydrate 60 mg every 6–8 h) or acetaminophen 500 mg every 6–8 h were prescribed. At ≥1 week after the extraction, the sutures were removed. At the suture removal, all patients were advised again to contact our department for any problems related to extraction, and in such cases, our consultation was conducted within a few days. ## 2.5. Outcome Variables The patients’ clinical data were examined by three oral surgeons (RM, SS, and KN) in a review of the patients’ panoramic X-ray images on the Picture Archiving and Communication System (PACS) and past electronic medical records. DOI was defined as inflammation around the wound with purulent discharge that occurred >1 week after the extraction [8,9]. ## 2.6. Predictive Variables The following surgical variables were examined: simultaneous left and right extraction, simultaneous maxilla and mandible extraction, and the surgeon’s specialist qualification (Japanese Society of Oral and Maxillofacial Surgeons). Wisdom tooth variables included the Winter classification, position, right or left side, the number of roots, and root canal treatment. The imaging evaluations included the use of computed tomography (CT) (CBCT and medical CT) and the imaging features of wisdom tooth lesions (follicular cyst and radicular cyst). The following data were obtained: height, weight, body mass index (BMI), smoking habit, alcohol consumption, hypertension, diabetes, bisphosphonate medications, corticosteroid therapy, contraceptives medications, hemostatic agent, and perioperative blood pressure. Hypertension was defined based on a physician’s diagnosis. Diabetes was defined as >$6.5\%$ HbA1c [10]. ## 2.7. Statistical Analysis In this study, data were recorded in an electronic database using Microsoft Excel. For the statistical analyses, the digital database used was JMP ver. 14.2.0 for Macintosh (SAS, Cary, NC, USA). Categorical variables were presented as numbers and percentages, while continuous variables were presented as mean and standard deviations. For the comparisons of pairs of groups, the chi-square test or Fisher’s exact test was used for categorical variables, and the Mann–Whitney U test was used for continuous variables. Adjusted odds ratios (ORs) to control the simultaneous effects of multiple covariates were obtained. Statistical significance was defined at $p \leq 0.05.$ ## 3.1. Univariate Analyses A total of 1400 lower third molars were extracted during the study period. The incidence of DOI was $1.1\%$ at 16 sites. Table 1 summarizes the results of the statistical analyses. The development of DOI was significantly associated with the Winter classification ($$p \leq 0.003$$), position ($$p \leq 0.003$$), hypertension ($$p \leq 0.011$$), and use of a hemostatic agent ($$p \leq 0.004$$). ## 3.2. Multivariate Logistic Regression Model Results A multivariate logistic regression model for the occurrence of DOI was next performed. The selected items were significant variables in the bivariate analysis and variables with higher correlation coefficients (hemostatic agent, hypertension, position, Winter class), sex, and age. The results of the multivariate logistic regression analysis demonstrated that hemostatic agent use, hypertension, and position were significantly associated with the development of DOI. Position (OR = B for A, 7.75; $$p \leq 0.0163$$) and hypertension (OR = 7.60, $$p \leq 0.013$$) had high ORs for the extracted variables. The use of a hemostatic agent (OR = 6.87, $$p \leq 0.0022$$) was also significant. ## 4. Discussion Although DOI is a rare complication of wisdom tooth extractions (which are one of the most frequent surgeries performed by oral and maxillofacial surgeons), a DOI can result in severe physical and emotional burdens. Our present retrospective analyses identified risk factors for the development of a DOI after the extraction of a wisdom tooth. The incidence of DOI in previous investigations ranged from $1.5\%$ to $3.7\%$ [1,6,10,11,12,13], and our finding of a $1.1\%$ incidence is similar to these values. In the present patient series ($$n = 1400$$), the tooth extraction procedures were performed by surgeons with different levels of experience. A surgeon’s lack of experience was reported to be a major factor associated with postoperative complications [14]. The univariate analyses conducted herein detected no significant difference in the DOI rate between the extractions performed by the residents and those performed by the specialists. A reason contributing to this result might be that all extractions were performed under the guidance of highly experienced oral and maxillofacial surgeons in our department. In addition, 3-0 silk was used as the suture instead of absorbent thread, for medical and economic reasons. Our DOI result is similar to those of previous reports; however, the difference in operators and the use of the 3-0 silk suture did not seem to affect the infection rate. The most common age of onset for a DOI is the teens to early twenties [11,15], and DOI was reported to be the most common secondary infection in a group of patients between 12 and 24 years old [7,15]. The occurrence of DOI has been described as most frequent at 1 month post extraction [1,6,10,14]. In the present patient series, the DOIs occurred at an average of 29.1 postoperative days. Food impaction was suggested to be a risk factor for DOI [4,6]; after wound healing, it might be more difficult for food debris to escape from the socket, and this is more likely to happen at ~1 month after the surgery. It is therefore important to inform patients about the possibility of a DOI occurring several weeks after their extractions. A younger age, total tissue coverage, deeper impaction, lower Nolla stage, mesioangular direction, and full bone coverage have been suggested as DOI risk factors [9,11], but the precise list of DOI risk factors has not been established. The results of our present univariate analyses revealed that the Winter class ($p \leq 0.01$), position ($p \leq 0.01$), hypertension ($p \leq 0.01$), and use of a hemostatic agent ($p \leq 0.01$) were significantly associated with DOI, and the multivariate logistic regression model identified hemostatic agent use, hypertension, and position as significant factors for the development of a DOI. Aspects of the patient’s physical status, such as diabetes, the use of a bisphosphonate, corticosteroid, or contraceptive, and the presence of a radicular cyst or root canal treatment were not significantly associated with DOI. These variables thus do not seem to be key factors for DOIs. Gender, the surgeon’s experience, the patient’s medical condition, smoking, and the use of an oral contraceptive have been reported to be related to postoperative complications [16] The logistic regression analysis in one of our earlier investigations demonstrated that the simultaneous extraction of left and right mandibular wisdom teeth is a risk factor for DOI [1]; the reason is thought to be that the simultaneous extraction of the mandibular wisdom tooth on both sides induces swelling and trismus and leads to an unsanitary condition in the patient’s mouth. The present study’s univariate analyses detected no significant difference in the DOI rate between the cases with simultaneous left and right extraction and those with simultaneous maxilla and mandible extraction. The association of the wisdom tooth’s position with the development of a DOI that the present study observed herein is consistent with past reports. It is thought that the position is related to the amount of bone coverage, and that a deeper wisdom tooth needs a more extensive alveolar ostectomy, greater tooth sectioning, and a longer operation time. In addition, the restricted space causes difficulty in self-cleaning and [17]. The proper surgical technique to reduce the amount of ostectomy is thus necessary. The present study reported that intraoperative hemostatic treatment is significantly associated with the development of infections, including DOI [1]. In our department, oxidized cellulose is available as a hemostasis agent. There are few reports about susceptibility to infection in relation to the use of oxidized cellulose, which is reported to take 2 weeks to absorb [18]. Generally, age, gender, the site of extraction, tobacco use, oral contraceptive use, anesthesia, and the surgeon’s experience are frequently cited risk factors for wisdom tooth extraction complications [8]. Possible explanations for the increased incidence of DOIs caused by hemostasis agent use could include selection bias (i.e., more difficult extraction or extractions with preoperative infection). In addition, it is hypothesized that bacteria can become attached to the remaining hemostasis agent, causing a DOI. This possibility indicates that only the smallest necessary quantity of a hemostasis agent should be used, and any excess should be removed once the hemostatic effect has been achieved. Hypertension was highly correlated with DOI in our present analyses, whereas the patient’s perioperative blood pressure was not. Our present results provide the first clinical data to be reported regarding DOIs, and they are significant. Generally, hypertension is considered a risk factor for tooth loss due to periodontal disease [19]. It has been speculated that increased blood pressure is likely to cause both the spread of inflammation and secondary damage to the vascular endothelium [20]. These factors might affect the development of a DOI, but the exact mechanism of DOI development remains unknown. However, the identification of hypertension and hemostasis agent use as risk factors is a new discovery; new criteria and long-term observation may thus be necessary. Antibiotics are generally prescribed to prevent postoperative infections, and patients with immunodeficiency in particular are prescribed more antibiotics [21]. Unfortunately, antibiotic resistance has become a serious public health issue worldwide [22]. Even short-duration or single amoxicillin administration causes a reduction in the number of strains that are susceptible to amoxicillin [23,24]. The optimal timing of antibiotic administration (preoperative, postoperative, or both) is not established [25,26]. The current best evidence described in a review suggests that antibiotic use reduces surgical site infections but not by enough to overcome the concerns about adverse effects and antimicrobial resistance, or to justify the routine use of antibiotics [27]. Short-term intraoral amoxicillin administration was applied in the present patients but it did not prevent the occurrence of DOI. Further research is necessary to determine the proper perioperative administration of antibiotics in wisdom tooth extractions. The treatment for DOI is not well-defined. An oral antibiotic is commonly administered for a DOI. Fusobacterium, Prevotella, Bacteroides, and Peptostreptococcus have been identified in DOIs, and the antibiotic clindamycin has been the most effective for DOIs, followed by metronidazole and amoxicillin/clavulanate. Amoxicillin alone is not sufficiently effective for Fusobacterium or Prevotella [28]. When antibiotic treatment is not successful, surgical debridement of the extraction site is recommended [29]. Removal of the granulation tissue from the socket, debridement of bone particles, and removal of any foreign matter are thought to be essential for DOI treatment [29]. In this study, antibiotic treatment was performed in all cases. For the patients with a mild DOI, amoxicillin or sitafloxacin was used. For the patients with a severe DOI, ceftriaxone or sulbuctam/ampicillin was administered intravenously. Four patients underwent surgical debridement. After the treatment, all 16 of the cases of DOI healed well. As in previous reports, the use of an antibiotic and then a surgical procedure, if necessary, seem to be the most suitable treatments for DOIs. There are some study limitations to consider. The patient population was retrospectively drawn from a single hospital. There was a bias in the degree of difficulty of the tooth extraction, which may have affected the surgical method selected by the oral surgeons. Even though in the present study all surgeons followed our surgical protocol to standardize the surgical procedures, the levels of experiences among the providers were different. Besides, CBCT was used for not all the cases. We would like to conduct further research through prospective studies. Secondly, although another investigation indicated that the incidence of infection was not significantly different between cases with secondary closure versus primary closure [30], our suture protocol was not established. In addition, whether the patients with DOIs came back to our department after their sutures were removed depended on the patients and their symptoms. It is thus necessary to take this uncertainty into account in future studies. ## 5. Conclusions The results of this retrospective study of 1400 cases demonstrated that hypertension, the position of the wisdom tooth, and the use of a hemostasis agent were significantly associated with the development of a DOI. To our best knowledge, the present study is the first to report that the presence of hypertension affects the incidence of DOI. Especially for patients with any of these three factors, long-term observation and professional oral care might be important after wisdom tooth extraction. ## References 1. Sukegawa S., Yokota K., Kanno T., Manabe Y., Sukegawa-Takahashi Y., Masui M., Furuki Y.. What are the risk factors for postoperative infections of third molar extraction surgery: A retrospective clinical study?. Med. Oral Patol. 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--- title: 'Different Approaches to Appraising Systematic Reviews of Digital Interventions for Physical Activity Promotion Using AMSTAR 2 Tool: Cross-Sectional Study' authors: - Karina Karolina De Santis - Katja Matthias journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048476 doi: 10.3390/ijerph20064689 license: CC BY 4.0 --- # Different Approaches to Appraising Systematic Reviews of Digital Interventions for Physical Activity Promotion Using AMSTAR 2 Tool: Cross-Sectional Study ## Abstract High-quality systematic reviews (SRs) can strengthen the evidence base for prevention and health promotion. A 16-item AMSTAR 2 tool allows the appraisal of SRs by deriving a confidence rating in their results. In this cross-sectional study, we aimed to assess and compare two approaches to appraising 30 SRs of digital interventions for physical activity (PA) promotion using AMSTAR 2. Approach 1 (appraisals with $\frac{2}{16}$ items) was used to identify SRs with critically low confidence ratings. Approach 2 (appraisals with all 16 items) was used [1] to derive the confidence ratings, [2] to identify SR strengths and weaknesses, and [3] to compare SR strengths among subgroups of SRs. The appraisal outcomes were summarized and compared using descriptive statistics. Approach 1 was quick (mean of 5 min/SR) at identifying SRs with critically low confidence ratings. Approach 2 was slower (mean of 20 min/SR), but allowed to identify SR strengths and weaknesses. Approach 2 showed that confidence ratings were low to critically low in $\frac{29}{30}$ SRs. More strengths were identified in SRs with review protocols relative to SRs without review protocols and in newer SRs (published after AMSTAR 2 release) relative to older SRs. Only two items on AMSTAR 2 can quickly identify SRs with critical weaknesses. Although most SRs received low to critically low confidence ratings, SRs with review protocols and newer SRs tended to have more strengths. Future SRs require review protocols and better adherence to reporting guidelines to improve the confidence in their results. ## 1. Introduction High-quality systematic reviews (SRs) can strengthen the evidence base for prevention and health promotion. Although the number of published SRs has increased rapidly over the last 30 years [1], many such SRs of health interventions have weaknesses in their quality [2,3,4] and, thus, may have limited practical use for policy development and health decision-making. A Measurement Tool to Assess Systematic Reviews, Version 2 (AMSTAR 2) [5] is a tool for appraising SRs of health interventions that was published in late 2017. AMSTAR 2 consists of a questionnaire with 16 items and a comprehensive rating guidance document [5]. The appraisals are conducted by rating 16 aspects of SRs, including research question and review protocol, literature search, study selection and data management, data synthesis, and assessment of potential biases and conflicts of interest. The item ratings are used to derive the overall confidence rating in the results of the SR (critically low, low, moderate or high) [5]. While AMSTAR 2 is an open-access tool with acceptable psychometric properties [5,6,7], the rating time for one SR is approximately 15 to 32 min for experienced users [5,7,8] and could be even longer for less experienced users. Thus, alternative approaches to using AMSTAR 2 for SR appraisals should be tested to potentially reduce the rating time. In this study, we aimed to assess and compare two approaches to appraising SRs of interventions in public health using AMSTAR 2. Approach 1 (appraisals with $\frac{2}{16}$ items) was used to identify SRs with critically low confidence ratings. Approach 2 (appraisals with all 16 items) was used [1] to derive the confidence ratings, [2] to identify SR strengths and weaknesses, and [3] to compare SR strengths among subgroups of SRs. ## 2.1. Protocol and Reporting This study was performed within our scoping review [9] with a prospectively registered protocol [10]. Except for additional sensitivity analysis, there were no changes between this study and the protocol [10]. The study adheres to ‘The Strengthening the Reporting of Observational Studies in Epidemiology’ (STROBE) guidelines [11]. The STROBE checklist is reported in Supplementary Materials, Table S1. ## 2.2. Design and Setting This study used a cross-sectional design to assess appraisals of SRs focusing on evaluation of digital interventions for physical activity (PA) promotion that were published in peer-reviewed journals through March 2021. ## 2.3. Data Sources Data sources for this study were SRs included in our scoping review [9,10]. These SRs were selected out of 304 reviews of any type that were identified in electronic searches of international databases (Medline, PsycINFO and CINAHL from inception through 19 March 2021) and in bibliographic searches of the included reviews [10]. The inclusion criteria for this study are based on the PICOS framework: [1] P (population): humans, any age or clinical status (i.e., healthy or clinical samples), [2] I (intervention): any digital intervention for PA promotion (i.e., intervention supported by digital tools, such as smartphone apps, activity trackers, or health websites), [3] C (comparison): any other intervention or no intervention, [4] O (outcome): evaluation of any PA promotion outcome (e.g., general fitness or mobility), [5] S (study design): SR. To reduce selection bias, SRs were independently selected by two researchers and the final consensus on inclusion was reached by discussion. Out of 304 reviews, 30 SRs met the inclusion criteria for this study. The list of included SRs is reported in our scoping review [9]. ## 2.4. Procedure To reduce rating bias, SRs were independently appraised by two researchers using AMSTAR 2 [5] and the final consensus on ratings was reached by discussion. AMSTAR 2 includes 16 items (Table 1) that can be rated “yes” (fulfilled items) or “no” (not fulfilled items). In addition, $\frac{5}{16}$ items (items 2, 4, 7, 8 and 9) can be rated “partial yes” (if they are partially fulfilled) and $\frac{3}{16}$ items (items 11, 12 and 15) can be rated “no meta-analysis conducted” (Table 1). The appraisal outcome is an overall confidence rating in the results of the SR that is derived based on ratings on $\frac{7}{16}$ critical items (2, 4, 7, 9, 11, 13 and 15) and $\frac{9}{16}$ non-critical items [5]. The confidence ratings range from “high” (no or one weakness on non-critical items), “moderate” (more than one weakness on non-critical items), “low” (one weakness on critical items) to “critically low” (more than one weakness on critical items) [5]. The appraisal procedure was performed using two approaches. Based on Approach 1, all 30 SRs were appraised with $\frac{2}{16}$ items on AMSTAR 2 (item 2: presence of a review protocol and item 7: presence of a list of excluded studies) to identify SRs with critically low confidence ratings. Both items are critical items for deriving the overall confidence ratings (see Table 1 for the list of critical and non-critical items). These two items were often not fulfilled in SRs of health interventions [2,3,4,12] and they were selected using a fast and frugal decision tree for the critical appraisal of SRs [13]. Based on Approach 2, all 30 SRs were appraised with all 16 AMSTAR 2 items to derive the confidence ratings and to identify SR strengths and weaknesses. The appraisal outcomes (confidence ratings) for all 30 SRs were derived according to the AMSTAR 2 guidelines [5]. Item ratings were used to identify strengths and weaknesses in all 30 SRs. SR strengths were classified as fulfilled AMSTAR 2 items (i.e., items rated “yes” or “partial yes”) and SR weaknesses were classified as not fulfilled AMSTAR 2 items (i.e., items rated “no”). ## 2.5. Variables All data were coded into a self-developed spreadsheet in Microsoft-Excel 10 (Supplementary Materials, Table S2). The coded variables included SR characteristics (first author and publication year), AMSTAR 2 appraisal outcomes (confidence ratings and item ratings) and rating time (in min/SR) for Approach 1 and Approach 2. ## 2.6. Data Analysis All data were summarized using descriptive statistics (frequencies or means with standard deviations) and a descriptive data analysis was planned [10]. Data analysis was performed in three steps. First, the appraisal outcomes (confidence ratings and rating time) were descriptively compared between Approach 1 and Approach 2. Second, SR strengths and weaknesses were identified and descriptively summarized. Third, a sensitivity analysis was planned to compare SR strengths in SRs with better (high and moderate) confidence ratings relative to SRs with worse (low and critically low) confidence ratings. SR strengths were expressed as percentage scores for the sensitivity analysis. According to a procedure described by others [2], item ratings for each SR were coded as 0 (“no”), 0.5 (“partial yes”) or 1 (“yes”), summed, divided by 16 items (for SRs with meta-analysis) or 13 items (for SRs without meta-analysis) and expressed as percentage scores for each SR. Mean difference scores and $95\%$ confidence intervals ($95\%$ CI) for independent groups were used to compare SR strengths between groups. It was assumed that statistically significant difference between groups exists if the $95\%$ CI does not include zero. All calculations were performed in Microsoft Excel 10 (Supplementary Materials, Table S2). ## 3.1. SR Characteristics The 30 SRs were published between 2007 and 2021 (Supplementary Materials, Table S2). Among all SRs, $\frac{21}{30}$ were published after AMSTAR 2 release between 2018 and 2021 and $\frac{11}{30}$ had review protocols. ## 3.2. Outcomes of SR Appraisal Approaches Approach 1 (SR appraisals with two items on AMSTAR 2) was quick (mean of 5 min/SR) at identifying SRs with critically low confidence ratings (Table 2). Among all SRs, $\frac{19}{30}$ SRs received critically low confidence ratings because they did not fulfill AMSTAR 2 item 2 (i.e., did not have a review protocol) and item 7 (i.e., did not report a list of excluded studies). Further $\frac{11}{30}$ SRs did not receive a confidence rating, because they fulfilled one or both items 2 and 7. In this case, all 16 items on AMSTAR 2 need to be rated to derive the confidence rating. Approach 2 (SR appraisals with 16 items on AMSTAR 2) was slower (mean of 20 min/SR) than Approach 1, but allowed to perform the full appraisals and to identify SR strengths and weaknesses (Table 2). Approach 2 showed that confidence ratings were low to critically low in $\frac{29}{30}$ SRs and only $\frac{1}{30}$ SRs received moderate confidence rating. There were no high confidence ratings. ## 3.3. SR Strengths and Weaknesses Approach 2 (SR appraisals with 16 items on AMSTAR 2) was used to identify SR strengths and weaknesses in 30 SRs. Each SRs had between 0 and 13 strengths based on items rated “yes” or “partial yes” and between 2 and 11 weaknesses based on items rated “no” (Table 2). Among the weaknesses, there were between 0 and 5 critical weaknesses based on “no” ratings on critical items (Table 2). The inspection of item ratings in all 30 SRs revealed that $\frac{9}{16}$ items were rated “yes” or “partial yes” in most SRs (i.e., in more than $50\%$ of 30 SRs) and $\frac{7}{16}$ items were rated “no” in most SRs (Figure 1). Consequently, nine SR strengths based on $\frac{9}{16}$ fulfilled items and seven SR weaknesses based on $\frac{7}{16}$ not fulfilled items were identified. The nine SR strengths among the 30 SRs were:Research questions and inclusion criteria were stated based on PICO (item 1);Comprehensive literature search was performed (item 4);Studies were selected in duplicate (item 5);Studies were coded in duplicate (item 6);Study details were reported (item 8);Risk of bias assessment was performed (item 9);Appropriate methods were used for meta-analysis (item 11);Heterogeneity in results was discussed (item 14);Potential sources of conflict of interest in review were reported (item 16). The seven SR weaknesses among the 30 SRs were:Review protocol was absent (item 2);Reasons for selecting study designs were not explained (item 3);List of excluded studies was not reported (item 7);Sources of funding for primary studies were not reported (item 10);Risk of bias impact on the results of meta-analysis was not assessed (item 12)Risk of bias was not discussed (item 13).Publication bias was not assessed (item 15). ## 3.4. Sensitivity Analysis of SR Strengths We were unable to perform the planned sensitivity analysis to compare SR strengths in SRs with better (high and moderate) confidence ratings relative to SRs with worse (low and critically low) confidence ratings due to too few SRs with better ratings ($\frac{0}{30}$ SRs with high confidence rating and $\frac{1}{30}$ SRs with moderate confidence rating; Table 2). Instead, we performed another sensitivity analysis based on available data to compare SR strengths in SRs with review protocols relative to SRs without review protocols and in newer SRs (i.e., published after AMSTAR 2 release between 2018 and 2021) relative to older SRs (i.e., published before 2018). More strengths (i.e., fulfilled items rated “yes” and “partial yes”) were identified in SRs with review protocols and in newer SRs (Table 3). Specifically, there were statistically significantly more SR strengths in SRs with review protocols relative to SRs without review protocols. There was also a non-significant trend toward more SR strengths in newer SRs relative to older SRs (Table 3). In addition, less critical weaknesses (i.e., critical items rated “no”) were identified in SRs with review protocols relative to SRs without review protocols, while the same number of critical weaknesses was identified in older SRs relative to newer SRs (Table 3). ## 4. Discussion This study assessed and compared two approaches to appraising SRs of interventions in public health using AMSTAR 2. Approach 1 (appraisals with 2 items) was quick (mean of 5 min/SR) at identifying SRs with critically low confidence ratings. Approach 2 (appraisals with 16 items) was slower (mean of 20 min/SR), but allowed us to perform the full appraisals and to identify SR strengths and weaknesses. Approach 2 showed that confidence ratings were low to critically low in $\frac{29}{30}$ SRs. More strengths were identified in SRs with review protocols relative to SRs without review protocols and in newer SRs (published after AMSTAR 2 release) relative to older SRs. This is the first study to assess and compare different approaches to appraising SRs of health interventions using different combinations of items on AMSTAR 2. Both approaches to appraising SRs with AMSTAR 2 were useful for different purposes. The appraisal approach with two items (critical items 2 and 7) was time efficient at identifying SRs with the lowest confidence ratings, although identification of SR strengths and weaknesses was not possible using this approach. The appraisals with these two items could be performed by less experienced users of AMSTAR 2 because presence of a review protocol and a list of excluded studies can be identified relatively fast and does not require as much methodological expertise as some other items on AMSTAR 2 (e.g., item 11 that requires a judgement of methods used in a meta-analysis). Since decision makers find it difficult to select appropriate SRs for their work [14], the appraisal approach with two critical items could assist with SR classification and selection for further work. For example, such an approach can be used when large numbers of SRs on a similar topic are available for their potential application in health decision-making. In this case, a decision rule could be developed to quickly identify and exclude SRs with critically low confidence ratings from the pool of potentially relevant SRs. This can be achieved by appraising SRs with two critical items only, because critically low confidence ratings based on two critical items would not improve if all 16 items were used for appraisals. Although items 2 and 7 are often not fulfilled in SRs of health interventions [2,3,4,12], combinations of other two critical items on AMSTAR 2 could be used to quickly identify SRs with critically low ratings (see Table 1 for the list of critical and non-critical items). While the appraisal approach with all 16 AMSTAR 2 items took longer, it allowed to perform full appraisals and to identify SR strengths and weaknesses. Our finding that most SRs of digital interventions for PA promotion have low to critically low confidence ratings has also been shown in SRs of other health interventions [2,3,4,12,15]. Two hypotheses were proposed for such poor ratings of SRs of health interventions: [1] the AMSTAR 2 tool is too conservative and tends to overestimate SR weaknesses and [2] the quality of SRs of health interventions is poor [4]. While this study was not designed to test these hypotheses, our ratings show that newer SRs published after AMSTAR 2 release tend to have more strengths than older SRs. This could be due to SR authors using AMSTAR 2 as a checklist for SR production and writing, as suggested before [16]. Furthermore, there could also be a higher awareness of the availability of reporting guidelines, such as PRISMA [17] and its newest update PRISMA2020 [18]. Despite the availability of reporting guidelines, the poor confidence ratings in this study suggest that SR authors do not adequately adhere to such guidelines. Our results also confirm the finding that SRs with review protocols have more strengths than SRs without review protocols [19,20,21,22], presumably due to better planning and preparation for SR production. The SRs in our study had several weaknesses. Two items (a list of excluded studies, item 7, and sources of funding for primary studies, item 10) were especially poorly addressed (fulfilled in less than $10\%$ of SRs). These results are in line with other studies [3,23]. Item 7 is particularly important for replicability of SRs and detecting any biases in study selection. Item 10 is required to assess any risk of bias in primary studies due to funding. A Cochrane review found that the results of industry-sponsored primary studies sometimes favor sponsored products, leading to more favorable efficacy results and conclusions [24]. Since the results and conclusions in SRs are based on primary studies, the information about funding should be assessed on the primary study level. Effective collaboration between industry and academic research is especially required in the field of our SRs of digital interventions for PA promotion. In addition to item 7 and 10, other weaknesses identified in this study suggest that replicability of some SRs was low and the risk of other biases was insufficiently addressed. Specifically, more than $50\%$ of SRs in this study did not have a review protocol (item 2), did not provide reasons for the choice of study designs included in the SR (item 3), and did not assess or discuss the impact of potential sources of biases on SR outcomes (items 12, 13 and 15). Focus on the content of these items on AMSTAR 2 is required to improve the replicability of SRs and to transparently assess any potential biases that could affect SR outcomes. Appraisal of SRs of digital interventions for PA promotion is important before such SRs can be used for practical purposes, such as policy development or health decision-making. In general, it is well known that regular PA promotes and supports both mental and physical health. However, a study that incorporated data from 358 population-based surveys in 168 countries found that the global age-standardized prevalence of inadequate PA was $27.5\%$ in 2016 [25]. Behavior change related to PA could be supported by digital interventions involving modern technologies, such as apps or wearables [26,27]. However, it is unclear whether digital interventions to promote PA and healthy lifestyle work better alone or as a complement to in-person interventions [9] and whether they work in different populations based on age or health status [28]. There is also a need to identify factors that might increase the uptake of digital interventions for PA and improve participation in such interventions to prolong their effectiveness. The evidence addressing these issues is required from methodologically sound SRs to comprehensively and objectively assess and summarize the current state of knowledge in this rapidly developing field. AMSTAR 2 is a tool that can be used to identify such methodologically sound SRs. We show that an appraisal procedure can be shortened by first using a selection of critical items to quickly identify SRs with critical weaknesses that may not be considered for further practical use. In the second stage, all SRs without critical weaknesses on the selected critical items may be fully appraised with all 16 items on AMSTAR 2 to identify SR strengths and weaknesses. Based on such full appraisal outcomes, the SRs can be considered for further practical use. This study had several methodological strengths. First, the risk of any biases was reduced because the study was prospectively registered [10] and two researchers selected and appraised all SRs. Second, we tested an alternative approach to SR appraisals on AMSTAR 2 and show that only two (critical) items can quickly identify SRs with the lowest confidence ratings. Third, despite poor confidence ratings in most SRs, our sensitivity analysis shows that SRs with review protocols and newer SRs tend to have more strengths. These results should encourage future SR authors to prospectively register review protocols and to adhere to reporting guidelines, including SR aspects addressed in AMSTAR 2, to improve the replicability and, thus, the overall confidence in SRs of health interventions. There were several limitations in this study. First, we included a small sample of SRs in one field of public health. Thus, the results of this study may not be generalizable to SRs in other fields of public health and beyond. Second, we included SRs published up to 2021. This sample was selected from our scoping review [9,10] and new literature search was beyond the scope of this study. Third, due to lack of high confidence ratings, we were unable to perform a planned sensitivity analysis to compare SR strengths in SRs with better (high and moderate) confidence ratings relative to SRs with worse (low and critically low) confidence ratings. Fourth, we compared SR strengths based on two factors (presence or absence of review protocol and SR age relative to AMSTAR 2 release date). There are likely to be more predictors of SR strengths which were not analyzed in this study. ## 5. Conclusions This study assessed and compared two approaches to appraising SRs of interventions in public health using AMSTAR 2. Only two items on AMSTAR 2 can quickly identify SRs with critical weaknesses. Although most SRs received low to critically low confidence ratings, SRs with review protocols and newer SRs tended to have more strengths. Future SRs require review protocols and better adherence to reporting guidelines to improve the confidence in their results. ## References 1. 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--- title: Whole-Genome Sequencing Data Reveal New Loci Affecting Milk Production in German Black Pied Cattle (DSN) authors: - Paula Korkuć - Guilherme B. Neumann - Deike Hesse - Danny Arends - Monika Reißmann - Siham Rahmatalla - Katharina May - Manuel J. Wolf - Sven König - Gudrun A. Brockmann journal: Genes year: 2023 pmcid: PMC10048491 doi: 10.3390/genes14030581 license: CC BY 4.0 --- # Whole-Genome Sequencing Data Reveal New Loci Affecting Milk Production in German Black Pied Cattle (DSN) ## Abstract German Black Pied (DSN) is considered an ancestral population of the Holstein breed. The goal of the current study was to fine-map genomic loci for milk production traits and to provide sequence variants for selection. We studied genome-wide associations for milk-production traits in 2160 DSN cows. Using 11.7 million variants from whole-genome sequencing of 304 representative DSN cattle, we identified 1980 associated variants (−log10(p) ≥ 7.1) in 13 genomic loci on 9 chromosomes. The highest significance was found for the MGST1 region affecting milk fat content (−log10(p) = 11.93, MAF = 0.23, substitution effect of the minor allele (ßMA) = −$0.151\%$). Different from Holstein, DGAT1 was fixed (0.97) for the alanine protein variant for high milk and protein yield. A key gene affecting protein content was CSN1S1 (−log10(p) = 8.47, MAF = 049, ßMA = −$0.055\%$) and the GNG2 region (−log10(p) = 10.48, MAF = 0.34, ßMA = $0.054\%$). Additionally, we suggest the importance of FGF12 for protein and fat yield, HTR3C for milk yield, TLE4 for milk and protein yield, and TNKS for milk and fat yield. Selection for favored alleles can improve milk yield and composition. With respect to maintaining the dual-purpose type of DSN, unfavored linkage to genes affecting muscularity has to be investigated carefully, before the milk-associated variants can be applied for selection in the small population. ## 1. Introduction The dual-purpose cattle breed German Black Pied (DSN, German: “Deutsches Schwarzbuntes Niederungsrind”) is an endangered breed from Germany, with currently around 2500 herdbook cows [1]. Since DSN cows produce about 2500 kg less milk compared to Holstein cows, they were almost entirely replaced by Holstein in the 1960s and 1970s [2]. The DSN breed is maintained as a genetic reserve due to its beneficial properties, such as high milk fat and protein content ($4.3\%$ and $3.7\%$, respectively), their relation to Holstein as an ancestral breed [3], and their high genetic diversity despite the small population size [4]. Currently, DSN breeders are subsidized by the German government to compensate for the low milk yield. For maintaining DSN long-term, it is crucial to improve its milk production while keeping its dual-purpose character and breed-specific characteristics. Genetic information for this small endangered population is needed to support its conservation and further development. Genome-wide association studies (GWASs) have been performed for milk production [5], clinical mastitis [6], endoparasite resistance and fertility [7]. These GWASs used genotypes from the Illumina BovineSNP50 BeadChip that contains around 34 K informative SNPs for DSN. All commercially available bovine SNP chips contain SNPs that are biased towards cosmopolitan breeds. As those breeds and not the local breeds contributed to the chip design, the commercial chips do not accurately depict specific mutations and linkage disequilibria of local breeds such as DSN [8]. As a consequence, only a comparably small portion of SNPs (approximately $63\%$) on commercial SNP chips is informative for DSN [9]. More importantly, potentially functional causal mutations segregating the DSN breed are almost entirely absent. Furthermore, most SNP chips hardly target SNP-rich regions, since the probe design interferes with neighboring SNPs [10]. Therefore, a DSN-specific SNP chip was developed, which contains 182 K sequence variants that are highly informative, in particular for functional SNPs and SNPs unique to DSN [9]. Using high-density sequence information allows a fine-grained view of the whole genome, detection of associations with DSN-specific variants, and, in particular, the identification of variation in associated genes. Only the most recent studies in DSN included genotypes obtained from the customized DSN200K SNP chip and imputed whole-genome sequencing (WGS) data [11,12]. In the previous GWAS for milk production traits with 1490 DSN cows and 34 K informative SNPs of the Illumina BovineSNP50 BeadChip, we identified 41 significant SNPs on 17 chromosomes [5]. In the current study, we expanded our previous GWAS using a larger population and a saturated marker density of 11.7 million variants, as genotypes from the DSN200K SNP chip and the Illumina BovineSNP50 BeadChip were imputed to WGS level using the WGS data of 304 DSN animals [13]. We expected a higher significance and refined resolution of previously associated loci and, in particular, the identification of additional loci. The additional loci would result from the information on whole-genome variants which were not considered on the commercial SNP chip used in the previous study. ## 2.1. Population We obtained genotypic and phenotypic data of 2598 DSN cows representing almost the whole DSN population. For the GWAS, we required that at least 20 cows per farm, per sire and per birth year were available in order to control environmental influences. Thus, analyses were performed with 2160 cows which were kept on eight farms in Germany. The cows were born between 2007 and 2017 and descended from 37 sires. Pedigree data were obtained from the breeding company RBB Rinderproduktion Berlin-Brandenburg GmbH. ## 2.2. Genotypes Genotypes of the 2160 DSN cows with available phenotypic data for GWAS were available from Illumina BovineSNP50 chips (1501 DSN cows) [5], custom DSN200K SNP chips (478 DSN cows) [9], and whole-genome sequencing (181 DSN cows) [9]. The DSN200K chip was designed with Axiom® myDesign TG Array technology of Thermo Fisher Scientific Inc. (Waltham, MA, USA) and targeted 182 K sequence variants. For imputation, WGS data were available for 304 DSN cattle (181 cows which were used for GWAS, 76 additional cows and 47 bulls) [9]. Genotypes from Illumina BovineSNP50 or DSN200K SNP chips were imputed directly to the positions of the WGS data with Beagle v5.1 [13,14]. With regard to the size of the reference panel for imputation, we tested how much additional variation in the DSN population was found by subsequently adding the WGS data of additional animals (Figure 1). This followed a saturation curve. After reaching a certain number of animals, adding more animals to the reference panel did not significantly increase the number of newly identified variations. Based on this, we believe that having 304 DSN individuals in the reference panel for imputation was sufficient to capture the majority of variants in the investigated DSN population. Imputed WGS data counted 15,055,053 variants. Filtering for biallelic variants was performed with a minor allele frequency (MAF) of ≥$5\%$ and an SNP call rate of ≥$90\%$ using vcftools v0.1.14 [15], and resulted in 11,770,842 variants which were available for GWAS. Genomic positions of variants refer to the Bos taurus genome version ARS-UCD1.2 [16]. ## 2.3. Phenotypes Data regarding 305-day milk performance were obtained from the breeding company in March 2022 containing data for the years 2009 to 2021. Traits included milk, fat and protein yield in kilograms (milk kg, fat kg, protein kg) for the first three lactations (LA1, LA2, LA3) considering only full lactations (≥270 days in milk). Fat and protein content (fat %, protein %) were calculated by dividing the fat or protein yield by milk yield of the respective lactation. The lactation mean (LAm) was calculated when data of the respective trait were available in all three lactations. Values outside the mean ± 3 standard deviations of each trait were removed. Up to 1991, 1610, 1262 and 1010 DSN cows had milk performance data available in LA1, LA2, LA3 and LAm, respectively. ## 2.4. Genome-Wide Association Studies (GWASs) GWASs were performed with multiple linear regression models implemented in the R language for statistical computing using the base packages [17]. The model for testing the additive effect of each SNP for each trait Y included fixed effects for population stratification ps, farm f, sire s, birth year by, birth season bs, calving year cy, calving season cs and age at first calving in days ac, together with the SNP genotype gt and the residual error e:[1]Y=ps+f+s+by+bs+cy+cs+ac+gt+e Population stratification was estimated using the pairwise population concordance test (parameters --cluster and --ppc) implemented in PLINK v1.9 [18] and the identity-by-state matrix of all 2160 DSN cows. The p-value cut-off of 1 × 10−5 resulted in 58 clusters of relatedness. Fixed effects marked with an asterisk (“”) were included in the model only when the difference in Akaike information criterion (ΔAIC) between the null model (Y = ps) and the null model extended with one of the other fixed effects (Y = ps + fixed effect) was ≤−10 (Supplementary Table S1). The heritability of traits was calculated using GCTA software v1.93.2 [19] by performing an REML (restricted maximum likelihood) analysis on the GRM (genomic relationship matrix). ## 2.5. Significance Threshold and QTL Definition Calculating the inflation factor of p-values from GWAS for all traits, we observed high average inflation factors λ on autosomes (1.51 ± 0.20 SD) as well as on chromosome X (2.09 ± 0.51 SD) (Supplementary Figure S1). The effective population size of DSN was low with Neff = 107 [1]. Hence, DSN had a highly structured population, so we expected a certain inflation of p-values. To reduce the number of false positive associations with each trait, we corrected p-values to λ = 1.2 for autosomes and chromosome X, separately. Significance thresholds were set with Bonferroni correction, dividing the α-level by the number of independent variants. The number of independent tests was estimated using linkage disequilibrium (LD)-based pruning with PLINK v1.9 [17]. The parameter indep-pairwise was used with a window size of 500, a step size of 100, and an r2 value of 0.6, resulting in 642,791 independent variants. Thresholds for highly significant ($p \leq 0.01$), significant ($p \leq 0.05$) and suggestive ($p \leq 0.1$) associations were divided by the number of independent variants corresponding to −log10(p) ≥ 7.8, ≥7.1 and ≥6.8, respectively. Quantitative trait locus (QTL) regions were coarsely defined by grouping associated variants ($p \leq 0.1$) within ± 2.5 Mb on a chromosome across all traits. Regions with fewer than three associated variants were discarded from further analysis. Afterwards, QTL regions were defined around each of the top SNPs by taking all variants above the suggestive threshold for significance ($p \leq 0.1$) that were not more than 500 kb apart. For visualization, we added 100 kb up- and downstream to the QTL region. ## 2.6. QTL Annotation Detected QTL regions as defined above were investigated for candidate genes. The Bos taurus gene annotation was downloaded from Ensembl release 106 [20]. A total of 21,880 protein-coding genes were considered. Genes in every QTL region were analyzed separately for gene ontology (GO) term enrichment using g:Profiler version e106_eg53_p16_65fcd97 in the R package gprofiler2 v0.2.1 [21]. The options for annotated genes and a g:SCS threshold of $p \leq 0.1$ for suggestive and $p \leq 0.05$ for significant GO term enrichments were used. Variants in QTL regions were investigated for impact consequence on gene transcripts using variant effect predictor (VEP) from Ensembl [22]. For this, rare variants from the imputed WGS genotypes (MAF ≥ $1\%$) were also considered. Categories included variants with high, moderate or low impact. Additionally, the VEP predicted if missense variants were tolerant or deleterious using the SIFT algorithm [23]. Variants with an impact defined as intergenic, intron, non-coding transcript or non-coding transcript exon variant were not considered. Top SNPs located in intron or promoter regions were tested to examine whether they were located in transcription factor binding sites of vertebrates as obtained from JASPAR CORE database release 9 [24] using the R package TFBSTools v1.36.0 [25]. Identified QTL regions were investigated for an overlap with previously published associations and QTLs for the same trait from cattleQTLdb release 48 [26]. PubMed IDs of corresponding publications were obtained using R package easyPubMed v2.21. Figures were produced using the R package ggplot2 v3.4.0 [27]. Gene arrows and names were added to plots using the R packages gggenes v0.4.1 and ggrepel v0.9.2 [28,29]. p-values between genotype groups in SNP effect plots were estimated using pairwise t-tests and displayed using R package ggpubr v0.5.0 [30]. ## 3.1. Genomic Regions Associated with Milk Production Traits We identified 13 significant and suggestive loci (−log10(p) > 6.8) on nine chromosomes (1, 3, 5, 6, 8, 10, 20, 21 and 27) for all investigated milk production traits except the single lactation traits milk yield in LA1, and fat and protein yields in LA2 (Table 1). In total, 1,980 sequence variants (733 highly significant, 707 significant and 540 suggestive) were associated with milk performance traits (Supplementary Table S2). Due to the high correlation between traits, some variants were associated with multiple traits. Correlations between yield traits were high within single lactations (r ≥ 0.77) and moderate across lactations (r ≥ 0.37) (Supplementary Table S3). Fat content as well as protein content were moderately correlated across lactations (r ≥ 0.41 and r ≥ 0.55, respectively). Within a lactation, fat and protein content had only a low correlation (r ≤ 0.27). The protein yield and content, as well as fat yield and content, were not correlated (r ≤ 0.08). Depending on the investigated lactation, the heritability for milk yield ranged between 0.37 and 0.42, for fat yields between 0.29 and 0.42, and for protein yields between 0.32 and 0.38 (Supplementary Table S4). The heritability for milk fat percentage in DSN ranged between 0.45 and 0.60 and for milk protein percentage between 0.59 and 0.72. These values are consistent with expected values for Holstein and other breeds [31,32,33]. Highly significantly associated genomic loci (−log10(p) ≥ 7.8) were identified for milk yield on chromosomes 1 and 8, for fat content on chromosome 5, for protein content on chromosomes 5, 6 and 10, for fat yield on chromosome 27 and for protein yield on chromosome 1. In the following, we explain only the most significant loci. ## 3.2. Most Significant Locus Affecting Milk Fat and Protein Content on Chromosome 5 The most significant locus was identified for milk fat content on chromosome 5 (Table 1, Figure 2b). This effect was significant across all lactations whereas with the most significant effect found in LA1 in association with rs211210569 (−log10(p) = 11.93) at 93,516,066 bp. Its minor allele T, which segregated in DSN at a frequency of 0.23, reduced milk fat content in LA1 by $0.151\%$ points. The allele effect was additive (Figure 2c). The top SNPs for lactations LA2, LA3 and LAm were located 9 kb downstream (5:93,525,076, rs207994397), 3 kb downstream (5:93,518,685, rs209372883) and 3.1 Mb upstream (5:90,406,099, rs134606936) of the top SNP in LA1, respectively. The negative effect of the minor allele of rs207994397 on fat content was highest in LA2 (βMA = −$0.226\%$). The same locus also had a highly significant effect on protein content in LA3, with the top SNP rs41604619 at 95,098,733 bp (−log10(p) = 8.26, Table 1, Figure 2b). This SNP was located 1.5 Mb upstream of the top SNP for fat content in LA1. While the minor allele of the top SNP for fat content rs211210569 reduced the fat content, the minor allele T (MAF = 0.29) of the top SNP for protein content rs41604619 increased the protein content by $0.061\%$ points. The top SNPs affecting milk fat and protein content were in low linkage disequilibrium (LD) of r2 = 0.08 (D’ = 0.80). The top SNP rs211210569 associated with fat content in LA1 was located in intron 1 out of three introns of the gene MGST1 (microsomal glutathione S-transferase 1). In addition, sequence data of DSN revealed two missense variants in MGST1 (5:93,497,602, novel; 5:93,509,514, rs210140457), both with tolerated impact on the protein function (Supplementary Table S5). Although MGST1 harbored the most significant SNP, the neighboring genes LMO3 (LIM domain only 3) and SLC15A5 (solute carrier family 15 member 5) were also located in the highly associated region (Table 2). Local top SNPs were found in LMO3 (5:93,337,092, rs109041635) as well as in SLC15A5 (5:93,658,801, rs451608212). The top SNPs were linked to the key SNP in MGST1 with r2 = 0.61 (D’ = 0.96) and r2 = 0.43 (D’ = 0.82), respectively. LMO3 harbored one synonymous variant only and SLC15A5 contained two tolerated (5:93,617,050, rs209784274; 5:93,650,729, rs136481676) and two deleterious missense variants (5:93,617,097, rs109333413; 5:93,627,362, rs211525134) (Supplementary Table S5). The deleterious missense variants caused amino acid changes at positions 247 and 291 of 571 amino acids in the protein. The amino acid changes were located between two transmembrane domains which belong to the “Proton-dependent oligopeptide transporter family” (Interpro: IPR000109, UniProtKB: F1N3P6_BOVIN). Moreover, MGST1 was suggestively assigned to the biological process GO term “cellular response to lipid hydroperoxide” (GO:0071449) and LMO3 was assigned to the “positive regulation of glucocorticoid receptor signaling pathway” (GO:2000324) (Supplementary Table S6). SLC15A5 was involved in “transmembrane transport” (GO:0055085), but was not significant in the enrichment analysis. ## 3.3. Loci on Chromosome 1 and 8 Affect Milk Yield as Well as Fat and Protein Yields Loci on chromosomes 1 and 8 were highly significantly associated (−log10(p) >7.8) with milk yield and other yield traits (Table 1, Figure 3a,b). The locus on chromosomes 1 also had significant effects on the fat and protein yield, while the locus on chromosome 8 had a significant effect on the protein yield. The significant effects on chromosome 1 were all observed in LA3. The top SNP associated with milk yield was rs209578598 at 83,272,783 bp (−log10(p) = 7.82). Its minor allele A had a frequency of 0.36 and decreased the milk yield by 349 kg (Table 1, Figure 3c). In the same chromosomal region, rs379781684 at 75,187,853 bp was the top SNP affecting protein (−log10(p) = 7.96) and fat yield (−log10(p) = 7.03). The minor allele C had a frequency of 0.15 and decreased the protein yield by 16.8 kg and fat yield by 19.4 kg. The top two SNPs had a distance of 8.1 Mb. They were in low-to-moderate linkage in terms of the common LD estimators (r2 = 0.05, D’ = 0.41). FGF12 (fibroblast growth factor 12) was the only gene residing in the top region for fat and protein yield of chromosome 1 and was, therefore, the most likely candidate gene (Table 2). The top SNP was in intron 3 of four introns of FGF12. No variants occurred in DSN that might have had an impact on the protein sequence of FGF12 (Supplementary Table S5). The top region for milk yield of chromosome 1 (rs209578598, 1:83,272,783) contained seven genes (Table 2). The top SNP was in intron 4 of nine introns of the gene PARL (presenilin associated rhomboid like) (Supplementary Table S2). This SNP was in high LD with SNPs in YEATS2 (YEATS domain containing 2), a gene that carries many additional SNPs, but its specific function is barely known. Interesting functional genes were HTR3C and ENSBTAG00000039011, which encode the 5-hydroxytryptamine (serotonin) receptor, family member C; however, ENSBTAG00000039011 was only predicted and could have simply been a gene duplication. Furthermore, HTR3C carried two missense variants (1:83,069,525, rs385043393, −log10(p) = 6.88; 1:83,064,588, rs381149322), and ENSBTAG00000039011 had two splice acceptor variants which had a high impact on gene transcripts (Supplementary Table S5). The receptor is necessary for the signaling of serotonin (REAC:R-BTA-112314), a neurotransmitter that is essential for the regulation of milk synthesis in the epithelium of the mammary gland (Supplementary Table S6). The gene ABCC5 (ATP binding cassette subfamily C member 5) resided in the middle of the region associated with milk yield and contained three synonymous and one splice region variant (Supplementary Table S5). This gene is involved in tripeptide (or more precisely glutathione) transmembrane transporter activity (GO:0034634, GO:0042937) (Supplementary Table S6). On chromosome 8, the top SNP affecting milk yield in LA3 was rs385677618 at 56,534,074 bp (−log10(p) = 8.04) and in LAm rs797297575 at 60,079,367 bp (−log10(p) = 8.03, Table 1, Figure 4a,b). The minor alleles T of the top SNP for LA3 and G of the top SNP for LAm had frequencies of 0.33 and 0.14 and decreased the milk yield in LA3 by 346 kg and 280 kg, respectively. The same region showed an association with the protein yield in LA3 and LAm with the top SNP rs432948152 at 56,568,636 bp (−log10(p) = 6.84) and rs210911072 at 59,917,537 bp (−log10(p) = 7.53), respectively. The minor alleles of those two variants decreased the protein yield by 8.5 kg and 6.4 kg, respectively. The minor alleles were dominant (Figure 4c). These two SNPs, which were 3.5 Mb apart from each other, had a moderate linkage with each other (r2 = 0.23, D’ = 0.84). The top SNPs for milk and protein yields (rs385677618 and rs432948152, respectively) were intergenic between TLE4 (TLE family member 4, transcriptional corepressor) and the pseudogene ENSBTAG00000006294 (ATP synthase subunit f, mitochondrial pseudogene) (Table 2). Both genes were more than 500 kb away from the top SNP. TLE4 was the only protein coding gene. The gene contained one tolerated missense variant in the DSN population (8:55,714,236, rs719817703, Supplementary Table S5). The gene TLE4 was included in the pathway “Degradation of β-catenin by the destruction complex” (REAC:R-BTA-195253) belonging to the “Wnt signaling” pathways (Supplementary Table S6). The region around the second top SNP (rs797297575) on chromosome 8 at about 60 Mb comprised twelve different genes (Table 2), among which were four olfactory receptor genes which are involved in the “Olfactory transduction” pathway (KEGG:04740, Supplementary Table S5). The top SNP itself was located between the OR13E10 (olfactory receptor family 13 subfamily E member 10) and OR13J1C (olfactory receptor family 13 subfamily J member 1C) genes, which are both less than 7 kb away. Among the olfactory receptor genes, OR13E1 (olfactory receptor family 13 subfamily E member 1), OR13E10 and OR13J1F (olfactory receptor family 13 subfamily J member 1F) had variants with high impacts on gene transcripts, including frameshift and stop-gained mutations, but also variants with moderate impact such as missense variants (Supplementary Table S5). Five additional genes had variants with moderate or even deleterious impacts: GBA2 (glucosylceramidase β 2), RGP1 (RGP1 homolog, RAB6A GEF complex partner 1), SPAG8 (sperm associated antigen 8), TMEM8B (transmembrane protein 8B) and OR13J1C (olfactory receptor family 13 subfamily J member 1C). ## 3.4. Loci on Chromosomes 6 and 10 Affected Milk Protein Content Loci on chromosomes 6 and 10 were highly significant associated, specifically with milk protein content (Table 1, Figure 5a). There were no effects observed for the other investigated traits. On chromosome 6, the top SNP rs378558630 at 85,373,205 bp was associated with protein content in LA1, LA2 and LAm with the highest significance in LA2(−log10(p) = 8.47, Table 1). The minor allele A had a frequency of 0.49 and decreased the protein content ranging from $0.035\%$ points in LA1 to $0.055\%$ points in LA2. The top SNP for LA3 rs382685419 was only 2 kb upstream of the other SNP (Figure 5b). Its minor allele T had a similar frequency (MAF = 0.50) and similar negative effect size, reducing the protein content by $0.054\%$ points. The allele effects were additive (Figure 5c). The two SNPs were in high LD (r2 = 0.96, D’ = 1.0). Although the whole significant region contained eight genes (Table 2), the top two SNPs were intergenic between SULT1E1 (sulfotransferase family 1E member 1) and CSN1S1 (α-S1 casein) (Figure 5c). CSN1S1 was the only gene in the associated region that is encoded on the positive strand; all others were on the negative strand. The high density of highly significant SNPs around rs378558630 upstream of CSN1S1 could affect the expression of CSN1S1 and thereby regulate the milk protein content. The casein genes CSN2 (β casein), CSN1S2 (α-S2 casein) and CSN3 (kappa casein) were close to but not directly within the associated region (Figure 5c). Near the top SNP, SULT1E1 carried missense variants. SULT1E1 and SULT1B1 (Sulfotransferase Family 1B Member 1), which were up- and downstream of CSN1S1, were involved in sulfation processes and sulfotransferase (GO:0051923, GO:0008146) (Supplementary Table S6). Furthermore, the gene ENSBTAG00000053565 (UDP-glucuronosyltransferase 2C1) contained two variants with a high impact on its function including a stop-gained and a frameshift mutation (Supplementary Table S5); the protein contributed to the pathways “Steroid hormone biosynthesis” (KEGG:00140) and “Pentose and glucuronate interconversions” (KEGG:00040) (Supplementary Table S6). On chromosome 10, the two SNPs rs211239920 at 44,746,907 bp (−log10(p) = 10.48) and rs208655317 at 44,746,980 bp (−log10(p) > 7.95), which were only 73 bp downstream of the first SNP, were highly significantly associated with protein content in LA2, LA3 and LAm, respectively (Table 1, Figure 6a). The minor allele T of the most significant SNP rs211239920 with a frequency of 0.34 increased the protein content by $0.054\%$ in LA2, meaning the allele effect was additive (Figure 6b). The two top SNPs, rs211239920 and rs208655317, were intronic variants (intron 2 of three introns) of the gene GNG2 (G protein subunit γ 2). The SNP rs211239920 was located in the putative transcription factor binding site of HMBOX1 (MA0895.1), a target for the Homeobox-containing protein 1. An additional 15 highly linked (r2 > 0.9) and highly associated SNPs (−log10(p) > 7.8) were in the GNG2 gene region (Supplementary Table S2). The whole region underlying the top variant rs211239920 comprised 40 genes (Table 2). A total of 21 of those 40 genes comprised variants with a moderate impact on gene transcripts. The five genes ENSBTAG00000053552 (mTORC1-mediated signaling), NID2 (nidogen 2), PTGDR (prostaglandin D2 receptor), ENSBTAG00000040590 and TPM1 (tropomyosin 1) contained even variants with a high impact such as splice acceptors and frameshift variants. ## 3.5. A Locus on Chromosome 27 Affected Milk Fat Yield On chromosome 27, a highly significant QTL for milk fat yield in LA1 was found with rs42120938 as the top SNP at 25,539,379 bp (−log10(p) = 8.95, Table 1, Figure 7a,b). The minor allele A of this variant had an allele frequency of 0.42 and decreased the fat yield in LA1 by 9.4 kg. The allele effect was additive (Figure 7c). The top SNP was located in intron 2 of TNKS (tankyrase), a large gene consisting of 27 exons (Supplementary Table S2). Interestingly, TNKS carries a novel deleterious missense variant that segregates in DSN (27:25,625,081), but did not reach the significance threshold (Supplementary Table S5). The deleterious missense variant caused an amino acid change at position 825 of 1327 amino acids in the protein located between two ankyrin repeats, which are common protein–protein interaction motifs (Interpro:IPR002110, UniProtKB:E1B8R5_BOVIN). TNKS was assigned to the metabolic pathway “Degradation of AXIN” (REAC:R-BTA-4641257), which contributes to the “Wnt signaling” pathway (Supplementary Table S6). ## 4. Discussion In this study, we performed GWASs with milk performance data in a population of 2160 DSN cows using WGS data. Since the herdbook population of DSN is small, with only approximately 2500 cows in total, the population available for this association study was also small. Despite the limited power to find significant genomic loci in such a small population, it was possible to detect 1980 associated variants located in 13 loci on nine chromosomes. This was mainly possible due to the use of WGS data. Some of the identified loci were pleiotropic, such as the locus on chromosome 5, where milk fat as well as protein content were affected. The loci on chromosomes 1 and 8 were associated with different yield traits, namely, milk, protein and fat yields on chromosome 1, and milk and protein yields on chromosome 8. The pleiotropy on chromosomes 1 and 8 contributed to the high correlations between the yield traits, especially within the same lactation. The identification of different loci and effects for the same milk production trait across different lactations was partly due to the generally low and declining number of animals from lactation 1 to 3, and adventitious environmental effects, not all of which could be corrected for. As we examined “field data”, large individual differences due to environmental fluxes were expected. Besides these effects, genes necessary for the development of the mammary gland and for metabolic pathways contributing to milk production differed between the first and subsequent lactations. Genetic determinants driving differential development in different stages of life are well captured through GWASs, which examine every lactation separately. In our previous GWASs for milk production traits in DSN [5], where the sample size and number of SNP genotypes were smaller (1490 DSN cows, 36K SNPs from Illumina BovineSNP50), we identified 41 significantly associated SNPs located on 17 chromosomes. Not all of those loci could be confirmed in this study. Nevertheless, seven associated loci from this study and the previous one overlapped (on chromosomes 1, 3, 6, and 8); six loci were novel and have not shown an association before (on chromosomes 5, 10, 21 and 27). Consistent with the catalogue of published GWAS results obtained from the cattleQTLdb release 48, the MGST1 locus identified in DSN cows for fat and protein content was reported for different Holstein populations and diverse other breeds such as Braunvieh, Fleckvieh, Normande and Montbéliarde [34,35,36,37]. Similarly, evidence was provided in numerous independent association studies for associations with the casein gene cluster on chromosome 6 for protein content [34,35,38,39], the locus on chromosome 10 for protein content [34,38,40,41], the locus on chromosome 20 for protein content [38,40,41,42] and the locus on chromosome 21 for milk yield [42]. A comprehensive list of 24 publications that reported loci for the same milk performance traits as presented here is available in Supplementary Table S7. The most exciting result was the identification of the locus on chromosome 5 harboring MGST1, which affects milk fat content, while simultaneously missing an effect of DGAT1 on chromosome 14 as the milk fat-producing gene with the highest effect in Holstein and other breeds. This finding was surprising for two reasons: Firstly, DSN is considered an ancestor population of Holstein, suggesting there should be an effect of DGAT1 in DSN as well. However, DGAT1 was found to be almost fixed for the Alanine protein variant of the K232A polymorphism in the investigated DSN population (frequency of 0.97), which is associated with higher milk and protein yield [43], and, therefore, cannot be associated with any trait. Secondly, because the impact of MGST1 was high and had not been identified in our previous study with the commercial IlluminaBovineSNP50 BeadChip [5]. The identification of this locus was only possible using imputed DSN-specific whole-genome sequencing data [44]. As mentioned above, MGST1 had been identified before as a candidate gene for milk fat content in numerous GWASs with different cattle breeds. Those studies could not fully rule out effects of the neighboring genes LMO3 and SLC15A5. Although we also prioritize MGST1 as the key candidate gene for milk fat content on chromosome 5, we also cannot rule out effects of the neighboring genes that showed smaller peaks of associated SNPs. Previously, a strong eQTL effect for MGST1 expression in mammary tissue of dairy cattle supported the regulatory effect of MGST1 on milk fat content [45]. This was also underlined by an enriched expression of MGST1 in adipocytes in human breast (www.proteinatlas.org) [46]. It contributes to many metabolic pathways, among them the estrogen metabolism (www.proteinatlas.org). Knockout of MGST1 in mice resulted in lower plasma cholesterol concentrations (www.mousephenotype.org) [47], which in turn is a marker for the negative energy balance in early lactating cows [48]. Therefore, MGST1 might contribute to milk fat via the regulation of energy and/or fatty acids for the production of milk fat in the mammary gland. Nevertheless, the neighboring genes LMO3 and SLC15A5, which showed smaller peaks of associated variants, remain interesting candidate genes. SLC15A5 is responsible for protein transport through membranes and LMO3 was shown to contribute to reprogramming of adipose tissue depots during obesity, thereby modulating nutrient homeostasis [49]. Transcripts of both genes were enriched in breast adipocytes (www.proteinatlas.org). On chromosome 1, two loci were associated with milk, protein and fat yield. In the locus affecting protein and fat yield, FGF12 was the only candidate gene. FGF12 has an impact on ribosome biogenesis [50] and, therefore, on the translation amount and efficiency, which in turn could directly influence the protein yield in milk and indirectly the milk fat yield. Among the other genes in the second locus 8.1 Mb downstream, YEATS2, ABCC5 and HTR3C/ENSBTAG00000039011 are interesting candidate genes. YEATS2 expression is enriched in breast myoepithelial cells (www.proteinatlas.org) and ABCC5 was shown to influence body fat mass, probably by regulating GLP-1 (glucagon-like peptide 1) [51]. The serotonin receptor HTR3C/ENSBTAG00000039011 is necessary for the signaling of serotonin, a neurotransmitter that is essential for the regulation of milk synthesis in the epithelium of the mammary gland. However, in general, no gene can be prioritized based on the current mapping resolution. On chromosome 8, the whole region between 55 and 60 Mb was significantly associated with milk and protein yield. Although two different top SNPs with a distance of about 3.5 Mb were mapped for the yield traits in LA3 and LAm, we would not expect two genes to be underlying the statistical finding in this region. Even though TLE4 is the only candidate gene close to the top SNP for milk and protein yield in LA3, the other genes in the whole region cannot be excluded. TLE4 remains interesting as a transcriptional corepressor, as it has been linked to obesity in both mice and humans before [52]. Close to the second-ranked SNP were several SNPs carrying functional mutations which might be functionally interesting; however, prioritization of a key candidate gene was impossible on the basis of the current data. Among the interesting genes were GBA2 (Glucosylceramidase β 2) and HINT2 (Histidine triad nucleotide binding protein 2), which are involved in lipid metabolism (www.proteinatlas.org), and NPR2 (Natriuretic peptide receptor 2), which is enriched in breast fibroblast (www.proteinatlas.org). A highly significant locus was identified on chromosome 6 in the region of the casein cluster, with CSN1S1 as the closest and most likely candidate gene. This association was found in our previous GWAS [5]. The encoded α-S1 casein comprised $40\%$ of the casein fraction in bovine milk [53]. This casein protein is important for the capacity of milk to transport calcium phosphate (www.proteinatlas.org). Polymorphisms in the regulatory region of CSN1S1 contribute to differences in the transcription level and, therefore, to the amount of α-S1 casein produced, which is known to influence milk protein as well as milk fat content and milk properties [54,55,56]. The genetic architecture of the casein gene cluster of DSN in comparison to other breeds was investigated in detail [57]. The locus on chromosome 10 associated with protein content was, with 3.98 Mb and 40 genes, the biggest of all identified loci in this study. As a result of the high number of associated variants and genes in this region, no gene could be prioritized. Among the interesting genes with respect to mutations and functions were GNG2 (G protein subunit γ 2), TRIP4 (Thyroid hormone receptor interactor 4) and LACTB (Lactamase β). The most significant SNPs were located in GNG2. As a G protein subunit, GNG2 is involved in signaling mechanisms across membranes. GNG2 is enriched in breast adipocytes (www.proteinatlas.org). TRIP4 may act in energy partitioning, thereby ensuring sufficient energy for milk production, and LACTB is involved in lipid metabolism (www.proteinatlas.org). TNKS was the only candidate gene for the milk fat yield locus on chromosome 27. TNKS occurs in many tissues and it was found in various cell types of the brain. A knockout mouse study (www.mousephenotype.org) linked this gene to reduced body fat content and lower levels of blood glucose, blood protein and blood cholesterol, most likely by regulating vesicle trafficking and modulating the localization of GLUT4 [58]. As already mentioned above, the high quality of imputed DSN-specific WGS data was the key to the findings of this study. In order to define the population substructure, e.g., in an identity–state matrix, the WGS data provided the most comprehensive information of DSN-specific genomic variants between individuals. A downside of the usage of WGS data was the higher significance threshold caused by the higher number of variants tested. Therefore, the p-values thresholds were more stringent and a higher number of animals was needed to reach the power to identify associations. ## 5. Conclusions This GWAS using imputed WGS data for 2160 DSN identified 13 loci on nine chromosomes associated with milk production traits in different lactations in dual-purpose DSN cattle. The resolution of WGS data helped to pinpoint associated genomic loci and the underlying genes, despite the limitation that the highest possible sample size of the investigated DSN population was still relatively small. Although DSN is considered an ancestor breed of Holstein, the major gene affecting milk fat content in DSN was MGST1, while DGAT1 was the major gene affecting fat content and milk yield in Holstein cattle, which is fixed for the alanine protein variant for high milk yield in DSN. Additionally, we prioritized the following genes upon presence of highly significant SNPs located in or close to these genes, the putative function of SNPs and the function of affected genes: FGF12 for protein and fat yield, HTR3C for milk yield, CSN1S1 and GNG2 for protein content, TLE4 for milk and protein yield and TNKS for milk and fat yield. Since the DSN population was small, the development of a scheme which utilizes the obtained information for genomic selection is challenging. For increasing a learning population, data of several generations of animals has to be collected. Nevertheless, most significant SNPs contributing to variation in DSN cattle could be used to select for high milk yield and content traits to improve the economic merit of the animals on the market. However, this requires additional information on potentially linked gene variants that would undermine the breeding goals. 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--- title: 'Factors Associated with Willingness toward Organ Donation in China: A Nationwide Cross-Sectional Analysis Using a Social–Ecological Framework' authors: - Mengjun Zeng - Haomiao Li - Xiaohui Song - Jipin Jiang - Yingchun Chen journal: Healthcare year: 2023 pmcid: PMC10048496 doi: 10.3390/healthcare11060824 license: CC BY 4.0 --- # Factors Associated with Willingness toward Organ Donation in China: A Nationwide Cross-Sectional Analysis Using a Social–Ecological Framework ## Abstract Improving public willingness toward organ donation is an important solution to the low organ donation rate. This study aimed to explore factors impacting public willingness for organ donation in China from a multi-agent perspective and further explore the impact of these factors on high or low willingness, using a social–ecological framework. Data from a total of 11,028 (effective rate, $94.18\%$) participants were analysed. Generalised linear model (GLM) and quantile regression were used to explore factors associated with willingness and high/low willingness toward organ donation, respectively. The mean willingness toward organ donation was 56.9 (range, 0–100) points. GLM regression revealed that age, family health, males, lower educational levels, and agricultural hukou were negatively associated with willingness. For personality, conscientiousness was negatively associated with willingness, whereas openness was positively associated with willingness. Health literacy perceived social support, and media utilisation were positively associated with willingness. Quantile regression further indicated that educational levels of college, bachelor, master’s, and PhD, openness, health literacy, perceived social support, and media utilisation were positively associated with organ donation willingness at all percentiles. It is necessary to adopt more targeted and diversified publicity, education, and guidance for different types of individuals. Meanwhile, social support needs to be strengthened. To enhance the willingness of the residents to donate organs, media publicity should be strengthened, particularly by using modern ways to improve their health literacy. ## 1. Introduction Organ transplantation plays an essential role in the treatment of patients with organ failure. With advances in medical technology, organ donation has saved large numbers of lives. In China, organ donation and transplantation have shown remarkable improvement through the untiring efforts of several generations of transplant surgeons [1]. The team of transplant coordinators is growing and maturing with the construction and improvement of the organ donation system. Voluntary deceased organ donation has increased annually since the pilot programme was initiated in 2010. Since 1 January 2015, China abolished the use of prison organs, and voluntary organ donation has become the only legal source of organ transplantation in China [2]. At present, China still adopts the ‘opt-in’ system, which means people with full capacity for civil conduct can express their willingness to donate through voluntary registration, and people under the age of 18 are forbidden to register. Volunteers can choose the type of organ or human tissue they want to donate. When the intention to donate changes, the registration can be changed or withdrawn at any time. However, the ultimate donation is subject to medical evaluation and the consent of the immediate family. There are two main approaches to registering as a volunteer, which, respectively, are online registration through WeChat or the official website of the China Organ Donation Administrative Center and, offline, through written registration through the local Red Cross. If a citizen has not expressed his disapproval of organ donation during his lifetime, his immediate family members may jointly express their willingness to donate his organs in written form [1,3]. From 2015 to 2020, 29,334 cases of organ donation after death were completed. The organ donation rate (per million population (PMP)) increased from $2.01\%$ in 2015 to $3.70\%$ in 2020. Although the absolute number of organ donations in China has increased, like other countries in the world, China still faces a shortage of donors to meet the huge demand for domestic patients [4]. The PMP in China, in 2020 ($3.70\%$), was lower than the global average ($5.80\%$), and much lower than other countries, such as the United States ($38.0\%$), Spain ($38.0\%$), Estonia ($25.4\%$), Croatia ($25.4\%$), and Portugal ($24.8\%$) [5]. The Chinese government has made great efforts to improve public awareness and willingness for organ donation. The main publicity channels for organ donation include shooting public service advertisements. To be more specific, celebrities who register as organ donation volunteers are invited to serve as ambassadors for organ donation. In addition, news media report typical donation cases and touching stories to motivate more people to join. Moreover, public welfare activities are held on special festivals—June 11th has been set as China’s Organ Donation Day since 2017. Each year on this day, Red Cross, organ donation management centres, and Organ Procurement Organizations from the national to the provincial level will hold various forms of publicity activities, such as organ donations in communities, universities, hospitals, and so on. Moreover, organ donation courses have been introduced into Chinese university classrooms. In the relevant departments of the hospital, such as the ICU and neurosurgery department, organ donation billboards will be posted in order to let the family members of severe patients know that organ donation could be another option when a life cannot be saved [1,6]. At present, the public’s willingness and awareness of organ donations are increasing annually; however, the organ donation rate still lags far behind the advanced international level. Improving public willingness toward organ donation is a significant strategy for improving the low organ donation rate. Therefore, identifying factors associated with willingness toward organ donation is essential and urgent. Previous studies have explored factors associated with willingness toward organ donation from many perspectives, mainly focusing on the following aspects. First, demographic factors, including age, gender, occupation, income, and family-centred traditional values, have been reported to be associated with willingness toward organ donation [7,8]. Second, knowledge of organ donation; attitude toward organ donation and factors influencing this attitude, such as information delivery, education, and media use, have been reported to be associated with willingness toward organ donation [9,10,11,12]. Third, laws, legislations, and social policies, such as incentives, are also associated with willingness toward organ donation [13]. Text messaging, feedback, and prosocial emotions also affect willingness toward organ donation [14]. In addition, some personality-associated characteristics, such as self-efficacy, may be linked with willingness toward organ donation. Self-efficacy is a cognitive process in which individuals learn new behaviours that affect their ability to their performance in future events through environmental and social factors [15]. Some previous studies have indicated that personality, determined using the Big Five personality traits, influences willingness toward blood and organ donation [16,17,18], nevertheless, their associations with willingness toward organ donation are less studied. Previous studies have revealed that the factors associated with willingness toward organ donation belong to various dimensions and originate from multiple subjects, such as individual, family, society, and government. Nevertheless, only a few studies have assessed factors associated with willingness toward organ donation from a multi-agent perspective. Meanwhile, to improve willingness, factors need to be intervenable, although most factors identified in previous studies (such as age, gender, marital status, etc.) are difficult to intervene. In addition, one potential factor may have different effects on high and low willingness, respectively; considering these different effects is important for constructing more targeted intervention measures. The present study was based on national survey data and can provide more representative conclusions and make more general suggestions. On one hand, the present study comprehensively analyses the factors influencing willingness toward organ donation from a multi-agent perspective. On the other hand, it focuses on the differences in the factors affecting the different levels of willingness. We used the social–ecological model as a framework to assess factors influencing willingness toward organ donation. The social–ecological model is a multiple-tier framework, which includes individual, relational, community, and societal levels from micro to macro levels, for organising risk and protective factors, which then aid in determining corresponding prevention strategies. In a previous study, the social–ecological model proposed that individual willingness was associated with individual, physical, social, and regulatory influences [19]. Efforts to modify the willingness must consider these multiple levels of constraints [20,21,22]. Factors toward organ donation willingness identified in current research tend to be fragmented. That is, even where summaries of factors are provided, they are often limited to one or two social–ecological levels. Following the enhanced organisation of factors, the social–ecological model of organ donation willingness can provide grounding for multi-level intervention and prevention programme design and implementation [23]. In summary, based on a nationwide survey, this study aimed to explore factors influencing public willingness toward organ donation and further explore the impact of these factors on high or low willingness using a social–ecological framework. To add to the current literature on the topic and further comprehend organ donation willingness, as well as intervention feasibility of factors, our study particularly detects several elements in each level or in several levels that are significantly associated with organ willingness. ## 2.1. Sampling and Participants The data used were from surveys conducted in 23 provinces, 5 autonomous regions, and 4 municipalities in mainland China from July to September 2021. A total of 120 cities were selected from 2 to 6 cities in each province and autonomous region by random number table method in multi-stage sampling. Based on the results of the seventh National Population Census in 2021, 120 urban residents were selected for quota sampling (quota attributes are gender, age, and urban–rural distribution) so that the gender, age, and urban–rural distribution of the samples basically conform to the population characteristics. In each sample city, questionnaires were collected through a professional online questionnaire survey platform (Wenjuanxing: https://www.wjx.cn/; accessed on 1st July to 1st September 2021). The volunteers were asked to distribute the questionnaires face-to-face with a quick response code, which can present the questionnaires once scanned. Informed consent was obtained from all respondents before the survey began. A total of 11,709 questionnaires were collected. The datasets are not publicly available as the data needs to be used for other research purposes but are available from the corresponding author upon reasonable request. Participants who were aged ≥12 years and filled in the informed consent form, could complete the network questionnaire survey by themselves or with the help of the investigators and could understand the meaning of each item in the questionnaire were included in the present study. After excluding participants with key missing values, 11,028 valid questionnaires (with a $94.18\%$ effective rate) were obtained, with high quality and national representativeness. ## 2.2.1. Dependent Variable Participants were asked to choose a number from 0 to 100, representing the willingness toward organ donation, with 100 being the strongest level of willingness, and 0 indicating no willingness at all. Residents chose scores according to their own intentions to reflect their levels of willingness. ## 2.2.2. Independent Variables Based on a social–ecological framework, we classified factors that may potentially be associated with willingness toward organ donation into the following three levels: individual, family, and social levels (Figure 1). Individual level. The individual level refers to the demographic and behavioural factors that influence willingness toward organ donation. At the individual level, participants’ demographic indicators (age, gender, educational level, work status, and nationality) were selected. Additionally, self-efficacy, personality and health literacy were taken into analysis. Self-efficacy was measured using the New General Self-Efficacy Scale (Supplementary Table S1) [24]. Furthermore, personality is closely related to distress, anxiety, and several behaviours [25]. Personality was measured using the 10 items of the Big Five Inventories, with the following domains: neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness (Supplementary Table S2) [26]. Health literacy includes a set of skills required to make appropriate health-related decisions [27], which was associated with attitude, knowledge, and behaviours and can enable individuals to develop transferable skills in accessing, understanding, analysing, and applying health information [28,29]. Health literacy (Supplementary Table S3) was measured using the New Short Form Health Literacy Instrument [30]. Family level. At the family level, factors included income, marital status, family health, hukou status, and family type. The family health was measured using the Short Form of the Family Health Scale (FHS-SF) translated into Chinese with the consent of the original author (Supplementary Table S4) [31]. Family health is a resource at the level of the family unit that develops from the intersection of the health of each family member, their interactions, and capacities, as well as the family’s physical, social, emotional, economic, and medical resources. The Chinese version of the FHS-SF has good reliability (Cronbach alpha = 0.83) and validity (χ2/df = 4.28, GFI = 0.98, NFI = 0.97, RFI = 0.95, RMSEA = 0.07 < 0.08) and can be used to assess the level of family health of Chinese residents [31,32]. Hukou indicates the respondent’s hukou place (including non-agricultural hukou and agricultural hukou) and is a special identifier in China. The hukou status affects several aspects of life in China, including buying a house, buying a car, children’s school enrolment, and other welfare [33,34]. Family type includes nuclear family, conjugal family, backbone family, single-parent family, and other types. Social level. At the social level, factors included social support and social media utilisation. Social support was measured using the Perceived Social Support Scale (Supplementary Table S5). Perceived social support is defined as the availability of individuals to make one feel cared about, valued, and loved [35]. Social media included newspapers, magazines, radios, televisions, books (not textbooks), computers (including tablets), and smartphones. Participants were asked about the frequency of using these media (0 = never, 1 = occasional, 2 = sometimes, 3 = often, and 4 = almost every day). Subsequently, social media utilisation was calculated as the sum of all the frequencies (Supplementary Table S6). ## 2.3. Statistical Analysis First, for description analysis, we compared the differences in willingness between different socioeconomic groups using the Kruskal–Wallis one-way analysis. Kernel density estimations were performed to display the distribution of willingness toward organ donation, as well as the willingness among different groups. Smooth curve fitting for the trend of willingness along with the change of continuous variables (self-efficacy, personalities, family health, health literacy, perceived social support, and media utilisation) were performed on the basis of generalised additive or linear models. Second, to explore the influences of all factors on willingness toward organ donation, a generalised linear model (GLM) was applied. Although independent variables were classified into different levels, our data did not comply with the hierarchical data, in which sample households were included in each sample region, and each individual was included in each sample household [36]. Therefore, multi-level analysis is not appropriate for this study. We constructed three GLM models, i.e., a model including only individual characteristics, a model including individual characteristics and family characteristics, and a model including individual, family, and social characteristics. The fitness of the models was measured based on log-likelihood, Akaike information criterion (AIC) and Bayesian information criterion (BIC). Third, to explore the different effect size predictions of impacting factors, quantile regression (QR) was utilised. QR, introduced by Koenker and Bassett [1978], is an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of models for several conditional quantile functions. QR can describe the relationships between the explanatory variables and willingness across the entire distribution by enabling the modelling of any conditional quantile of the outcome variable [37]. In addition, QR does not assume the normality or homoscedasticity of the distribution of outcome variables [38]. Quantiles 25, 50, and 75 were analysed. The p values were two-tailed, where statistical significance was set at an alpha level of 0.05. Data were analysed using Stata 17.0. ## 2.4. Ethics Approval and Consent to Participate This quantitative study was performed in accordance with the ministry of health and ‘involves people of biomedical research ethics review method (try out)’, national drug supervision and administration of the quality control standard for clinical trials [2003], medical instrument clinical trial regulations [2004], and Declaration of Helsinki. The investigators obtained ethics approval from the Ethics Committee of Jinan University (JNUKY-2021–018). We certify that all applicable institutional and governmental regulations concerning the ethical use of human volunteers were followed over the course of this study. All the study participants provided written informed consent to participate in this study upon recruitment. ## 3. Results Of the 11,028 participants, the mean score of organ donation willingness was 56.9 (range, 0–100) points. The distribution of organ donation willingness indicated that the distribution of each group was relatively balanced, with the highest willingness accounting for the largest and the lowest willingness accounting for the second largest, presented in Figure 2. The sample characteristics and organ donation willingness across different groups are shown in Table 1. Almost $60\%$ of the respondents were aged 19–45 years old; $54.37\%$ were females; $57.66\%$ were registered as non-agricultural hukou; $56.44\%$ were married; $52.13\%$ of respondents had an education level of college or bachelor. The distribution of the sample complied with the distribution of the national population. Younger participants (under 45 years old), females, higher income, those with non-agricultural hukou, higher educational levels, students, and those with free medical care had higher willingness toward organ donation, whereas widowed participants had the lowest willingness (37.41 ± 33.62). The highest willingness was exhibited among those with educational levels of master’s and PhD (65.91 ± 30.96), followed by students (64.01 ± 31.01). The distribution of organ donation willingness across different groups is presented in Figure 3, which provides further information about the relatively high and low willingness, particularly the high density of high willingness in groups of non-agricultural hukou, high educational levels, and students, and the high density of low willingness in groups of older adults, widowed participants, and agricultural hukou. The factors associated with willingness toward organ donation based on GLM are shown in Table 2. We sequentially included individual characteristics (Model 1), family-level factors (Model 2), and social-level factors (Model 3) in the models, with model 3 revealing the best fitness, with the lowest AIC and BIC. In model 3, age (β = −1.65, $95\%$ confidence interval [CI] = −2.77 to −0.53), and family health (β = −0.19, $95\%$ CI = −0.32 to −0.06) were negatively associated with willingness. Females (β = 2.08, $95\%$ CI = 0.89–3.28) and students (β = 4.21, $95\%$ CI = 2.02–6.41) had higher willingness than males, unmarried and currently occupied participants. Participants who had educational levels of college and bachelor’s (β = 5.64, $95\%$ CI = 2.04–9.23) and master’s and PhD (β = 9.98, $95\%$ CI = 5.72–14.24) had higher willingness than those who were illiterate. For personality, conscientiousness (β = −0.84, $95\%$ CI = −1.29 to −0.40) was negatively associated with willingness, whereas openness (β = 1.04, $95\%$ CI = 0.61–1.47) was positively associated with willingness. Participants with agricultural hukou (β = −2.28, $95\%$ CI = −3.59 to −0.96) were negatively associated with willingness compared to those with non-agricultural hukou. Health literacy (β = 0.31, $95\%$ CI = 0.19–0.44), perceived social support (β = 0.12, $95\%$ CI = 0.06–0.19), and media utilisation (β = 0.59, $95\%$ CI = 0.46–0.72) were also positively associated with willingness. The results of QR, which estimated the different effect sizes of each factor on different willingness segments, are revealed in Table 3. It was intriguing that for educational levels of college and bachelor and master’s and PhD, the personality of openness, health literacy, and media utilisation were positively associated with organ donation willingness at all percentiles. Student occupation was positively associated with willingness only at the 50th percentile. The personality of conscientiousness and agricultural hukou were negatively associated with willingness at the 25th and 50th percentiles. Females, secondary education and college, and a bachelor’s were associated with higher willingness at the 50th and 75th percentiles. Social support was associated with higher willingness at the 25th and 50th percentiles. ## 4. Discussion In the present study, factors associated with a willingness toward organ donation in China were comprehensively identified and described using a social–ecological framework, which is derived from individuals, families, communities, and the whole society. Such an analytical framework provides a good reference for improving national organ donation willingness in the future, i.e., systematic efforts need to be made from multiple levels and multiple dimensions. In addition, this study provides a more systematic idea for studying organ donation willingness and identified more factors that could be manipulated through interventions compared with previous studies. In this study, age, male sex, lower educational levels, and agricultural hukou were negatively associated with a willingness toward organ donation. These results are consistent with those of several previous studies [39,40]. Older populations and males are not that willing to donate organs due to traditional family values [41]. Influenced by traditional concepts, Chinese people believe that keeping the body intact shows their respect for the deceased. In addition, owing to the lack of death education, Chinese people are very taboo on the topic of ‘death’, so few people will talk about what they will do after death while they are alive. Urban residents are proposed in wider and more abundant information than rural residents, which could lead to a gap in knowledge about organ donation between urban and rural residents [41]. Similarly, the willingness to donate organs develops with an increase in education level; individuals with higher education levels are more likely to attend organ donation education programmes than those with lower education levels [42]. Even if these factors are challenging to intervene with, we can adopt differentiated guidance and publicity methods to improve the willingness toward organ donation in the future. In addition, the subsequent focus of organ donation publicity should be placed on death education, which means introducing the concept of organ donation into death education. Furthermore, integrating the idea of organ donation into hospice care is of great significance in nudging the penetration of organ donation in China. Personality is associated with donating willingness and behaviour. Previous studies indicated that the donors frequently make a very conscious choice consonant with their personality, ranging from autonomous, nonconformist, headstrong, and self-determined to a prosocial attitude. In other words, the donors make a symbolic statement following their self-identity [16]. It is intriguing that different dimensions of personality play different roles in our study, which indicated that openness was associated with higher willingness, but conscientiousness was negatively correlated with willingness. Openness describes the individual acceptance of new things and multiple orientations. In China, organ donation is often irrelevant to most people’s daily lives. Therefore, the more open to new things, the more organ donation can be accepted. Nevertheless, in the cultural background of China, it is important to keep the body intact and not be disfigured [43]. Some residents may think that organ donation, which makes their bodies not intact, may be disagreed with by their family members and, thus, be considered a sign of irresponsibility to their families. Therefore, conscientiousness is negatively related to organ donation. This can also explain why family health, which is closely related to family members’ conscientiousness, was negatively correlated with willingness. The higher the level of family health, the higher the degree of dependence and trust among family members, that is, the greater the influence of family on individual willingness. Health literacy is associated with knowledge and attitude toward donation, including how and why to donate, as well as the significance of donation. This has also been supported by previous studies [44,45]. Additionally, in China, health literacy affects the ability to make autonomous decisions related to health and may reduce the influence of traditional beliefs [46]. Therefore, improving public health literacy in several ways may be effective in improving the willingness toward organ donation. The positive association between social support and willingness toward organ donation could be explained by the following viewpoints. First, social support is associated with rapid information diffusion and can improve organ donation awareness [47,48]. Second, social support could solve the concerns about organ donation. Previous studies have indicated that ambivalence is common among donor candidates; however, instrumental social support can mediate the negative effects of donation-related concerns. Recommendations include providing appropriate social support to minimise donation-related concerns, thereby reducing the ambivalence of donation candidates [49,50]. Social media has been proven to be an important publicity channel to improve the public willingness toward organ donation, which is also supported by previous studies [51,52]. Particularly, with the popularisation of information technology, network media have been integrated into every aspect of life and became significant sources of information. This should also be an important means to publicise the social significance of organ donation and related processes and policies. ## Limitations Although this study used a nationally representative database, it had several limitations. First, this study suffered from the inherent flaws of a cross-sectional study, and causal effects could not be obtained. Second, we conducted the survey through respondents retrospectively completing a questionnaire, which may be subjected to recall bias. Third, the survey mainly concentrated on the health indicators of Chinese populations. Therefore, factors associated with organ donation willingness may not be well-rounded. More factors should be explored in future studies. Fourth, we applied a social–ecological framework to identify factors from a more systematic perspective. However, our data were not multi-level; therefore, they could not perfectly support the application of the socio–ecological model in the field of organ donation. Fifth, the study participants were asked to indicate their willingness toward organ donation on a scale from 0 to 100, which may be an overly fine-grained scale, even though we conducted quantile regressions, which may overcome this limitation to a certain extent. In light of these limitations, subsequent prospective studies are needed to examine the most effective measures to improve the public willingness toward organ donation. ## 5. Conclusions In China, residents’ willingness toward organ donation needs to be improved. Improving the willingness requires taking measures from multiple levels, such as the individual, families, and society. More targeted and diversified publicity, education, and guidance for different types of individuals should be adopted. Meanwhile, social support needs to be strengthened. 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--- title: 'Traditional Mexican Food: Phenolic Content and Public Health Relationship' authors: - Julia María Alatorre-Cruz - Ricardo Carreño-López - Graciela Catalina Alatorre-Cruz - Leslie Janiret Paredes-Esquivel - Yair Olovaldo Santiago-Saenz - Adriana Nieva-Vázquez journal: Foods year: 2023 pmcid: PMC10048498 doi: 10.3390/foods12061233 license: CC BY 4.0 --- # Traditional Mexican Food: Phenolic Content and Public Health Relationship ## Abstract Phenolic compounds have a positive effect on obesity, diabetes, and cardiovascular diseases because of their antioxidant and anti-inflammatory capacity. The prevalence of these diseases has increased in the last years in the Mexican population. Therefore, the Mexican diet must be assessed as provider of phenolic compounds. To assess this, a survey of phenolic compound intake was validated and applicated to 973 adults (798 females) between 18 and 79 years old. We compared the phenolic compound intake of 324 participants with more diseases (239 females) and 649 participants with healthier condition (559 females). The groups differed in sex, age, and scholarship. Males, older participants, and those with lower schooling reported suffering from more diseases. Regarding phenolic compound intake analyses, the participants with healthier conditions displayed a higher phenolic compound intake than the other group in all foods assessed. In addition, the regression model showed that the phenolic compounds intake of Mexican dishes, such as arroz con frijol or enchiladas, positively affected health status, suggesting that this traditional food is beneficial for the participant’s health condition. However, the weight effect of PCI was different for each disease. We conclude that, although PCI of Mexican food positively affects health conditions, this effect depends on sex, age, and participants’ diseases. ## 1. Introduction In recent years, nutrition science has focused on counteracting nutrient deficiency and some diseases by identifying active-food components. Diet offers the possibility to improve the subject’s health conditions by using these components or functional food [1]. Functional food is part of a habitual diet, but it has special biological properties, such as phenolic compounds (PC). PCs are a diverse group of plant micronutrients [1], some of which modulate physiological and molecular pathways involved in energy metabolism [2]. They can act by different mechanisms, the most important of them are conducted by anti-inflammatory, antioxidant activities, and antiallergic [3]. The anti-inflammatory activity entails the activation of sirtuins; induction of Nrf2 via inhibition of microglial activation; and suppression of proinflammatory mediators (TNF-α, prostaglandins, C-reactive protein levels, interleukins IL-1α, IL-1β, IL6, IL8, and intercellular adhesion molecule-1), while the antioxidant activity captures unpaired electrons present in free radicals, interruption of autoxidation chain reactions, deactivation of singlet oxygen, mitigation of nitrosative stress, activation of antioxidant enzymes, or inhibition of oxidative enzymes [1]. PCs can be divided into flavonoid and non-flavonoid derivatives [2,4]. Flavonoid is the most important PC class and includes more than 5000 compounds [5] that seem to impact human health positively. Multiple authors have explained the underlying mechanism of PC effect on human health. Oxidative stress (OS) and inflammation triggered by increased OS are the cause of many chronic diseases in reactive-oxygen species (ROS) [6]. Although OS is part of normal cellular conditions (e.g., mitochondrial respiratory chain), this could damage other biological molecules [7]. Therefore, the human body must trigger antioxidant mechanisms to prevent cellular injury. Interestingly, flavonoids have a protective effect because they counteract OS by using at least four mechanisms; [1] reduction of free radical formation, [2] protection of α-tocopherol in Low Density Lipoprotein (LDL) from oxidation, [3] regeneration of oxidized α -tocopherol, and [4] chelation of metal ions such as Fe and Cu, preventing the consequent production of new free radical [1]. Moreover, recent studies explain how PC positively affect certain illnesses, such as obesity, diabetes, cardiovascular diseases, thrombocytopenia, or metabolic syndrome [1,8,9]. Several common features characterize these pathologies; among them, we can highlight the redox balance and a notable inflammatory response that strongly alters the biochemical and functional characteristics of the affected tissues. Regarding obesity, it results from energy imbalance, increasing adipose tissue [10,11]. PC intake reduce the weight gain by reducing adipose tissue using lipolysis [1,10,11,12,13,14]. Others studies report that subjects with obesity show a low level of antioxidant enzymes (e.g., catalase, glutathione peroxidase, and glutathione reductase), which generates lower antioxidant capacity [11,13,15,16]. Therefore, PC intake might also increase their antioxidant capacity through free-radical scavenging activity. Significant evidence from epidemiological investigations showed that dietary PCs might manage and prevent type 2 diabetes (T2D) [1]. T2D disease consists of metabolism disturbances characterized by chronic hyperglycemia or impairment of insulin secretion or action [17]. Hyperglycemia increases ROS production by glucose auto-oxidation and nonenzymatic glycation processes, affecting the normal function of proteins and lipids. Therefore, high PC intake has been highly recommended for this disease management to counteract ROS [1]. In addition, PCs from coffee, guava tea, whortleberry, olive oil, propolis, chocolate, red wine, grape seed, and cocoa have been reported to show antidiabetic effects in T2D patients through increasing glucose metabolism and improving vascular function, as well as reducing insulin resistance and the HbA1c level [1]. On the other hand, cardiovascular diseases entail problems in the heart and blood vessels. Patients frequently show increased blood pressure, evidencing a disorder in the circulatory system [18]. PC intake improves endothelial dysfunction [19,20], decreases vascular OS [20,21], inhibits platelet aggregation and the oxidation of LDL, and reduces blood pressure, evidencing their counteracting capacity on cardiovascular disease symptoms [19,21]. Most interventions for these diseases have been focused on changing the patient’s diet, but they have failed to conform to eating habits associated with culture. It is well known that there are multiple dietary habits worldwide, many of which have been described as a good source of PC. For example, in the Western diet, fruit, vegetables, tea, wine, and cocoa products provide a mean intake of flavonoids of around 250 mg/d for the United States (US) adults [4,22]. Greek and Korean populations have similar flavonoid intake to the US population, with a mean between 250 and 320 mg/d. In contrast, the British population has a PC intake greater than other countries because they consume around 1000 mg/d [22,23,24]. However, there is no data about the PC intake of Latino American populations, even when these have outstanding gastronomic richness. Traditional Mexican food is characterized by using grains, tubers, legumes, vegetables, and spices [25,26], most of which are rich in PC [27]. However, the Mexican diet has changed over the last decades because traditional food has been replaced with ultra-processed food with high-caloric values [28,29]. Moreover, some vegetables and fruits are preferably consumed after some kind of processing, affecting the quantity, quality, and bioavailability of the PCs [1]. Along with changes, diseases associated with eating habits have increased by more than $27\%$ in the Mexican population [24,30]. Over the last two years, the National Health and Nutrition Survey reports that $72.2\%$ of the Mexican population is overweight or obese [28,29]. In a posterior survey, they added that $30.2\%$ of the adults have hypertension, and $15.6\%$ suffer from diabetes. In addition, there is a high prevalence of dyslipidemia diseases, with $49\%$ of the adult population suffering from high levels of triglycerides and $54.3\%$ with high cholesterol levels [24,30]. Health reports have also correlated the increased prevalence of these diseases with a decreased intake of fruits and vegetables. In this study, we expected to find a better health condition in participants with higher PC intake. Given that traditional Mexican food is not ultra-processed, and this seems to contain high levels of PC [25,26], we would expect that participants with a higher intake of beverages or Mexican dishes would show better health conditions than those with a lower intake. ## 2.1. Participants Nine hundred and seventy-three adults (798 females; 175 males) between 18 and 79 years old were enrolled in this study. They were ethnic Mexican and native Spanish speakers and had at least nine years of education. In this study, health condition was obtained from volunteers through a survey; therefore, they were not explored by a physician. All participants were informed of their rights and provided written informed consent for participation in the study. This research was carried out ethically and was approved by the Benemérita Universidad Autónoma de Puebla. ## 2.2. Procedure This is a cross-sectional study with a non-probabilistic sample. We obtained the data from a self-administered food consumption survey, which was directed at the open public. In the survey, we requested the food intake frequency with a high level of phenolic compound [1]. ## 2.2.1. Survey of Food Consumption with a High Level of Phenolic Compounds The survey assesses participants’ intake of food and health condition. This entails one hundred and twenty-four items distributed in nine sections: [1] identification data; [2] anthropometric data; [3] medical records; and [4] food intake frequency with a high level of PCs in the last month: fruits, vegetables, cereals, legumes, spices, beverages, and Mexican dishes (See Table A1). The survey was posted on social media (i.e., Facebook) or via WhatsApp or email. ## 2.2.2. Validation The survey was applied to the pilot group of 32 subjects, who reported a complete understanding of the items. They also reported being comfortable with all items and completion times. The pilot group responded to $100\%$ of the questions. The statistical analysis for the survey’s validation was performed using a chi-square test. The factor analysis technique assessed the items with an orthogonal rotation “Varimax”. In this analysis, the factor weight of each item was at least 0.4 for all items. We also measured internal consistency of each item for each factor, exploring their reliability using Cronbach’s alpha (0.96). ## 2.3.1. Clustering Analysis A K-means clustering was performed to determine the participant’s health-condition level. The variables included were the body mass index (BMI); number of diseases (diabetes, hypertension, hypercholesterolemia, hypertriglyceridemia, kidney disease, and fatty liver); and number of gastrointestinal symptoms or illnesses: constipation, gastritis, irritable bowel syndrome (IBS), peptic ulcer, bacterial overgrowth syndrome (BOS), and ulcerative colitis (UC). The clustering analyses resulted in 649 participants with less diseases (LD) and 324 with more diseases (MD). ## 2.3.2. Comparisons between LD and MD Groups Demographic data. A chi-square test was used to compare groups for sex and age distribution. The groups were also compared for the scholarship, BMI, number of dietary supplements, number of diseases, and number of gastrointestinal symptoms or illnesses using independent t-tests. Phenolic compounds intake. We calculated the frequency of PC intake for each participant. For food intake frequency, we considered the number of times the food was consumed and its grams in the last month. We calculated the frequency of phenolic-compounds intake (PCI) using the food biochemistry composition reported in multiple papers (See Table 1), and selected only papers describing total phenolic compounds (TPC) mg gallic acid equivalent (GAE)/100 g. Given that TPC’s composition varies by multiple conditions, we computed the TPC’s average for each food, considering the variations (i.e., fruits, vegetables, cereals, legumes, seeds, spices, and beverages). Then, for each participant as follows: PCI = [(food-intake frequency × TPC)/participant’s BMI]. For Mexican dishes, we added the TPC of each recipe’s ingredient, then recalculated TPC for an individual portion of each recipe (TPCr). Given that some recipes of Mexican dishes are varied, we averaged the TPC of individual portions between recipes (See Table A2). For the analyses, we calculated phenolic compounds intake of recipe (PCIr) for each participant as follows:PCIr = [(food-intake frequency × Average of TPCr)/participant’s BMI]. ANOVAs were performed for each nutritional group (i.e., fruits, vegetables, cereals, seeds, spices, beverages, and Mexican dishes), and the sex or age category was considered as a between-subject factor, while PCI/PCIr was included as a within-subjects factor. Two-way ANOVAs were performed for each nutritional group. Group (i.e., LD and MD) and the sex or age category was considered as a between-subject factor to observe the sex and age category effects, and PCI or PCIr was included as a within-subjects factor. Data were analyzed using SPSS Statistics 21. Greenhouse–Geisser corrections were made for violations of sphericity when the numerator was greater than 1, p-values resulting from a set of comparisons were corrected by the false discovery rate method (FDR). We report results surviving FDR correction (p values < 0.05). ## 2.3.3. Risk of Developing Disease Regression analyses were performed to identify the association between the participant’ diseases, PCI or PCr, and other variables. Linear regression was performed using as a dependent variable our cluster (i.e., LD and MD groups), and PCI, PCIr, sex, age, and scholarship were also included as independent variables. Linear regression was also performed per disease (i.e., diabetes, hypertension, hypercholesterolemia, hypertriglyceridemia, kidney disease, fatty liver, and obesity), including as the dependent variable the presence or absence of disease, and PCI, PCIr, sex, age, scholarship as independent variables. The linear regression analyses include multiple linear backward regressions to find a reduced model that best explains the data. A p-value < 0.05 was considered statistically significant in all analyses. ## 3.1. Demographic Results Differences between groups were also observed in the sex (Xi [1] 22.4, $p \leq 0.001$) and age category distributions (Xi [4] 34.2, $p \leq 0.001$). The men’s distribution was greater than expected in the MD group (See Table 2). In contrast, in the age category, the groups differed in the subset of participants between 18 and 29 years old. A greater number of participants than the expected count was observed for the LD group, while the inverse pattern was observed for the MD group. The groups were significantly different in scholarship (t [971] 3.7, $p \leq 0.001$). The LD group had greater years of schooling than the MD group (LD, Mean (M) = 15.4; MD, $M = 16.0$; Cohen’s $d = 0.2$). They also differ in BMI (t [971] −32.3, $p \leq 0.001$), with the LD group displaying lower BMI than the other group (LD, $M = 22.4$; MD, $M = 30.1$; Cohen’s d = −2.2). In addition, the groups did not differ in the number of dietary supplements consumed (t [971] 1.7, $$p \leq 0.1$$; LD, $M = 0.8$; MD, $M = 0.7$). As we expected, the MD group also showed greater numbers of diseases than the LD group (t[971] −7.1, $p \leq 0.001$, Cohen’s d = −0.4; LD, $M = 0.1$; MD, $M = 0.4$). However, they did not differ in the number of gastrointestinal diseases or symptoms (t [971] 1.2, $$p \leq 0.2$$) (See Table 3). ## 3.2. Phenolic Compounds Intake Results Regardless of the level of the health condition, participants had a greater intake of apples ($67.2\%$), oranges ($56.7\%$), tomatoes ($86.7\%$), white onions ($79.9\%$), chilies ($71.5\%$), carrots ($71.5\%$), lettuce ($70.3\%$), nopal ($58.2\%$), potatoes ($57.7\%$), corn ($74.2\%$), rice ($67.8\%$), oatmeal ($56.5\%$), and beans ($65.9\%$). The beverages more frequently consumed were coffee ($65.1\%$) and hibiscus water ($54.6\%$), while the Mexican dishes more consumed were salsas verdes ($61.9\%$), followed by salsas rojas ($57.5\%$) (see Figure 1A). The sex groups differed in PCI of some foods. Females had a higher PCI of cereals, legumes, seeds, and beverages than males, while males showed a higher PCI of fruits. The age groups also differed in PCI. The participants between 40 and 49 years of age had a higher PCI of vegetables, those between 18 and 29 years old showed higher PCI of legumes, while the participants between 50 and 59 years of age had a higher PCI of spices than the other groups (see Table 4). ## 3.2.1. Phenolic Compounds Intake: Group and Sex Distribution Fruits. The groups were different in PCI of fruits, and a main effect of the group was found (F [1, 960] = 82.6, $p \leq 0.001$, η2p = 0.08, ε = 0.07); this evidenced a higher PCI of fruits for LD than MD groups (Mean difference, (Md) = 30.1, $p \leq 0.001$; LD, $M = 139.3$, MD, $M = 109.2$). A significant group by fruit interaction was also observed (F [15, 960] = 51.0, $p \leq 0.001$, η2p = 0.05, ε = 0.07). The post-hoc tests confirmed that the LD group had a higher PCI of each fruit than the MD group (Each fruit, $p \leq 0.01$). No significant group by fruits by sex interaction was found (See Figure 1B). Vegetables. A main effect of the group was found (F [1, 960] = 77.6, $p \leq 0.001$, η2p = 0.07, ε = 0.07). The LD group showed higher PCI of vegetables than the MD group (Md = 9.2, $p \leq 0.001$; LD, $M = 45.62$, MD, $M = 36.45$). A significant group by vegetable interaction was also found (F [17, 960] = 43.9, $p \leq 0.001$, η2p = 0.04, ε = 0.06). The post-hoc tests revealed that the LD group had a higher PCI of each vegetable than the MD group (each vegetable, $p \leq 0.001$). A significant group by vegetables by sex was also observed (F [17, 960] = 4.46, $$p \leq 0.03$$, η2p = 0.005, ε = 0.06). However, the post-hoc test showed that female or male groups did not differ in PCI of vegetables between LD and MD groups (See Figure 1B). Cereals. As is shown in Figure 1B, a main effect of group in PCI of cereals (F [1, 960] = 107.5, $p \leq 0.001$, η2p = 0.1, ε = 0.4) was observed. As we hypothesized the LD group had a higher PCI of cereals than the MD group (Md = 7.25, $p \leq 0.001$; LD, $M = 35.69$, MD, $M = 28.44$). A significant group by cereal interaction was observed (F [6, 960] = 59.22, $p \leq 0.001$, η2p = 0.06, ε = 0.4). The post-hoc tests confirmed a higher PCI for each cereal for LD than the MD group (Each cereal, $p \leq 0.001$). We also found a significant group by cereal by sex interaction (F [6, 960] = 5.68, $p \leq 0.001$, η2p = 0.006, ε = 0.4). Although, the pair comparisons showed that PCIs of cereals differed between LD and MD groups, with a greater PCIs for women that belonged to the LD group. The men’s subgroups, the LD and MD groups, did not show differences in PCIs of flaxseed, wheat, and millet (Flaxseed, Md = 4.8, $$p \leq 0.1$$; wheat, Md = 0.8, $$p \leq 0.2$$; millet, Md = 0.2, $$p \leq 1.0$$). Legumes. The groups were also different in the PCI of this nutritional group. A significant main effect of group (F [1, 960] = 150.4, $p \leq 0.001$, η2p = 0.1, ε = 0.6) evinced that LD group showed a higher PCI of legumes than the MD group (Md = 62.0, $p \leq 0.001$; LD, $M = 268.6$, MD, $M = 206.5$) (See Figure 1B). We also found a significant group by legume interaction (F [3, 960] = 88.3, $p \leq 0.001$, η2p = 0.08, ε = 0.6). The LD group showed a higher PCI of all legumes than the other group (Each legume, $p \leq 0.001$). In addition, a significant group by legume by sex was also observed (F [3, 960] = 3.6, $$p \leq 0.03$$, η2p = 0.004, ε = 0.6). Although the comparisons favored the LD group for the female subgroup, only for the male subgroup, the PCI of soybeans was not different between groups with a different health condition (Soybean, Md = 2.2, $$p \leq 0.1$$). Seeds. A main effect of the group was also found in this nutritional group (F [1, 960] = 104.8, $p \leq 0.001$, η2p = 1.0, ε = 0.3). The LD group showed a higher PCI of seeds than the other group (Md = 38.6, $p \leq 0.001$; LD, $M = 180.7$, MD, $M = 142.1$) (See Figure 1B). A significant group by seed was also observed (F [4, 960] = 70.6, $p \leq 0.001$, η2p = 0.07, ε = 0.3). The post-hoc tests evidenced that the LD group displayed higher PCI of seeds than the MD group (Each seed, $p \leq 0.001$). No significant group by seed by sex interaction was found. Spices. The groups were different in the PCI of spices (F [1, 960] = 62.3, $p \leq 0.001$, η2p = 0.06, ε = 0.1). The LD group had a higher PCI of spices than the MD group (Md = 12.3, $p \leq 0.001$; LD, $M = 67.8$, MD, $M = 55.6$) (See Figure 1B). A significant group by spice interaction confirmed that the LD group had a higher PCI than the other group in each spice [F [19, 960] = 14.5, $p \leq 0.001$, η2p = 0.01, ε = 0.1]. In addition, a significant group by spices by sex interaction was also observed (F [19, 960] = 5.0, $$p \leq 0.002$$, η2p = 0.05, ε = 0.1). Only for male group multiple PCI of spices were no different between the LD and MD groups (Marjoram, Md = 13.0, $$p \leq 0.5$$; achiote (annatto), Md = 0.6, $$p \leq 0.1$$; Chaya, Md = 0.1, $$p \leq 1.0$$; fennel, Md = −0.2, $$p \leq 0.7$$; linden, Md = 2.0, $$p \leq 0.8$$; saffron, Md = 1.7, $$p \leq 0.7$$; Mexican pepper leaf, Md = 4.9, $$p \leq 0.06$$; papalo, Md = 6.2, $$p \leq 0.2$$). The remaining comparisons were significant in favor of the LD group regardless of sex. Beverages. The groups with different health condition differed in PCI of beverages (F [1, 960] = 111.46, $p \leq 0.001$, η2p = 0.3, ε = 0.1). The LD groups showed a higher PCI of beverages than the other group (Md = 145.24, $p \leq 0.001$; LD, $M = 759.26$, MD, $M = 614.01$) (See Figure 1B). A significant group by beverages was also found (F [4, 960] = 45.94, $p \leq 0.001$, η2p = 0.3, ε = 0.5). The post-hoc tests evidenced that the LD group had a greater PCI in all beverages than the MD group ($p \leq 0.01$). No significant group by beverage by sex was observed. Mexican dishes. A main effect of the group was observed (F [1, 960] = 196.6, $p \leq 0.001$, η2p = 0.2, ε = 0.4). The LD group displayed a greater PCI of Mexican dishes than the other group (Md = 47.7, $p \leq 0.001$; LD, $M = 204.6$, MD, $M = 156.9$) (See Figure 1B). A significant group by Mexican dishes interaction (F [12, 960] = 47.6, $p \leq 0.001$, η2p = 0.04, ε = 0.4) confirmed that the LD group had a higher PCI of each Mexican dish ($p \leq 0.001$). No significant group by beverage by sex was observed. ## 3.2.2. Phenolic Compounds Intake: Group and Age Distribution Although, no significant group by age category interaction was observed for any comparison, but a significant main effect of group (i.e., LD vs. MD) was observed for each comparison with LD showed higher PCI than MD group: fruits (F [4, 954] = 55.02, $p \leq 0.001$, η2p = 0.05, ε = 0.07 (Md = 28,57, $p \leq 0.001$; LD, $M = 134.43$, MD, $M = 105.85$)), vegetables (F [4, 954] = 108.44, $p \leq 0.001$, η2p = 0.10, ε = 0.06 (Md = 12.26, $p \leq 0.001$; LD, $M = 48.90$, MD, $M = 36.64$)), cereals (F [4, 954] = 87.35, $p \leq 0.001$, η2p = 0.08, ε = 0.41 (Md = 7.56, $p \leq 0.001$; LD, $M = 34.97$, MD, $M = 27.41$)), legumes (F [4, 954] = 129.57, $p \leq 0.001$, η2p = 0.12, ε = 0.56 (Md = 66.51, $p \leq 0.001$; LD, $M = 270.57$, MD, $M = 204.06$)), seeds (F [4, 954] = 104.59, $p \leq 0.001$, η2p = 0.10, ε = 0.33 (Md = 44.40, $p \leq 0.001$; LD, $M = 186.51$, MD, $M = 142.11$)), spices (F [4, 954] = 16.58, $p \leq 0.001$, η2p = 0.07, ε = 0.15 (Md = 15.21, $p \leq 0.001$; LD, $M = 71.43$, MD, $M = 56.22$)), beverages (F [4, 954] = 97.46, $p \leq 0.001$, η2p = 0.3, ε = 0.9 (Md = 156.30, $p \leq 0.001$; LD, $M = 767.05$, MD, $M = 610.75$)), and Mexican dishes spices (F [4, 954] = 156.49, $p \leq 0.001$, η2p = 0.14, ε = 0.37 (Md = 49.15, $p \leq 0.001$; LD, $M = 204.92$, MD, $M = 155.76$)). ## 3.3. Risk of Developing Diseases As shown in Table 5, two regression models displayed an R2 higher than 0.4, the remaining regressions had a R2 of 0.1 (See Table A3). The stronger regressions included as independent variables our cluster (i.e., LD and MD groups) and obesity disease. In the regression model, which included our cluster, we found that high PCI of tomato, garlic, lettuce, corn, grape, wine, romeritos, arroz con frijoles, and scholarship predicted a smaller number of diseases. In contrast, older age and higher PCI of wheat and cranberry predicted a higher likelihood of suffering from a disease (See Figure 2). In the regression model, which included obesity as an independent variable, PCI of tomato, corn, garlic, chamomile tea, coffee, grape, Swiss chard, enchiladas, and wine predicted the absence of obesity, while the PCI of plum and oregano predicted the presence of this disease (For further information see Table A4 and Table A5). ## 4. Discussion This study examined the PCI and how the Mexican diet was associated with participants’ health condition. We expected to find more diseases in participants with lower PCI based on previous literature. Given that traditional Mexican food is not ultra-processed and contains high levels of PC [25,26], we expected better health condition in participants with a higher intake of beverages or Mexican dishes. Our hypotheses partially agreed with our results because food with high PC was associated with better health condition; however, the consumption of beverages and Mexican dishes is lower than our expectations. ## 4.1. Demographic Data Our statistical analyses revealed that sex, age, and scholarship variables seem to play a role in the presence or absence of diseases in our Mexican cohort. Males suffer more diseases than females even when we had more participation from females in our sample However, this result matched previous studies reporting that males have lower life expectancy than females in Mexico [123]. A recent study explained that males do not usually go to the doctor when they have symptoms of illness, making them a vulnerable population to develop multiple diseases. As the prior literature has reported, our participants over 29 years had more diseases than their younger pairs. This result confirmed that older age increases the likelihood of suffering a disease due to the natural deterioration of the human body [124]. The scholarship also seems to influence disease development; those participants with higher scholarship reporting less illnesses. Jun et al. [ 2016] described that participants with higher scholarship usually include dietary supplements. In our study, even when we did not explore the relationship between scholarship and number of dietary supplements, we suggest that our participants with high scholarships could have nutritional habits such as Jun et al. ’s participants. In addition, our demographic interpretations should be carried out carefully because our sample is not representative of the Mexican population. ## 4.2. Frequency of Food Consumption Mexican eating habits of fruits matched the US and Korean populations in apple and orange consumption [24,30], and it was also consistent with the eating habits of Asian populations, mainly in chilies and rice consumption [24], our sample’s data also matched European Union in wheat and potato intake [125]. We suggest that Mexican’s eating habits match other populations because of the increased influence of other countries via social media and economic globalization. Previous studies reported that the Mexican population has lower consumption of without or low sugar beverages and traditional Mexican dishes than those described in the last decades [28,29], suggesting higher consumption of fast or ultra-preprocessed food. In our study, more than $50\%$ of our sample consumed only two traditional Mexican dishes (i.e., salsas verdes and salsas rojas), and the most consumed beverage was not autochthonous from Mexico (e.g., coffee). Moreover, their consumption was similar to the eating habits of Latin American and Asian populations [1]. We could suggest that our data confirm the alarming changes previously reported in the Mexican diet. However, we also found that beans, corn (mainly tortilla), and nopal intake remained preserved in the Mexican eating habits, with a higher intake than in other countries [1]. These foods are essential in other autochthonous Mexican dishes such as “tacos”. Therefore, we would suggest exploring other Mexican dishes in future studies. ## 4.3. Comparisons between LD and MD Groups The LD and MD differed in the PCI of all food groups, with LD showing a higher PCI than MD groups. These results matched our hypothesis and previous studies reporting a positive effect of PC on different diseases [22,23,24]. Vegetables and fruits have been considered great providers of PC [1]. Then, we expected a higher intake for LD group. The vegetables consumed more frequently by our sample, such as tomato and lettuce, were good predictors of a smaller number of diseases, which matched our hypothesis. However, even when the PCI of fruits was higher for the LD group, we found that low percentage of our sample consumed fruits. Moreover, the fruits consumed by more than $50\%$ of our sample were not variables predictors in the regression analyses (See Figure 2). The lower PCI of fruits observed in this sample might be part of the problem in the health status of the Mexican population; we suggest that they might be replacing fruit with high-caloric snacks. Given that, in this study, we did not explore that kind of food, this statement would require further research. We also found that cereals were consumed more by LD than MD groups. Corn is the most consumed by our sample; this cereal contains PC, which seems to decrease the risk of developing a chronic illness, such as diabetes, and cardiovascular diseases. Corn contains a molecule called lignin, which is the main component of dietary fiber. In addition, it has two functions: [1] inhibits enzymatic activity associated with the production of radical anion superoxide and [2] blocks the growth and viability of cancerogenic cells [126]. Then, cereals, particularly corn consumption, seems to affect health status positively. This statement is consistent with the regression analysis results, in which high corn intake was associated with a lower number of diseases in our sample. Our statistical analyses also showed that LD had a higher PCI of legumes than MD groups. Previous study report that the legumes have a high biologic value because they contain essential amino acids and PC. These can modify the basal physiological function within the intestinal microenvironment affecting the microbiota and epithelial barrier, improving metabolic and gastrointestinal health, enhancing resistance to colonization by pathogens, and exerting an impact on the gut microbiota. They regulate metabolic stability and membrane transport in the intestine, thus improving bioavailability. These actions decrease the severity of diseases associated with the intestine due to their chemopreventive effects [127]. In our study, the analyses revealed no differences between LD and MD groups in gastrointestinal symptoms and diseases. We suggest that the positive effect of legumes on the digestive system might have been hampered by the poor variety of legumes consumed by our sample. They mainly consumed beans. On the other hand, legumes also contribute to glycemic control and protein anabolism [127]. LD and MD differed in the presence of diseases, between them diabetes mellitus. Interestingly, the Mexican dish “Arroz con frijoles” contains as a main ingredient bean, and its high intake was associated with fewer diseases. A recent study reported that beans improve postprandial glycemic response and glycated hemoglobin (HbA1c) by inhibiting α-amylase, and maltase. Therefore, beans have been considered an ideal food for managing blood glucose and insulin resistance [128]. Our results suggest that a high bean intake positively affects glycemic control in our sample. We also found that LD and MD groups differed in the PCI of seeds, with a higher PCI for LD group. The seeds are food groups that contain PC and modulator molecules, such as essential fatty acids, which protect the digestive tract and allow appropriate maintaining in lipid metabolism (i.e., a decrease of triglycerides, LDL, cholesterol, very low density lipoprotein (VLDL), regulation of markers of platelets such as a reduction of endothelial adhesion, platelet aggregation, decrease of inflammation markers related to arachidonic acid, modulating the production of prostaglandins, leukotrienes, decrease of cyclooxygenases, reduction of oxidation of molecules such as LDL, deoxyribonucleic acid (DNA), reduce the production of ROS, increase reduced glutathione (GSH), glutathione peroxidase (GSH-Px), and plasma antioxidant capacity) [129]. In our study, the seeds were not consumed by more than $50\%$ of our sample, which might be explained by their high cost and scarce availability in the market. This fact might justify that PCI of any seed was a predictor variable in our regression model, suggesting that this food did not reach a significant effect on health status because of its low consumption. A similar situation was found in the PCI of spices, even when they have many properties such as digestive stimulant action, antimicrobial, anti-inflammatory, antimutagenic, anticarcinogenic potential, and antioxidant capacities [130]. They were not consumed by more than $50\%$ of our sample; therefore, they did not reach a place between predictor variables. We suggest that most of the participants in our study did not know whether their meals had spices. The participants’ report might be biased by their lack of knowledge of the dish’s ingredients. As we expected the PCI of beverages were higher for LD than MD groups. Particularly, coffee was the beverage more consumed by our participants. This beverage contains chlorogenic acids (CGA), which have several effects on health conditions; between them, they are hypoglycemic, antiviral, and hepatoprotective and have antispasmodic activities [131]. In addition, a daily coffee intake of 2.5 cups has beneficial effects on endothelial function and vascular smooth muscle function in patients with hypertension [1], while another study reported that the level of coffee intake was not associated with gastrointestinal diseases and gastric cancer [1]. Moreover, in prior meta-analyses, the effect of coffee intake on obesity and chronic diseases is still controversial. However, a positive association between coffee intake, BMI, and abdominal obesity has been reported, suggesting several biological mechanisms against obesity triggered by biologically active compounds in coffee, such as CGA, caffeine, trigonelline, and magnesium [1]. In a study using an animal model, they reported that supplementation with CGA was associated with a body-weight reduction, a decrease of visceral fat mass, and lower triglyceride content in adipose tissue in high-fat-fed mice [1].In an in vitro study, trigonelline inhibited adipocyte proliferation and lipid accumulation in differentiating adipocytes [132]. In our study, the PCI of coffee was a predictor variable, that is, high PCI of coffee was associated with less number of diseases and the absence of suffering obesity disease, which matched previously described [1]. Although wine consumption was low in our sample, we found a relationship between fewer diseases or healthier weight status and wine intake. This result might be supported by recent studies describing the beneficial effects of wine compounds on health status. Verajano and Lujan-Corro [2022] explain that red wine contains at least two kinds of PC: flavonoids (anthocyanins, flavanols, and flavonols) and non-flavonoids (stilbenes, phenolic acids, among others); these PCs are attributed with antioxidant activity by increasing the activity of the catalase, superoxide dismutase (SOD), and glutathione reductase (GR) enzymes, or by enhancing the production of nitric oxide (NO), with a consequent lower cardiovascular risk. In particular, wine quercetin and resveratrol compounds can bind to LDL through glycosidic bonds lowering their levels and increasing levels of high-density lipoprotein protecting them against free radicals and reducing oxidation induced by metal ions [1]. In addition, these compounds together with tannic acid, and malvidin may also improve endothelial NO production, reducing platelet aggregation and vascular inflammation, while anthocyanins seem to reduce LDL levels in patients with dyslipidemia [1]. In this study, our findings suggest that wine prevents multiple mechanisms associated with the occurrence of diseases, which might explain our results [1]. In this study, chamomile tea showed a positive effect on the health status. Prior studies have described that chamomile flower head has several flavonoids such as apigenin, quercetin, patuletin, and luteolin. Moreover, the chamomile hot water extract and its major components (esculetin and quercetin) show moderate sucrase inhibition, suggesting that this influences the prevention of hyperglycemia in diabetics patients [133]. In addition, an animal model study reports that chamomile has a potent anti-inflammatory action, antimutagenic and cholesterol-lowering activities, and antispasmodic and anxiolytic effects [1]. Given that chamomile was a predictor variable for a smaller number of diseases. These properties attributed to chamomile might support the results observed in this study. We expected that PCI of Mexican dishes would be higher for LD than MD participants, and our data matched our hypothesis. However, the most consumed dishes (i.e., salsas rojas and salsas verdes) were not predictor variables in our regression model. Instead, Arroz con frijoles was associated with a smaller number of diseases. As we mentioned, the beans are part of the Arroz con frijoles’ recipe. We suppose that the bean cooked in the traditional way might promote better health conditions in our Mexican sample [128]. PCI of enchiladas was also a predictor variable, which was associated with the absence of suffering from obesity. We suggest that this dish has beneficial properties on health conditions because its main ingredients are tortillas of corn and tomato. Previous studies have described that corn mainly entails ferulic acid, followed by p-coumaric acid, which are highly copious in their conjugated forms [1]. In an animal model was verified that dietary ferulic acid supplementation suppresses blood glucose elevation, body and hepatic lipid accumulation, body weight gain, and inflammatory cytokines (IL-6 and TNF-α) in high-fat diet-induced obese mice, suggesting that ferulic acid could be helpful in lowering the risk of high fat-diet induced obesity and obesity-related metabolic syndromes [1]. In Mexico, white maize is the most consumed, and this is processed as tortillas. This food implies nixtamalization process, which entails the hydrolysis of the ester linkage between the ferulic acid and the cell wall components, which triggers the soluble fraction to be higher in nixtamalized maize products relative to that found in the whole grain—that is, ferulic acid increases $26\%$ [1]. Therefore, tortillas intake might have a positive effect on health condition of Mexican population. On the other hand, a recent study reports that tomato has multiple properties; between them, this improves the antioxidant defense and plasma lipid peroxidation products, the lipid profile, and HbA1c levels [134,135]. Therefore, both ingredients help to prevent the development of abnormal weight status and other diseases. ## 5. Conclusions We conclude that PCI positively affects health conditions and supports the hypothesis that specific nutritional foods have a particular effect on certain diseases. For example, higher PCI of arroz con frijoles was associated with lower number of diseases. In contrast, a higher PCI of enchiladas was associated with a lower likelihood of suffering from diabetes. Therefore, the specific effects of Traditional Mexican food will need further research. As expected, the most consumed foods in our cohort positively affected the health conditions (e.g., tomatoes, lettuce, or corn), suggesting that the feeding habits of our Mexican sample promote a greater health status. On the other hand, it is essential to highlight that the foods less consumed had a solid effect to consider them as predictor variables (e.g., garlic, grapes, Swiss chard, or wine). Therefore, these foods must have specific properties which should be studied deeply. In addition, our findings suggest that the PCI effect also depends on sex, age, and local feeding habits. Therefore, a more controlled study should be performed to describe these effects precisely. ## 6. Limitations There are inherent limitations in the present study. This is cross-sectional study, which may not support interpretations of causality between PCI and diseases. In addition, given that a physician did not explore our participants because the survey was applied during the pandemic virus (COVID-19). The participants might suffer from other health conditions. Therefore, interpretations should be carried out carefully. In addition, we did not use the traditional daily intake survey, anthropometric measures, or biochemical indices to assess the participants; these might provide more nuanced metrics for studies examining the impact of PCI on diseases. The PCI is not a precise measurement, because our calculus was not performed considering the culinary technique for each Mexican dish. This fact may affect the amount of PC in each personal portion. In addition, we did not assess all Mexican dishes. Therefore, our conclusions are limited to the more common dishes. ## References 1. Hasler C.M.. Functional foods: Benefits, concerns and challenges—A position paper from the American Council on Science and Health. J. Nutr. (2002) 132 3772-3781. 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--- title: Royal Jelly and Chlorella vulgaris Mitigate Gibberellic Acid-Induced Cytogenotoxicity and Hepatotoxicity in Rats via Modulation of the PPARα/AP-1 Signaling Pathway and Suppression of Oxidative Stress and Inflammation authors: - Sally M. Khadrawy - Doaa Sh. Mohamed - Randa M. Hassan - Mohamed A. Abdelgawad - Mohammed M. Ghoneim - Sultan Alshehri - Nema S. Shaban journal: Foods year: 2023 pmcid: PMC10048508 doi: 10.3390/foods12061223 license: CC BY 4.0 --- # Royal Jelly and Chlorella vulgaris Mitigate Gibberellic Acid-Induced Cytogenotoxicity and Hepatotoxicity in Rats via Modulation of the PPARα/AP-1 Signaling Pathway and Suppression of Oxidative Stress and Inflammation ## Abstract Gibberellic acid (GA3) is a well-known plant growth regulator used in several countries, but its widespread use has negative effects on both animal and human health. The current study assesses the protective effect of royal jelly (RJ) and Chlorella vulgaris (CV) on the genotoxicity and hepatic injury induced by GA3 in rats. Daily oral administration of 55 mg/kg GA3 to rats for 6 constitutive weeks induced biochemical and histopathological changes in the liver via oxidative stress and inflammation. Co-administration of 300 mg/kg RJ or 500 mg/kg CV with GA3 considerably ameliorated the serum levels of AST (aspartate aminotransferase), ALT (alanine aminotransferase), ALP (alkaline phosphatase), γGT (gamma-glutamyl transferase), total bilirubin, and albumin. Lowered malondialdehyde, tumor necrosis factor α (TNF-α), and nuclear factor κB (NF-κB) levels along with elevated SOD (superoxide dismutase), CAT (catalase), and GPx (glutathione peroxidase) enzyme activities indicated the antioxidant and anti-inflammatory properties of both RJ and CV. Also, they improved the histological structure and reduced cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) expressions along with up-regulating peroxisome proliferator activated receptor α (PPARα) and down-regulating activator protein 1 (AP-1) gene expression. Additionally, chromosomal abnormalities and mitotic index were nearly normalized after treatment with RJ and CV. In conclusion, RJ and CV can protect against GA3-induced genotoxicity and liver toxicity by diminishing oxidative stress and inflammation, and modulating the PPARα/AP-1 signaling pathway. ## 1. Introduction Plant growth promotors, called phytohormones, are used worldwide in agriculture [1]. The gibberellin hormones represent an important type of phytohormone. Of these hormones, gibberellic acid (GA3) is heavily utilized in Egypt and other countries to increase the growth of many fruits and vegetables by stimulating cell division, flowering, and fruit development to improve the quality of crops [2]. GA3 is largely persistent and stays active in the ground for long periods [3]. Exposure to its residue through consumption of GA3-treated fruits and plants, inhalation of its powder as well as skin contact leads to deleterious effects on human and animal health [4]. GA3 treatment decreases the ability of the cell to scavenge reactive oxygen species (ROS) causing oxidative stress and cell death [5]. ROS attack biomolecules such as DNA, lipids, proteins, and glutathione causing enzyme inactivation, genotoxicity, cell death, and loss of cell membrane functional integrity [6]. GA3 was reported to have genotoxic and cytotoxic effects [7]. Treating cultures of human lymphocytes with gibberellin A3 increased chromosomal abnormalities, sister chromatid exchanges, and DNA mutations [8]. According to Abou-Eisha [9], gibberellic acid triggered a dose-dependent increment in DNA damage in human blood cells. Additionally, GA3 is toxic to many soft organs including the liver, causing alterations in liver enzymes, a disruption in the oxidant/antioxidant balance, and apparent changes in the liver’s architecture [10]. The peroxisome proliferator-activated receptors (PPARs) are ligand-triggered transcription factors. Upon ligand binding, they act on DNA response elements (PPREs) in the promoters of target genes as heterodimers with the retinoid X receptor (RXR), causing gene transcription modulation [11]. The expression of PPARα is significant in the liver and tissues of high metabolic rate [12]. Staels et al. [ 13] stated that, in smooth muscle cells, PPARα activators showed anti-inflammatory activities by interfering adversely with the nuclear factor-kB (NF-κB) signaling pathway. Furthermore, PPARα negatively interacts with the transcription factor AP-1 [14]. Recently, hepatotoxicity has been proven to involve PPARα inhibition [15], while the hepatoprotective effect of natural compounds has been achieved by targeting PPARα, as well as diminishing oxidative stress [16]. Therefore, there is a need for economical and safe natural antioxidant products used as therapeutic agents for treating GA3-induced hepatotoxicity via decreasing oxidative stress and inflammation, and up-regulating PPARα. Royal jelly (RJ) is a white viscous milky fluid secreted from the hypopharyngeal gland of worker honey bees (Apis mellifera Linne). It contains high levels of amino acids, proteins, lipids, sugars, vitamins, and minerals [17]. Due to its important biological properties, RJ is used as a dietary supplement and in various industries, such as pharmaceuticals, food, and cosmetics [18]. RJ exhibits anti-inflammatory, antioxidant, anti-tumor, immunomodulatory [19], cytoprotective [20], and hepatoprotective [21] activities, as well as triggers hepatocyte regeneration and development [22]. Chlorella vulgaris (CV), a unicellular green alga that grows in freshwater, is one of the food supplements widely used around the world [23]. It is documented as a safe alga by the FDA [24]. It contains bioactive compounds such as pigments, proteins, vitamins, and other growth factors [25]. The high content of carotenoids and other bioactive components has shown anti-inflammatory, immunity-modulating, and anti-cancer properties [26]; in addition to hepatoprotective and antioxidative properties [27]. Therefore, the present work was performed to estimate the probable ameliorating effects of RJ and CV on gibberellic acid-induced chromosomal alterations in bone marrow cells, as well as biochemical, histopathological, immunohistochemical, and molecular changes involved in gibberellic acid-produced liver toxicity in rats. ## 2.1. Chemicals Gibberellic acid ($99\%$ purity) as white crystalline powder was supplied by Sigma-Aldrich (Saint Louis MO, USA). Royal jelly soft gelatin capsules were supplied from Pharco pharmaceuticals (Alexandria, Egypt). Chlorella vulgaris powder was provided by Algal Biotechnology Unit (National Research Centre, Dokki, Giza, Egypt). Kits determining serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) were obtained from Biosystems (Spain). Gamma-glutamyl transferase (γGT) and total bilirubin measuring kits were bought from Spinreact (Girona, Spain). The serum albumin level was measured using a kit from Bio-Med (Germany). Kits measuring malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) were supplied from Biodiagnostic (Giza, Egypt). ELISA kits for NF-κB and TNF-α were obtained from ELISAGenie (Dublin, Ireland) and Ray Biotech (Norcross, GA, USA), respectively. Other substances had high analytical grades and were provided by suitable sources. ## 2.2. Experimental Animals Seventy-two healthy male albino rats (120–140 g) were purchased from El-Giza National Research Center (Dokki, Egypt). All procedures dealing with the rats followed the approval of the Institutional Research Ethics Committee of Beni-Suef University (BSU-IACUC, Approval No. 021-180). The rats were housed in well-aerated cages under normal laboratory conditions at 12 h light and dark cycle and 25 ± 2 °C. Animals freely accessed water and standard rat chow diets. ## 2.3. Experimental Plan and Sampling After adaptation for one week, the animals were indiscriminately allocated into six groups with 12 rats/each, divided into 2 separate cages, and treated orally using gastric gavage once daily for 6 weeks. In each group, six rats were used for cytogenetic analysis. The others were used for investigating hepatotoxicity. Group I: Negative control rats were provided with distilled water through oral intubation. Group II (RJ): Rats were orally administered 300 mg/kg/day royal jelly, suspended in distilled water [28]. Group III (CV): Rats were orally administered 500 mg/kg/day Chlorella vulgaris, suspended in distilled water [29]. Group IV (GA3): Rats were orally administered 55 mg/kg/day gibberellic acid, suspended in distilled water [30]. Group V (GA3 + RJ): Rats received 55 mg/kg GA3, followed by a dose of 300 mg/kg royal jelly. Group VI (GA3 + CV): Rats received 55 mg/kg GA3, followed by a dose of 500 mg/kg Chlorella vulgaris. At the experiment end, blood samples from 6 animals per group were gathered from the retro-orbital venous plexus and left to coagulate at room temperature. After centrifugation at 3000 rpm for 15 min, sera were collected and conserved at −20 °C until use. Cervical dislocation under mild anesthesia was applied, and then livers were removed and washed with cold saline. Each liver specimen was divided into 3 parts. The first part was used for histopathological and immunohistochemical examination (fixed in $10\%$ neutral buffered formalin). The second part was kept at −70 °C until assessing gene expression. The third part was used for preparing tissue homogenate ($10\%$ w/v) by cold phosphate-buffered saline (10X, pH 7.4), then centrifuged by high-speed cooling centrifuge for 10 min at 3000 rpm, and the obtained clear supernatants were kept at −20 °C. ## 2.4. Cytogenetic Assay The colchicine hypotonic procedure was used to prepare bone marrow cells for chromosomal abnormalities and mitotic index analyses. From each group, six animals were sacrificed by cervical dislocation. Two hours before sacrifice, 4 mg/kg colchicine was given intraperitoneally; then, the smears of bone marrow from animals in each group were prepared according to Preston et al. [ 31]. Slides were stained with Giemsa staining and 50 well-spread metaphase/animal were examined for chromosomal abnormalities. The mitotic index was determined as the dividing cells number/1000 cells/animal. ## 2.5.1. Assay of Liver Function Biomarkers ALT and AST levels were measured in the serum spectrophotometrically at 340 nm using a Hitachi spectrophotometry, Model U-2000 (Hitachi Ltd., Tokyo, Japan) by using reagent kits purchased from Biosystems, Spain (Cat. No. 11832 & 11830, respectively) as described by IFCC reference procedures [32]. Serum ALP (Biosystems, Barcelona, Spain; Cat. No. 11590) and γGT (Spinreact, Girona, Spain; MD 41288) activities were measured spectrophotometrically at 405 nm according to Tietz [33] and Young [34], respectively. Serum albumin was measured according to Doumas and Biggs [35] at wavelength 623 nm using kits purchased from BioMed (Hannover, Germany; ALB100100). Total bilirubin was determined spectrophotometrically according to David and Michael [36] at 546 nm by Spinreact (Girona, Spain; MD1001042) kits. ## 2.5.2. Assay of Oxidant/Antioxidant Indices Lipid peroxidation, as the malondialdehyde level, was assayed spectrophotometrically (Hitachi spectrophotometry, Tokyo, Japan) at 534 nm in the liver homogenate using Biodiagnostic kits (Giza, Egypt, Cat. No. MD 2529) in agreement with the method of Ohkawa et al. [ 37]. Using Biodiagnostic kits (Giza, Egypt), CAT (Cat. No. CA2517, at 510 nm), SOD (Cat. No. SD2521, at 560 nm), and GPx (Cat. No. GP2524, at 340 nm) were determined spectrophotometrically (Hitachi spectrophotometry, Tokyo, Japan) following the methods of Aebi [38], Nishikimi et al. [ 39], and Paglia and Valentine [40], respectively. ## 2.5.3. Assay of Serum TNF-α and NF-κB Levels Following the manufacturer’s guidelines, serum levels of TNF-α and NF-κB were measured using ELISA kits from Ray Biotech (Norcross, GA, USA; ELM-TNFa) and ELISAGenie (Dublin, Ireland, RTFI00988), respectively. The optical density (OD) was measured spectrophotometrically at 450 nm using Hitachi spectrophotometry (Tokyo, Japan). ## 2.6. Quantitative Real Time-Polymerase Chain Reaction (qRT-PCR) for Detection of PPARα and AP-1 Genes Expression Level The gene expression level of PPARα and AP-1 in liver tissue of all experimental groups was performed by qRT-PCR. Total RNA was extracted by total RNA isolation kits (Thermo Scientific, Waltham, MA, USA) and quantified at 260 nm. RNA samples of 1.8 and higher A260/A280 were chosen for reverse transcription to form cDNA using a RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, Waltham, MA, USA). cDNA was amplified by SYBR Green master mix (Fermentas, Waltham, MA, USA) using the primer set (described in Table 1) in a total volume of 20 μL. The acquired amplification data were analyzed by the 2−ΔΔCt method [41] and the values were standardized to β-actin. ## 2.7.1. Microscopic Evaluation After the experiment completion, liver specimens from all experimental groups were directly immersed in $10\%$ formalin fixative for two days. Thereafter, they were transacted to the usual paraffin technique and the next stains as explained by Suvarna et al. [ 42]. ## 2.7.2. Image Analysis Image analysis was done to gauge the area percentage (6 fields in each group X400) of each of the following:Total collagen fibers in Crossmon’s trichrome-stained liver sections. Glycogen content in PAS-stained sections. Total protein content in Bromophenol blue-stained sections. Positive COX-2 content expression in immunostained sections with COX-2 antibody. Positive iNOS content expression in immunostained sections with iNOS antibody. A LEICA digital camera (DFC290 HD system, Morrisville, TN, USA) was used to screen and capture all stained hepatic sections. The assessment was completed by a freeware program (Image-J 1.52a). ## 2.8. Statistical Analysis SPSS (version 25, Chicago, IL, USA) was used to carry out the statistical analysis. Data were represented as mean ± standard deviation (SD). All statistical comparisons were done using a one-way ANOVA test with Tukey’s test post hoc analysis. The value of (p ≤ 0.05) was judged significant. ## 3.1. RJ and CV Decrease Cytogenetic Toxicity Induced by GA3 in Rats The results obtained from the examination of rat bone marrow cells at the metaphase stage are shown in Table 2. The investigated structural chromosomal aberrations included deletions, breaks, ring chromosomes, fragments, end-to-end association, centromeric attenuation, and centric fusion. While aneuploidy (metaphases with more or less 42 chromosomes) and polyploidy (metaphases with more than 2 haploid sets of chromosomes) were examined to depict the numerical chromosomal aberrations. GA3 provoked a meaningful (p ≤ 0.001) increase in total structural and numerical chromosomal aberrations. Deletion, break, and ring chromosome were the most observed structural chromosomal aberrations and considerably (p ≤ 0.001) increased relative to the control group. End-to-end association (p ≤ 0.001), centric fusion (p ≤ 0.001), centromeric attenuation (p ≤ 0.01), and fragments (p ≤ 0.05) were also raised significantly over the control group. Aneuploidy as the most frequent numerical chromosomal aberration along with polyploidy were significantly increased in GA3-administered rats at (p ≤ 0.001) and (p ≤ 0.05), respectively, in comparison with the control rats. Treatment of GA3-intoxicated rats with RJ and CV significantly decreased most of the detected types of structural and numerical chromosomal aberrations. Additionally, both the total number of structural and numerical chromosomal aberrations were considerably decreased (p ≤ 0.001) in GA3-administered rats after treatment with either RJ or CV. In contrast, the mitotic index (assessed by the ratio of cells undergoing mitosis to those of non-dividing cells) was significantly decreased (p ≤ 0.001) in the GA3-induced group compared with the control group indicating bone marrow cytogenetic toxicity (Figure 1). While the mitotic index in the groups treated with RJ and CV concurrently with GA3 was increased considerably (p ≤ 0.001) indicating anti-cytogenotoxicity towards GA3, as shown in Figure 1. Table 2 \| Groups \| Structural Chromosomal Aberrations \| Structural Chromosomal Aberrations.1 \| Structural Chromosomal Aberrations.2 \| Structural Chromosomal Aberrations.3 \| Structural Chromosomal Aberrations.4 \| Structural Chromosomal Aberrations.5 \| Structural Chromosomal Aberrations.6 \| Structural Chromosomal Aberrations.7 \| Numerical Chromosomal Aberrations \| Numerical Chromosomal Aberrations.1 \| Numerical Chromosomal Aberrations.2 \| TCA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Groups \| ChromatidBreak \| Chromatid Deletion \| RingChromosome \| Fragment \| Endto EndAssociation \| CentricFusion \| CentromericAttenuation \| TSA \| Polyploidy \| Aneuploidy \| TNA \| TCA \| \| Control \| 1 ± 1.3 \| 0.7 ± 0.5 \| 0.5 ± 0.8 \| 0.5 ± 0.5 \| 0.0 ± 0.0 \| 0.0 ± 0.0 \| 0.0 ± 0.0 \| 2.7 ± 0.8 \| 0.5 ± 0.5 \| 0.3 ± 0.8 \| 0.8 ± 1.2 \| 3.5 ± 1 \| \| RJ \| 0.8 ± 1 \| 0.5 ± 0.5 \| 0.3 ± 0.4 \| 0.3 ± 0.5 \| 0.0 ± 0.0 \| 0.0 ± 0.0 \| 0.0 ± 0.0 \| 2 ± 1 \| 0.3 ± 0.5 \| 0.2 ± 0.4 \| 0.5 ± 0.5 \| 2.5 ± 1 \| \| CV \| 1 ± 0. 9 \| 0.7 ± 0.8 \| 0.3 ± 0.5 \| 0.5 ± 0.8 \| 0.0 ± 0.0 \| 0.0 ± 0.0 \| 0.0 ± 0.0 \| 2.5 ± 1 \| 0.5 ± 0.8 \| 0.3 ± 0.5 \| 0.8 ± 0.8 \| 3.3 ± 0.8 \| \| GA3 \| 5.5 ± 1 * \| 6.7 ± 1 * \| 4.7 ± 1 *** \| 2 ± 1.3 * \| 1.5 ± 1* \| 1.7 ± 0.8 * \| 1.3 ± 1.2 \| 23 ± 1 * \| 2 ± 1.3 * \| 2.8 ± 1.5 * \| 4.8 ± 1.2 * \| 28 ± 0.8 * \| \| GA3 + RJ \| 2.3 ± 1.2 ### \| 2.7 ± 1.2 ### \| 1.3 ± 1.2 ### \| 0.5 ± 0.5 # \| 0.3 ± 0.5 ## \| 0.5 ± 0.8 ## \| 0.2 ± 0.4 # \| 7.8 ± 1.2 *### \| 0.5 ± 0.5 # \| 0.8 ± 1 ## \| 1.3 ± 0.8 ### \| 9.2 ± 1.5 ### \| \| GA3 + CV \| 2.7 ± 1.4 ## \| 3.2 ± 1.2 ### \| 1.8 ± 1.5 ### \| 0.7 ± 0.8 \| 0.5 ± 0.5 # \| 0.5 ± 0.5 ## \| 0.3 ± 0.5 # \| 9.7 ± 2 ### \| 0.7 ± 0.5 # \| 1.2 ± 1 # \| 1.8 ± 1 ### \| 12 ± 2 *### \| ## 3.2. RJ and CV Ameliorate Liver Function Biomarker Changes Induced by GA3 in Rats Data represented in Table 3 revealed that the GA3-induced group had significantly (p ≤ 0.001) higher levels of ALT, AST, ALP, γGT, and total bilirubin, as well as significantly (p ≤ 0.001) lower levels of albumin. Rats that received RJ and CV simultaneously with GA3 showed a significant (p ≤ 0.001) amelioration of ALT, AST, ALP, γGT, and total bilirubin. Albumin was meaningfully raised in RJ (p ≤ 0.001) and CV (p ≤ 0.01) treated groups compared to GA3 intoxicated rats. ## 3.3. RJ and CV Alleviate Hepatic Oxidative Stress Induced by GA3 in Rats GA3-intoxicated rats exhibited a status of oxidative stress as manifested by a significant (p ≤ 0.001) elevation in hepatic malondialdehyde level accompanied by a significant (p ≤ 0.001) reduction in SOD, CAT, and GPx activity (Table 4). The group of rats treated with royal jelly simultaneously with gibberellic acid showed a remarkable (p ≤ 0.001) decrease in MDA content and increase in SOD, CAT, and GPx activity. Similarly, treatment of GA3-intoxicated rats with Chlorella vulgaris* significantly (p ≤ 0.001) suppressed lipid peroxidation, and significantly (p ≤ 0.001) enhanced activity of SOD, CAT, and GPx enzymes (Table 4). ## 3.4. RJ and CV Mitigate Inflammation Induced by GA3 in Rats The anti-inflammatory effect of RJ and CV against GA3- triggered inflammation was assessed by measuring TNF-α (Figure 2A) and NF-κB (Figure 2B) serum levels. Circulating levels of TNF-α were markedly (p ≤ 0.001) increased in the GA3-administered group as compared to the control group. RJ or CV supplementation revealed a significant (p ≤ 0.001) reduction in serum levels of TNF-α compared with the GA3-administered group. Likewise, the GA3-administered group showed a significantly (p ≤ 0.001) elevated NF-κB level, an effect that was significantly reduced after the administration of RJ (p ≤ 0.001) or CV (p ≤ 0.01). ## 3.5. RJ and CV Up-Regulate PPAR Alpha and Down-Regulate AP-1 in the Liver of GA3-Intoxicated Rats PPARα mRNA abundance (Figure 3A) was notably (p ≤ 0.001) decreased in the liver of GA3-intoxicated rats as compared to the control rats. RJ (p ≤ 0.001) and CV (p ≤ 0.01) administration up-regulated the expression of PPARα when compared with the GA3-intoxicated group. In contrast, AP-1 mRNA in the liver of GA3-intoxicated rats underwent a significant up-regulation (p ≤ 0.001) in comparison with the corresponding control group as represented in Figure 3B. Oral administration of either RJ (p ≤ 0.001) or CV (p ≤ 0.05) significantly down-regulated the AP-1 gene expression level in comparison with the GA3-intoxicated rats. ## 3.6. RJ and CV Attenuate Tissue Injury and Fibrosis in the Liver of GA3-Intoxicated Rats H&E stained liver sections from the control, RJ, and CV treated groups showed a normal hepatic structure. There were undeveloped connective tissue septa separating the classic lobules, which were formed of typical hepatic cords of polygonal hepatocytes that appeared as rays from the central vein containing cytoplasm with acidophilia and round centric nuclei, as well as typical peripheral portal areas. The radiating cords were separated by blood sinusoids with normal lining (Figure 4(A,A1)). On the contrary, the GA3-treated group showed severe irregularity and degeneration of the hepatocytes, besides inflammation. Numerous cells exhibited cytoplasmic hydropic degeneration (vacuolated cytoplasm) and nuclear degeneration, and others appeared apoptotic with condensed nuclei. Cells surrounding portal areas were more degenerated than those surrounding central veins. Most of the central veins, sinusoids, and portal vessels including lymphatic vessels appeared dilated with disrupted lining, congested, and containing tissue exudate with lymphocytic infiltration. Multifocal areas of lymphocytic infiltrations appeared surrounding the portal areas and central veins (Figure 4(B,B1)). All previously mentioned alterations caused by GA3 were ameliorated with the administration of RJ. Most of the cells were improved and returned to their normal structure but a few sinusoids were still dilated with degenerated lining, as well a few apoptotic cells were present (Figure 4(C,C1)). Also, in the group treated with CV, most of the pathological changes were partially alleviated, but mild changes in some hepatocytes and some apoptotic cells were still observed. Additionally, a few focal areas of periportal lymphocytic infiltrations and around the central veins with vascular dilatation were detected (Figure 4(D,D1)). The scoring of pathological alterations in all experimental groups is presented in Table 5. In hepatic slides stained with Crossmon’s trichrome, collagen fibers and their area percentage were detected and calculated. The control group showed normal distribution of interlobular and perivascular fine collagen fibers of green color (Figure 4A2). On the contrary, the administration of GA3 led to the initiation of liver fibrosis, in that the degenerated vessels were surrounded by excessive proliferation of fibers in comparison with the normal control (Figure 4B2). In the GA3 + RJ-treated group, fibers appeared with normal distribution compared with the GA3-treated group (Figure 4C2). Similarly, the proliferation of fibers was diminished by the treatment using CV, and there was only mild proliferation of perivascular fibers (Figure 4D2). The area quantity calculation of the collagen fibers in all experimental groups is shown in Table 6. It ensured that GA3 leads to the induction of fibrosis. A significant increase in fiber quantity appeared in the GA3-treated group (p ≤ 0.001) in comparison with normal. In contrast, a significant decrease in fibers (p ≤ 0.001) appeared in GA3 + RJ and GA3 + CV-treated groups in comparison with the GA3-treated group. ## 3.7. RJ and CV Attenuate Histochemical Changes in the Liver of GA3-Intoxicated Rats In PAS-stained liver sections, the distribution of cytoplasmic glycogen appeared with a typical strong positive magenta red reaction in the control group (Figure 5A). On the contrary, the GA3-treated group showed a great depletion of glycogen content, which was indicated by very faint coloration as compared with the control (Figure 5B). While, after administration of RJ, the secretion of glycogen was returned to normal after appearing in the form of a strong PAS color when compared with the GA3-treated group (Figure 5C). Also, glycogen content was partially restored in the GA3 + CV-treated group, which was indicated by a strong reaction in the normal hepatocytes and a moderate one in the others (Figure 5D). Image analysis of the area percentage of PAS-positive content was recorded in all studied groups in Table 6. A significant decrease (p ≤ 0.001) was detected in the GA3-treated group compared with the control group. Conversely, in GA3 + RJ and GA3 + CV-treated groups, there was a significant (p ≤ 0.001) increase in comparison with the GA3-treated group. Regarding the Bromophenol blue stain, hepatic sections of the control showed typical cytoplasmic protein content that appeared as a strong dark blue coloration (Figure 5A1). On the contrary, a marked reduction of protein appeared in the GA3-treated group manifested by a faint blue reaction compared with the normal (Figure 5B1). While, in the group treated with RJ, the previous depletion of protein was alleviated and the secretion was restored to normal, which was manifested by a strong blue color in comparison with the GA3-treated group (Figure 5C1). Also, the GA3 + CV-treated group revealed partial improvement in protein secretion indicated by a strong reaction in the normal hepatocytes and a moderate one in the others (Figure 5D1). Image analysis of the area percentage of the Bromophenol blue positive reaction was recorded in Table 6. In the GA3-treated group, a significant decrease (p ≤ 0.001) was showed in comparison with control. But, a significant increase (p ≤ 0.001) was revealed in GA3 + RJ and GA3 + CV-treated groups compared to the GA3-treated group. ## 3.8. RJ and CV Downregulate COX2 and iNOS Immunoexpression in the Liver of GA3-Intoxicated Rats COX-2-immunostained sections: the control group normally has very few positive, brown granules in the sinusoidal endothelium near the central vein (Figure 6(A,A1)). On the contrary, COX-2 expression was markedly elevated in the GA3-treated group in comparison with the control. It presents as excessive positive granules with high intensity filling numerous hepatocytes, the sinusoidal lining (endothelium and Van Kupfer cells), and in the perivascular tissue mainly around the central vein (Figure 6(B,B1)). Treatment with RJ markedly decreases COX-2 production, so GA3 + RJ group showed few positive contents that were expressed only in few hepatocytes and the sinusoidal endothelium compared to GA3-treated group (Figure 6(C,C1)). Also, administration of CV partially decreases the COX-2 positive contents and thus appeared with a mild intensity only in some hepatocytes mainly around the central vein and in the sinusoidal lining (Figure 6(D,D1)). The area percentage of COX-2 immunoexpression in all studied groups was recorded in Table 6. A significant increase (p ≤ 0.001) appeared in GA3-induced group in comparison with control. In contrast, the expression decreased significantly (p ≤ 0.001) in GA3 + RJ and GA3 + CV-administered groups in comparison with GA3-induced group. iNOS immunostained sections: the control group showed fine positive dark brown-colored iNOS granules in the sinusoidal endothelium near the central vein (Figure 6A2). In contrast, iNOS expression was increased markedly in the GA3-treated group; numerous positive granules appeared in the sinusoidal endothelium mainly in the periportal area and the perivascular tissue in comparison to the control (Figure 6B2). While in GA3 + RJ group, iNOS expression markedly decreased and thus appeared only in few sinusoidal endothelium (Figure 6C2). Likewise, the treatment with CV partially decreased iNOS expression; it presents only in some sinusoidal lining and the periportal area (Figure 6D2). The area percentage of iNOS immunoexpression was recorded in Table 6. It was increased significantly (p ≤ 0.001) in the GA3-treated group in comparison with the control. In contrast, a significant decrease (p ≤ 0.001) was detected in the GA3 + RJ and GA3 + CV-treated groups compared to the GA3-treated group. ## 3.9. Image Analysis and Statistical Evaluation The comparative analysis and quantification of the area percentages of collagen fibers, PAS, bromophenol blue, COX-2, and iNOS expressions in all studied groups was shown in Table 6. It concluded that the reduction of collagen, COX-2, and iNOS, and the raising of glycogen and total protein area percentages in GA3 + RJ and GA3 + CV-treated groups in comparison with the GA3-treated group approve the protective effect of RJ and CV on the hepatic tissue against injury and inflammation produced by GA3. ## 4. Discussion The utilization of plant-growth hormones and their effects on health are a matter of concern. Gibberellic acid (GA3), a plant growth regulator, is commonly used in Egypt in agriculture to hasten the growth of vegetables and fruits [46]. Human beings and animals can be exposed to residues of GA3 through consuming GA3-treated plants or drinking contaminated water [47]. As well, agricultural workers dermally contact with GA3 or inhale its powder, causing acute toxicity [48]. The precise mechanism causing its toxicity has not yet been entirely understood. In the current study, the genotoxic effect of GA3 was manifested by the induction of chromosomal aberrations and change in the mitotic index in bone marrow cells of rats. Also, we inspected GA3-induced hepatotoxicity with a focus on oxidative stress, inflammation, and the PPARα/Ap-1 signaling pathway, as well as we tested the protective effects of royal jelly and Chlorella vulgaris against GA3- induced toxicity. GA3 administration increased the structural and numerical chromosomal aberrations in agreement with preceding studies in human lymphocyte cultures [49], and bone marrow cells of mice [50], rats [51], and rabbits [7]. As a result of GA3 interaction with DNA, chromosomes or chromatids’ terminal ends might be deleted, which make them unstable and create end-to-end associations and ring chromosomes, and may lead to total genomic damage [52]. Jovtchev et al. [ 53] reported that the increase in the incidence of chromosomal aberrations in rat bone marrow cells is attributed to the decrease in the mitotic activity of these cells. This confirms our results, which revealed a substantial reduction in the mitotic index in GA3-induced rats indicating bone marrow cytotoxicity as previously reported by Nassar et al. [ 51]. Co-treatment of GA3-intoxicated groups with royal jelly or Chlorella vulgaris decreased the chromosomal aberrations and increased the mitotic index indicating their anti-cytotoxic activities by following many previous studies. El-Monem [54] previously reported the capacity of royal jelly to defend against the genotoxicity induced by environmental pollutants, which may be attributed to its highly biologically active compounds. Nutrients including lipids, peptides, and proteins play a role in the antioxidant and anti-cancer properties of royal jelly in addition to phenolic and flavonoid components [55]. Furthermore, Chlorella vulgaris lowered the cytotoxicity and genotoxicity as demonstrated in an early study due to its content of bioactive compounds and natural antioxidants [56]. Saberbaghi et al. [ 57] as well showed that Chlorella vulgaris is capable of diminishing DNA damage and apoptosis and promoting cell cycle progression due to its antioxidant properties that prevent ROS and free radicals from damaging DNA. Additionally, Makpol et al. [ 58] confirmed that Chlorella vulgaris has a defensive nature and controlled DNA damage generated by H2O2. GA3 could exert toxic impacts on numerous soft organs including the liver [59]. It is well documented that the liver is the first organ in toxicological prospects concerning its role in xenobiotics biotransformation, detoxification, and excretion [60]. The results of the present study revealed that GA3 significantly increased the serum levels of AST, ALT, γGT, and ALP hepatic enzymes in line with earlier data described by Wafaa et al. [ 46]. The normal blood levels of these enzymes result from the continual leaking of minute amounts through the cell membrane within the hepatocytes. However, in the instance of hepatocellular toxicity, the membranes become more porous due to the loss of functional integrity leading to increased serum levels of these enzymes [61]. The reduction in the serum albumin as a result of GA3 administration was reported in our results in parallel with Troudi et al. [ 62]. Albumin is the most abundant blood plasma protein produced in the liver [63]. The declined level points to chronic liver disorders characterized by considerable hepatocyte destruction and deficiency in the synthetic function of the liver [64]. Also GA3 administration was associated with an elevated serum total bilirubin level as previously recorded by Troudi et al. [ 62]. Bilirubin accretion evaluates the binding, conjugation, and excretion capability of hepatocytes and is one of the best clinical indications of the degree of necrosis. Hence, the significant liver damage was linked to elevated bilirubin levels [65]. Nowadays, RJ plays an important role in folk medicine owing to its numerous biological activities [66]. The co-administration of RJ with GA3 exhibited a significant decline in the elevated AST, ALT, γGT, and ALP concentrations and ameliorated the changes of albumin and total bilirubin levels associated with GA3 hepatotoxicity. Our results were in line with Gholie Pour et al. [ 67] who confirmed that RJ significantly diminished the levels of liver enzymes. The modulating effects of RJ on the liver function enzymes could be attributed to vitamin C, vitamin E, and arginine found in RJ. Vitamins E and C are well-known antioxidants that prevent cell membrane damage caused by free radicals, reduce liver inflammation, and thus reduce enzyme leakage [68]. The current study revealed that CV administration significantly improved liver function biomarkers in harmony with Vakili et al. [ 69] who explained that CV supplementation showed meaningful improvements in liver enzymes. Non-alcoholic fatty liver diseased patients who consumed CV for three months experienced significant drops in ALT and AST [70]. Another explanation was that CV could protect liver cells by influencing insulin resistance. CV supplementation decreased plasma non-esterified fatty acid concentration improving glucose homeostasis and resulting in a discernible decrease in serum glucose concentrations [71]. Blood glucose levels were correlated with liver enzymes [72]. In the current study, GA3 administration showed a significant increment in MDA level and a significant decrement in SOD, CAT, and GPx activities in hepatic tissues, denoting that GA3 provoked oxidative stress and lipid peroxidation, as illustrated by Hussein et al. [ 5]. This was related to the generation of hydroxyl radicals, which can react with lipids via hydrogen abstraction and cause lipid peroxidation and oxidative damage inside the cell [73]. Also, ROS can attack thiols in proteins and glutathione causing inactivation of the enzymes [6]. SOD and GSH-Px play a key role in cellular defense against ROS, reducing oxidized lipids and protein targets of ROS [74]. GA3 could down-regulate CAT, SOD, and GPx mRNA in the liver tissues [5]. The diminution in antioxidant enzymes’ activities might be due to the extreme utilization following the flux of superoxide radicals [46]. Interestingly, our results showed that RJ treatment significantly decreased MDA hepatic level and increased the enzymatic activities of SOD, CAT, and GPx. These results were confirmed by You et al. [ 75] who stated that RJ could mitigate the deleterious effects of oxidative stress by boosting the activity of liver antioxidant enzymes. Kocot et al. [ 76] mentioned that short-chain peptides, phenolic compounds, and fatty acids are some of the substances obtained from RJ that have been shown to have potent antioxidant properties. Also, aspartic acid, cysteine, and cystine, which are involved in the formation of GSH, a powerful cellular antioxidant, are present in RJ [77]. Khodabandeh et al. [ 78] clarified that by lowering the leukocyte response and increasing the mitochondrial respiratory chain, RJ contributes to the reduction of lipid peroxidation and production of ROS. So, depending on the best deduction, the antioxidants in RJ have hepatoprotective effects against the harmful effects of free radicals generated by GA3. Similarly, our results revealed that CV supplementation showed higher SOD, CAT, and GPX activities with significantly lower MDA values in good agreement with Abdel-Tawwab et al. [ 79]. Phytochemicals; like tocopherols, chlorophylls, flavonoids, carotenoids, ubiquinone, and polyphenols that have antioxidant properties are extensively included in CV [80]. In this concern, Zahran and Risha [81] stated that CV increased CAT and GPX levels in Nile tilapia. Also, Chlorella species-derived polysaccharides have demonstrated antioxidant action against free radicals [82]. Chlorella vulgaris boosts the body’s overall antioxidant capacity while inhibiting lipid peroxidation to preserve cellular membranes from deterioration [83]. The inflammatory reactions associated with GA3 administration were highly obvious in our study in which it increased serum levels of TNF-α and NF-κB in accordance with Soliman et al. [ 30]. During inflammatory reactions in hepatic tissues, oxidative stress is an imperative factor [84]. Activation of the pro-inflammatory NF-κB pathway via ROS produces TNF-α and other inflammatory mediators [85]. TNF-α plays an important role in the development of liver injury [86], and has been demonstrated to intensify the pathophysiological reactions induced by toxicants [87]. TNF-α induces cell death through apoptotic and necrotic pathways, thus reducing TNF-α production declines in tissue injury [88]. NF-κB, a nuclear transcription factor, controls apoptosis and immunological actions and mediates acute and chronic inflammatory responses [89]. The release of NF-κB from inhibitory protein IκB causes its translocation from the cytoplasm into the nucleus where it binds to the promoters of pro-inflammatory mediators such as TNF-α, IL-1β, and IL-6, resulting in the induction of their gene expression [90]. Cytokines that are stimulated by NF-κB can directly activate the NF-κB pathway, generating a positive autoregulatory loop that can enhance the inflammatory response and frequency of inflammation [91]. Administration of RJ to GA3-intoxicated group in the current work significantly diminished the inflammatory mediators induced by GA3. We suggested that the free radicals mediated activation of NF-κB may be alleviated by royal jelly’s antioxidant action in accordance with Almeer et al. [ 92]. According to Ahmed et al. [ 17], the management of retinol loss, the antioxidant impact of some free amino acids, and the restoration of ascorbic acid availability by royal jelly are some of the hypothesized explanations for the antioxidant effect. Our data confirmed that rats received CV along with GA3 showed a significant reduction in TNF-α and NF-κB circulating levels. Abu-Serie et al. [ 93] explained that certain phenolics in CV, including gallates, which are powerful TNF-α inhibitors, may be responsible for its anti-inflammatory effect. Additional elements like triterpenoids have the power to reduce the expression of inflammatory mediators [94]. Also, ergosterol and peroxide-derived ergosterol from CV have been demonstrated to suppress the inflammatory response of lipopolysaccharide by lowering pro-inflammatory cytokines [95]. Our results revealed that GA3 significantly decreased the gene expression level of PPARα while increasing the activator protein 1 (AP-1) gene expression level. He et al. [ 96] stated that the expression of PPARα mRNA is markedly decreased in inflammatory liver disorders. Through transrepression of AP-1 and NF-κB signaling pathways, PPARα exerts anti-inflammatory actions. PPARα can successfully trans-repress a variety of pro-inflammatory gene promoters controlled by NF-κB or AP-1 response elements by protein-protein interactions [97]. The p65 and c-Jun components of the NF-κB and AP-1 transcription factors interact correspondingly with PPARα physically and functionally. Additionally, PPARα significantly lowers the gene production of pro-inflammatory cytokines in the liver, such as pro-IL-1 β, pro-IL-6, and pro-TNF [98]. By promoting the transcription of a number of pro-inflammatory genes, NF-κB and AP-1 play a crucial role in inflammation. The transcription factors NF-κB and AP-1 are stimulated as PPARα is reduced [99]. On the other hand, PPARα mRNA expression in the liver was remarkably more increased in the RJ supplemented group than in the GA3-intoxicated group, while the gene expression level of AP-1 was significantly decreased. Yoshida et al. [ 100] reported that RJ up-regulates the hepatic gene expression level of PPARα in diabetic mice. The inhibitory action of RJ on AP-1 gene expression is related to the up-regulation of PPARα expression since PPARα exerts anti-inflammatory effects through trans-repression of AP-1 [97]. Moreover, the up-regulation of the PPARα expression level observed in the RJ group explained the lower serum levels of NF-κB and TNF-α recorded in this group in agreement with previous literature proving that in models of systemic inflammation, non-alcoholic steatohepatitis, and atherosclerosis, PPARα may negatively affect the pro-inflammatory and acute phase response signaling pathway [101]. Also, PPARα activation boosts antioxidant defense and lowers oxidative stress [96]. Similarly, our findings reported the anti-inflammatory activity of CV supplementation via an elevated gene expression level of PPARα and lowered gene expression level of AP-1. Many transcription factors such as peroxisome proliferator-activated receptors and the retinoid X receptor (RXR) are stimulated by β-carotene, which is a bioactive component present in CV, as it is responsible for the production of retinol and retinoic acid [102]. Our recorded biochemical results are compatible with the histopathological alterations. Concerning the histological observations in the hepatic parenchyma of the GA3-treated group, there was severe cellular and nuclear degeneration, vacuolation, and apoptosis, besides dilatation and congestion of all hepatic vessels. in addition to lymphocytic infiltrations as previously revealed [46,62,103]. Vacuolar degeneration was recorded as one of the main first responses to cell injury [104]. Hepatocytic vacuolation is caused by oxidative changes and lipid peroxidation induced by GA3 [105]. Subsequently, lipid peroxides accumulated and produced organelles disintegration and membrane permeability alterations. Rahman and Mcnee [106] discussed that the inflammatory cell leakage was accompanied by cellular oxidation, in which the free radicals destruct the endothelial cells making output of interleukin and cytokine-induced neutrophil chemoattractant mediators, leading to filling of microcirculation with the inflammatory cells, which then go to the liver interstitium. Our results indicate that treatment with RJ has the power to recover the normal structure of hepatocytes and their secretions. The hepatoprotective effect of RJ was explained previously by Cemak et al. [ 107] and Mostafa et al. [ 28] as it preserves the integrity of hepatocyte membrane and prevents the hepatic enzymes leakage into the circulation. Sequentially, CV treatment makes partial hepatic improvement of the injured tissue. Kumar et al. [ 26] discussed the protective effect of CV, due to high carotenoid contents, which have anti-inflammatory and antioxidative activities. Also, Naguib [108] and El-Fayoumy et al. [ 109] found that the antioxidant properties of CV are due to its chemical constituents of active hydroxyl group plus unsaturated bonds that have a high ability to prevent cellular oxidation by recovering some free radicals. In the control group, Crossmon’s trichrome-stained liver sections showed collagen fibers of a fine normal periportal distribution as revealed by Alshawsh et al. [ 110]. While, in the GA3-treated group, there was a massive periportal collagen fiber distribution. These results plus the significant area percentage of fibers signalize the initiation of fibrosis with the long administration of GA3. Bauer and Schuppan [111] explained that hepatic fibrosis is mainly stimulated by hepatocyte degeneration and necrosis, which causes Kupffer cells stimulation and production of cytokines and growth factors, which enhance the proliferation of stellate cells and excessive secretion of connective tissue fibers and matrix. Moreover, Ross and Pawlina [112] clarified that nuclear damage caused by lipid peroxidation enhances collagen formation. The administration of RJ and CV lead to minimizing fibrosis, which was proved by our result of area percentage for collagen fibers, which was significantly more decreased than in the GA3-treated group. The glycogen and total protein contents in the PAS and Bromophenol blue-stained sections, respectively, were depleted significantly in response to GA3 administration compared to normal sections. Our results agree with those revealed by Ali et al. [ 103]. While the sections of GA3 + RJ and GA3 + CV-treated groups, which showed nearly normal contents, emphasize the hepatoprotective effect of RJ and CV. COX-2 was expressed as a few granules in normal livers, mainly in the sinusoidal lining. Excessive COX-2 expression is accompanied by inflammation and tissue injury [113]. This is compatible with our result of COX-2 in the GA3-treated group as it was expressed in the inflamed hepatocytes, sinusoidal lining including endothelial and Kupffer cells mainly surrounding collagen proliferation and inflammatory cell infiltration. This agreed with that mentioned by Denda [114] who stated that the Kupffer cells were the main hepatic prostanoids producers. RJ reduces tissue damage through the reduction of TNF-α and COX-2 expression [113]. CV administration inhibits the COX-2 expression due to their inhibitory activity that blocks the inflammatory mediator’s formation by COX-2 inhibitors as discussed by Cheng et al. [ 115]. Mohammed et al. [ 116] previously stated that iNOS levels were increased in cirrhosis. Moreover, its expression increased mainly surrounding the areas of fibrosis [117]. The excessive iNOS expression in hepatic tissue treated with GA3 with increased fibrosis suggests that GA3 toxicity is associated with increased production of nitric oxide. RJ significantly decreased the iNOS expression through the reduction of inflammation [28,118]. CV treatment has an inhibitory action on iNOS production due to anti-inflammatory, antioxidant, and free radical scavenging effects, and the presence of chlorophyll [119]. ## 5. Conclusions Depending on our biochemical and histopathological results, GA3 induced liver damage as implied by the elevation of serum biochemical parameters, reduction of the antioxidant activity, and increase in the inflammatory mediators. Also, GA3 induced cytogenotoxicity as manifested by chromosomal abnormalities and abnormal mitotic index. 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--- title: Psychometric Characteristics of the Self-Care of Chronic Illness Inventory in Older Adults Living in a Middle-Income Country authors: - Alta Arapi - Ercole Vellone - Dhurata Ivziku - Blerina Duka - Dasilva Taci - Ippolito Notarnicola - Alessandro Stievano - Emanuela Prendi - Gennaro Rocco - Maddalena De Maria journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048512 doi: 10.3390/ijerph20064714 license: CC BY 4.0 --- # Psychometric Characteristics of the Self-Care of Chronic Illness Inventory in Older Adults Living in a Middle-Income Country ## Abstract Chronic illness requires numerous treatments and self-care is essential in the care process. Evaluation of self-care behaviors facilitates the identification of patients’ needs and optimizes education and care processes. This study aimed to test the psychometric characteristics (validity, reliability, and measurement error) of the Albanian version of the Self-Care of Chronic Illness Inventory (SC-CII). Patients with multiple chronic conditions and caregivers were recruited in outpatient clinics in Albania. The patients completed the SC-CII, which includes three scales: self-care maintenance, self-care monitoring, and self-care management. Factorial validity was tested for each scale, with confirmatory factor analysis. Reliability was evaluated with the composite coefficient, Cronbach’s alpha, and the global reliability index for multidimensional scales. The construct validity was tested using hypothesis testing and known differences between groups. The measurement error was tested to assess responsiveness to changes. The self-care maintenance and self-care monitoring scales showed a unidimensional factorial structure, while the self-care management scale showed a bidimensional structure. Reliability estimates were adequate for all reliability coefficients. Construct validity was supported. The measurement error was adequate. The Albanian version of the SC-CII shows good psychometric properties in the Albanian sample. ## 1. Introduction Older people often suffer simultaneously from more than two chronic illnesses, defined as multiple chronic conditions (MCCs) [1]. These have increased in recent years, becoming responsible for $74\%$ of all deaths worldwide, with an important increase in the rate in low and middle-income countries as well [2]. MCCs represent an important social burden [3] due to the high morbidity and disability, and negatively affect patients’ and their family members’ quality of life [4]. Furthermore, MCCs are a global concern for healthcare systems because they are associated with substantial increases in healthcare costs and resource utilization [5]. According to the WHO, MCCs account for $11\%$ of premature deaths that occur in low/middle-income countries (LMIC), including Albania [6]. As such, the WHO recommends the practice of self-care behaviors in the management of MCCs to mitigate worse outcomes associated with them, not only in industrialized countries but also in LMIC [6]. Self-care is a process of maintaining health status through the promotion of health and disease management practices [7]. Associations between adequate self-care practices and reduced hospitalizations or mortality rates and better quality of life or well-being among patients are well documented [8]. Nevertheless, self-care behaviors are frequently inadequate due to the age of the patient, comorbidities, cognitive impairments, stressful life events, and the influence of others [8,9]. Furthermore, culture, social norms, values, meanings, language, environments, attitudes, behaviors, personal perceptions, and care partner [10] can influence self-care behaviors [7,11]. Thus, healthcare professionals should support and empower patients with chronic illnesses and their care partners to perform self-care. This suggests the need for a regular assessment of self-care behaviors to identify inadequate caring standards and implement customized interventions. Albania is a small middle-income country in the southeast of Europe. In recent years, the country has been exposed to deep political and socioeconomic reforms, which caused important epidemiological and health changes and a significant increase in chronic diseases and MCCs [12]. In fact, chronic diseases account for $89\%$ of total deaths in the country [13], with a $45\%$ increase in the prevalence of MCCs [14], including cardiovascular diseases, diabetes, and chronic respiratory diseases [15]. The “modernization” process [12] nurtured risky health behaviors such as increased tobacco and alcohol consumption, sedentary lifestyles, and poor dietary habits. All these factors require Albanians to adopt self-care behaviors that need to be properly assessed to implement interventions aimed at improving them. Furthermore, Albanians inherited the culture of ‘curing’ rather than ‘preventing’, the overuse of hospital services, and the lack of awareness of MCCs from the previous regime [13]. In addition, the nation is facing demographic challenges such as young adults’ migration to other nations or to urban areas, and smaller family nuclei [16]. This sociodemographic aspect might influence the self-care abilities of elderly people with MCCs in Albania. Collectively, these increasing trends in unhealthy behaviors, reduced health literacy, the poor culture of prevention, and demographic changes suggest the urgent need for intervention in these amendable risk factors [12] to reduce the increasing burden of MCCs in the Albanian population [16]. In the last decade, the government has employed different approaches to improve primary healthcare services such as the implementation of free check-ups, free medical visits in primary healthcare departments, and reimbursement of medications for chronic illnesses [16]. Consequently, some progress has been documented on the culture of prevention around chronic disease [13]. However, despite these efforts, self-care behaviors in the Albanian population affected by MCCs are underexplored. The few data available document that $59\%$ of hospitalized patients with chronic diseases (diabetes mellitus, heart diseases, hypertension, chronic lung diseases, osteoarthritis, bronchial asthma, and chronic kidney disease) are not able to recognize the risk factors, monitor their status, and control the progression of the disease [15]. Therefore, in Albania, long-term effort is requested from healthcare professionals to assess abilities and to educate patients with MCCs and their care partners on self-care practices to manage the chronic diseases [15,16]. An instrument internationally used to assess self-care in chronic diseases is the Self-Care of Chronic Illness Inventory (SC-CII). The SC-CII [17] was developed in English and translated into many languages such as Arabic, Catalan, Chinese, Dutch, Italian, Spanish, and Swedish, but not Albanian [18]. It captures the behaviors of the self-care process with three separate scales: self-care maintenance, self-care monitoring, and self-care management. Self-care maintenance refers to behaviors performed to improve well-being, maintain health, or maintain physical and emotional stability (e.g., “take medications as prescribed”) [7]. Self-care monitoring is the surveillance of chronic conditions (that is, “monitoring signs and symptoms”); this process involves the evaluation and perception of bodily changes by listening to the body [7]. Self-care management includes the recognition of chronic disease signs and symptoms and the patient behaviors in response to such symptoms (e.g., “take medicines to make the symptom decrease or go away”) [7]. The SC-CII has shown adequate validity and reliability across populations with supportive fit indices in confirmatory factor analysis (e.g., comparative fit index (CFI) ranged between 0.93 and 1.00 in the three scales) and reliability (reliability coefficients for all the three scales ranged from 0.67 to 0.86) [7,18]. To date, no valid and reliable instrument is available to assess self-care in MCCs in an LMIC, such as Albania, and consequently, self-care behaviors cannot be assessed. A valid and reliable psychometrically sound instrument would allow not only an accurate assessment of self-care, but also evaluations of interventions aimed at improving self-care in these populations. This study aimed to test the psychometric characteristics (validity, reliability, and responsiveness to changes) of the instrument Self-Care of Chronic Illness Inventory, in older Albanian adults affected by chronic illnesses. ## 2.1. Design For this study, we used a cross-sectional multicenter design conducted in Albania. ## 2.2. Sample and Setting A sample of 250 patients, considered adequate for these analyses [19], was recruited in outpatient clinics and community healthcare settings in central and south Albania. Inclusion criteria were age ≥ 65 years old, and a simultaneous diagnosis of chronic diseases such as heart failure, diabetes mellitus, chronic obstructive pulmonary disease, and at least one other chronic disease. Patients presenting a diagnosis of cancer or dementia were excluded. ## 2.3. Data Collection Data were collected in the central and south Albanian regions between August 2020 and April 2021 with face-to-face interviews conducted by trained nurse research assistants. A paper survey was purposefully developed by the researchers for the study. To assure the quality of data, random data monitoring was performed by the principal investigator (MDM). ## 2.4. Measurements The Self-Care of Chronic Illness Inventory (SC-CII) [20] is a self-report instrument based on the middle range theory of self-care of chronic illness [7]. The self-care maintenance scale has 7 items, the self-care monitoring scale has 5 items, and the self-care management scale has 6 items. Since the self-care management scale assesses responses to symptoms, this scale can only be completed if patients have symptoms. As recommended by Riegel and colleagues [21], for item #14, we used a 5-point ordinal response scale (from 1 “not quickly” to 5 “very quickly”) to form an observed variable. Each SC-CII item is measured using a 5-point Likert scale ranging from “Never” [1] to “Always”[5] [20]. The three scales use a standardized score from 0 to 100, with higher scores indicating better self-care. The cut-off point for self-care adequacy is 70. The translation of the original English version to the Albanian version followed the Principles of Good Practice for the Translation and Cultural Adaptation Process for Patient-Reported Outcomes (PRO) measures [22]. The Self-Care Self-Efficacy Scale (SCSES) [23] was used to measure self-efficacy for self-care in chronic illness. It consists of 10 items with a total score ranging from 0 to 100, with higher scores indicating better self-care self-efficacy. The SCSES has been validated in different cultural groups, showing a supportive validity. The 12-item Short Form Health Survey (SF-12) [24] version 2 was used to measure health-related quality of life (HRQOL). The scale is composed of two components of HRQOL: the physical (PCS) and mental (MCS). Each component scoring ranges from 0 to 100, where the higher the scores, the better the HRQOL. SF-12 reliability was tested with Cronbach’s alpha, which resulted in a coefficient of 0.84 for the PCS and 0.70 for the MCS [25]. SF-12 has been extensively used in patients suffering from chronic conditions [26,27]. The 9-item Patient Health Questionnaire (PHQ-9) [28] was used to measure depressive symptoms. This scale is composed of nine items with a 0–3 scoring possibility. The total score goes from 0 to 27; a higher score indicates higher depressive symptoms. The PHQ-9 showed good psychometric scoring for cutoff point ≥ 10: Cronbach’s alpha (0.86–0.89), test–retest (0.84), sensitivity and specificity (0.88) The PHQ-9 is available in many languages, and, in Albania, the tool is also used in primary healthcare centers to diagnose people with mental health conditions. The one-item Dyadic Symptom Management Type Scale (DSMT) [29] was included to explore the organization and sharing of care activities within the patient–caregiver dyad. This scale identifies 4 types of dyadic management for chronic disease. When the patients’ and caregivers’ answers are concordant, the typologies are: [1] patient-oriented dyadic care type, where the patient performs the greatest part of self-care; [2] caregiver-oriented dyadic care type, where the caregiver performs the greatest part of self-care; and [3] collaborative-oriented type, where the patient and caregiver collaborate or complement each other in an equal manner. When the patient and caregiver provide a discordant answer, the dyad is classified as incongruent [30]. A sociodemographic questionnaire was used to collect sociodemographic characteristics and clinical data of participants, such as age, gender, education level, marital status, family income, and employment status, and the number and the type of the chronic diseases. ## 2.5. Ethical Considerations Ethical approval for the study was obtained from the Catholic University of Our Lady of Good Counsel with protocol number $\frac{237}{2020.}$ The study was carried out according to ethical standards and according to the principles of the Declaration of Helsinki [31]. All participants received adequate information regarding the study and afterwards were asked to sign the informed consent form. ## 2.6. Statistical Analysis Descriptive statistics (mean, standard deviation, percentages, and frequencies) were calculated out to describe the sample characteristics and SC-CII items. Skewness and kurtosis univariate indices were considered to evaluate the normal distribution of the items. As dimensionality testing preceded reliability testing [32], we began the psychometric analysis of SC-CII performing confirmatory factorial analysis (CFA), and then tested its reliability. Consistent with recent recommendations about self-care inventory validation studies [20,33,34,35], we performed three separate CFAs, one for each scale (self-care maintenance, self-care monitoring, and self-care management). A general model with all three SC-CII scales was tested as well, similar to previous self-care inventory validation studies [20,34,36]. For the CFAs, we tested the same factorial structure tested by Riegel et colleagues [2019] [34]. Specifically, for the self-care maintenance scale, we specified two factors as follows: ‘health-promoting behaviors’ (items #1, #3, and #8) and ‘illness-related behaviors’ (items #2, #4, #5, and #6). Consistent with previous validation studies of SC-CII [20,34,37], item 7 (‘avoiding tobacco smoke’) was excluded from our analyses. Regarding the self-care monitoring scale, we specified a unidimensional factor model including items 9 through 13. Finally, with respect to the self-care management scale, we specified two factors: ‘autonomous behavior’ (items #14, #15, #16, and #20) and ‘consulting behaviors’ (items #17, #18, and #19) [20,34,36]. Due to the non-normal distribution of SC-CII items, the maximum likelihood robust (MLR) estimator [38] was used for parameter estimation. Different fit indices were tested in the CFA: the comparative fit index (CFI), Turker and Lewis index (TLI), root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) [39,40]. The goodness of fit values were interpreted following the literature recommendations [41,42]. Consistent with previous validations of self-care inventories [33,35], reliability was calculated with the composite reliability, or omega coefficient, that is indicated for multidimensional scales [43]. For completeness, the Cronbach alpha coefficient was calculated. Considering that the SC-CII is composed of several dimensions, we computed the global reliability index as well, which is specific for multidimensional scales [44] Construct validity was tested via hypothesis testing, following Terwee’s recommendations [19] and known group differences. Specifically, we hypothesized that the SC-CII scale scores were significantly correlated with the Self-Care Self-Efficacy Scale, SF-12 (MCS and PCS), and PHQ-9 scores. Construct validity was further verified by posing the hypothesis that male patients with MCCs would have a higher score in self-care monitoring and self-care management behaviors than female patients [9], and the scores in all three scales of the SC-CII would be significantly higher in the patient-oriented dyadic care type among the typologies identified, with the patient being the major provider of self-care [45]. To test the associations with SC-CII scores, we used Pearson’s product–moment correlation coefficients with a significant p value set at <0.05. Correlations of 0.10–0.29 were considered small, 0.30–0.49 as moderate, and >0.50 as strong [46]. Differences between scores were identified through the T-test conducted using two different groups (sex and dyadic care type). Additionally, we tested the SC-CII scale measurement error with the standard error of measurement (SEM) and the smallest detectable change (SDC). These values add additional information regarding the precision of an instrument and responsiveness to changes. To measure SEM, we used the formula standard deviation (SD) √ (1—reliability coefficient) [47], where the SD was the SD of the SC-CII scale score, and the reliability coefficient was the factor score determinacy coefficient or the global reliability index for multidimensional scales. A value of SEM < SD/2 indicates a more precise instrument. To measure the SDC, we used the formula 1.96 X√2 X SEM [48]. Smaller values of SEM and SDC indicate more precision in the instrument. Analyses were performed with SPSS 26 (IBM Corp., Armonk, NY, USA) and Mplus 8.2 (Los Angeles, CA, USA) [38]. ## 3.1. Sample Description Table 1 presents the sociodemographic and clinical characteristics of the sample. On average, the patients were 73 years old, mostly male ($53.6\%$), married ($78.4\%$), retired ($94.4\%$), and with a low level of education ($62\%$). Most ($80\%$) of them perceived that they earned enough to live, and they were living with a spouse ($78\%$) and/or children ($50\%$). The mean number of chronic illnesses was 2.5 (±0.69). The patients were mainly affected by hypertension ($87.6\%$), diabetes mellitus ($74.5\%$), and heart failure ($44.8\%$). ## 3.2. Item Descriptive Analysis and Scale Scores Table 2 reports the descriptive analysis of the SC-CII items. The sample reported a mean score of 72.0 (±16.0) for self-care maintenance, 75.0 (±19.1) for self-care monitoring, and 72.4 (±15.9) for self-care management. Patients with adequate self-care maintenance, self-care monitoring, and self-care management behavior made up $53.5\%$, $58.4\%$, and $53\%$ of the sample, respectively. Item 14 (“If you had symptoms in the past month, how quickly did you recognize it as a symptom of your illness”) was excluded by the dimensionality testing, as $4.7\%$ of the sample did not present symptoms in the last 30 days. Among patients reporting chronic disease symptomatology, $3.2\%$ specified that they were not able to recognize the symptom. Those who recognized the symptom referred different rapidity of recognition: $34.6\%$ did not recognize it very quickly (score 1 or 2), $27.5\%$ recognized it fairly quickly (score 3), and $29.9\%$ recognized it quickly or very quickly (score 4 or 5). ## 3.3.1. Dimensionality Since self-care maintenance is described as comprising “health promoting behaviors” and “illness-related behaviors” [17], we specified a two-factor confirmatory model. The model presented a misfit caused by the correlation between the two factors > 1. For this reason, we respecified a one-factor model and the goodness-of-fit indices of this model were inadequate. The model misfit was caused by an excessive shared variance between items #8 “Manage stress” and #4 “Eat a special diet” and between item #8 and #3 “Do physical activity”. Since in people with chronic diseases, adherence to a diet can often be a source of stress in maintaining well-being, while the practice of physical activity is often used to manage stress, we specified the covariance between dietary adherence (item #4) and physical activity (item #3). Thus, we reran the model that yielded the following supportive fit indices: χ2 (12, $$n = 251$$) = 22.688, $$p \leq 0.0305$$, CFI = 0.967, TLI = 0.942, RMSEA = 0.060 ($90\%$ CI 0.018 0.097), $$p \leq 0.300$$, SRMR = 0.038. All factor loadings were significant (Figure 1, panel a). ## 3.3.2. Scale Internal Consistency Reliability The internal consistency reliability of the self-care maintenance scale was adequate. Specifically, the composite coefficient of the scale was 0.87 and the Cronbach’s alpha coefficient was 0.76. ## 3.4.1. Dimensionality A one-factor model was tested that produced a partially adequate fit. The cause of the misfit was due to the excessive covariance between items #12 “Monitor whether you tire more than usual doing normal activities” and #13 “Monitor for symptoms”. The proximity of these items increased the shared variance between these two items [49]. For this methodological reason, we tested a model correlation between the residuals of these two items [50,51]. The model yielded an excellent fit as follows: χ2 (4, $$n = 251$$) = 4.268, $$p \leq 0.371$$, CFI = 0.999, TLI = 0.998, RMSEA = 0.016 ($90\%$ CI = 0.000 0.098), $$p \leq 0.648$$, SRMR = 0.014. All factor loadings were significant (Figure 1, panel b). ## 3.4.2. Scale Internal Consistency Reliability The internal consistency reliability, tested using the composite coefficient, for the self-care monitoring scale was high, 0.83, attesting to the internal coherence of the items. When the Cronbach alpha coefficient was computed, a coefficient of 0.88 was obtained. ## 3.5.1. Dimensionality Since self-care management is described as comprising autonomous behaviors and consulting behaviors [17], we tested a two-factor confirmatory model, The model yielded a poor fit. The misfit was caused by excessive covariance between items #17 “Take a medicine to make the symptom decrease or go away” and #18 “Tell your healthcare provider about the symptoms at the next office visit”. Additionally, in this case, as for the self-care maintenance scale, the proximity effects of these items could have produced an increase in the shared variance. We allowed the correlation between item residuals [50,51], and the fit indexes of the new model improved: χ2 (7, $$n = 226$$) = 10.027, $$p \leq 0.20$$, CFI = 0.985, TLI = 0.968, RMSEA = 0.044 ($90\%$ CI 0.000 0.099), $$p \leq 0.506$$, SRMR = 0.035. All factor loadings were significant, and two factors correlated 0.483 ($p \leq 0.001$), (Figure 1, panel c). ## 3.5.2. Scale Internal Consistency Reliability The internal consistency reliabilities of the two self-care management factors, tested using composite coefficients, were 0.79 and 0.67 for autonomous behaviors and consultive behaviors, respectively. The Cronbach’s alpha coefficient calculated for the entire six-item scale was 0.70. The global reliability index was 0.74 for the overall self-care management scale. ## 3.6. Construct Validity through Hypothesis Testing The SC-CII scale scores correlated significantly with other measures supporting the construct validity of the instrument (Table 3). The self-care maintenance, monitoring, and management scales were significantly ($p \leq 0.001$) correlated with the self-care self-efficacy scores, depression, and mental and physical quality of life. Male patients with MCCs scored higher in self-care maintenance ($$p \leq 0.004$$) and self-care monitoring ($$p \leq 0.002$$) behaviors than female patients, but not in management behaviors. In the patient-oriented dyadic care type, patients scored higher in the self-care maintenance ($$p \leq 0.002$$), monitoring ($$p \leq 0.003$$), and management ($$p \leq 0.004$$) scales than the caregiver-oriented dyadic care type. ( Table 4) ## 3.7. Measurement Errors of the SC-CII The SEM of the SC-CII was 5.77, 7.88, and 8.11 for the self-care maintenance, self-care monitoring, and self-care management scales, respectively. These measures were considered adequate since the SEM values were <SD/2. The SDC was 6.66, 7.78, and 7.89 for the self-care maintenance, self-care monitoring and self-care management scales, respectively. ## 4. Discussion This study aimed to test the psychometric properties (dimensionality, construct validity, internal consistency reliability, and measurement error) of the SC-CII in a middle-income population. The results show adequate validity and reliability, as shown in other previous studies of psychometric validation [18,20]. To our knowledge, this is the first study to test SC-CII in a southeastern European country, with important scientific and practical implications. ## 4.1. Dimensionality Regarding the self-care maintenance scale, the CFA models reported in the literature were not confirmed in our population. In fact, while in the existing model, behaviors fitted within two dimensions, health promotion and illness-related behaviors [17], in the Albanian model, we found a one-factor solution. Additionally, the initial one-factor solution did not fit the data well, but when we allowed the covariance between the residuals of items #8 “Manage stress” and #4 “Eat a special diet” and items #8 and #3 “Do physical activity” to freely correlate, the model improved. Our interpretation of the differences that emerged is that, regarding self-care maintenance behaviors, older Albanian adults with MCCs, in contrast with older adults with MCCs in Western countries, might not distinguish between illness-related behaviors and health promotion behaviors. This can be explained by a plausible influencing effect of cultural and social characteristics of this population that are different from those of Western nations due to previous political choices and the history of the country. Leininger’s culture care theory describes existing care diversities and universalities across cultures, and recognizes the influences of historical, cultural, and social structure factors on health/wellness patterns and well-being, care meanings, expressions, or patterns [52]. More specifically, self-care in general and self-care maintenance behaviors were recently found to be influenced by cultural beliefs and social norms [53]. This study is testing the SC-CII for the first time in a middle-income country, and provides evidence that this different dimensionality can be culturally based. Further studies in other similar countries might support or hinder this finding. Regarding the common variance of residuals among items #3, #4, and #8, the specification of correlation of residuals is methodologically acceptable if it does not influence the other model parameters [54], as was the case in our model. The correlation between these items might indicate that in Albania, patients with MCCs associate “eating a special diet” and “physical activity” with stress in maintaining a healthy lifestyle, which can be explained with the reduced health literacy and an inadequate culture of prevention in this population [13,16]. The self-care monitoring scale measures patients’ observation of signs and symptoms of chronic conditions. In the self-care process, the recognition of symptoms of chronic diseases is essential for the management of symptoms and the ability to properly manage the disease [21]. One factorial model was tested, and psychometric findings were consistent with previous studies [18,20]. This suggests that the self-care monitoring behaviors explored by this scale seem to be interpreted and applied similarly by adults with MCCs across cultures and nationalities. In the model, we allowed for a covariance of residuals of items #12 “Monitor whether you tire more than usual doing normal activities” and #13 “Monitor for symptoms”. This covariance can be explained by two plausible motivations. First, the closeness of items in the scale might have influenced their covariance [49,51]. Additionally, we can hypothesize that, in the Albanian patients, tiredness seems to be a symptom that is easy to identify, and it is likely considered as a common symptom of the chronic diseases. In previous linguistic validation models of the scale, different item covariance residuals were allowed to correlate to improve the model fit. For example, in the Italian population, similar to that of the USA, residuals of items #9 and #10 were allowed to covariate, while in the Swedish population, this was applied to items #9 and #11 [18]. This suggests that symptom identification, monitoring, and association with the chronic illness can be subject to cultural influences. In fact, several studies have emphasized that culture influences symptom identification and monitoring [53]. Despite these item covariations, we can confirm that patients conceptualize the self-care monitoring in a similar way; they take actions related to the monitoring of the disease, but attribute different importance to scale items. The self-care management scale comprises patients’ behaviors in response to symptoms of a chronic disease and incorporate behaviors in two dimensions: “autonomous behavior” and “consulting behavior” [20]. Autonomous behaviors refer to patients’ spontaneous actions to relieve symptoms based on prior experience, whereas consulting behaviors include adapting recommendations from others. We tested a two-factor confirmatory model, and the psychometric findings were consistent with previous studies [18,20]. Similar to self-care monitoring, self-care management behaviors in adults with MCCs are universally interpreted. How people deal with signs and symptoms of a disease can be related to their level of education, to their confidence in the recommendations of the healthcare professionals, to cultures, or simply to concerns about their health status [13], and behaviors are similar independent from culture or nationality. We allowed the covariance of residuals between items #17 “Take medicines to make the symptom decrease or go away” and #18 “Tell your healthcare provider about the symptom at the next office visit”. The shared variance is probably explained by the proximity effects of these items [49,51]. Additionally, in the Albanian sample, item #17 loaded in the “autonomous behavior” factor, confirming similar findings from a previous study [18]. This suggests that, in the Albanian population, some patients tend to use autonomous behaviors towards self-medication, while other patients refer to primary healthcare physicians to initiate or continue treatments. Another study documented similar behaviors among Albanian patients [16]. This is in line with the culture of ‘curing’ rather than ‘preventing’ inherited from the previous regime [13]. Despite their low level of education, Albanian patients showed themselves to be able to identify symptoms, autonomously take medications to relieve symptoms, and to refer behaviors to healthcare professionals during routine visits. ## 4.2. Scale Internal Consistency Reliability The SC-CII presented good internal consistency reliability, with coefficients being higher than 0.70. This suggests that, despite the multidimensionality of the scale, the items reflect the same constructs, and can be combined into an overall score. Thus, our results indicate that the Albanian version of the SC-CII presents adequate reliability both at the factor and scale levels, indicating that the three SC scales are precise in measuring self-care (maintenance, monitoring, and management) behaviors in the multiple chronic care conditions in the sample studied. ## 4.3. Construct Validity Testing The Albanian SC-CII presented a good construct validity, as supported by the presence of positive associations between these scales and the Self-Care Self-Efficacy Scale, as postulated by theories [7,55] and established previously in single chronic illness studies [33,35,56,57] and in MCCs [58]. Furthermore, we found that the higher self-care scores were associated with a better physical and mental quality of life and lower levels of depression scores than found in previous studies [59,60]. Regarding gender influences on the self-care behaviors, we partially confirm our hypothesis that male patients performed more self-care monitoring and management than females; we found statistically significant differences in self-care maintenance and monitoring, but not in self-care management scores. We suggest that male patients are more attentive regarding healthy behaviors and observation of sign and symptoms than female ones, but regarding the management of symptoms, they present similar behaviors. Our data are consistent with a previous study that showed that men and women alike seek explanations for bodily changes and take appropriate and timely action to manage signs and symptoms [61]. Finally, consistent with the classification system of patient–caregiver dyads proposed by Buck et al. [ 2019] [30], we found that in the patient-oriented dyadic typology, scores were higher in all the SC-CII scales when compared to the other dyadic typologies. This confirms that SC-CII is sensitive enough to capture differences in the levels of self-care maintenance, monitoring, and management. ## 4.4. Measurement Errors of the SC-CII Measurement error testing is an important test to perform when validating scales. Regarding the SC-CII, SEM and SDC testing reported small scores. In fact, SEM values were <SD/2 for each self-care scale, suggesting an acceptable measurement error; in the SDC testing we provided the following reference points for a meaningful change in the self-care scales: 6.66 for self-care maintenance, 7.78 for self-care monitoring, and 7.89 for the self-care management scale. Therefore, we can assume that the inventory is accurate to measure self-care behaviors in chronic conditions. ## 4.5. Strengths and Limits Our study presents several strengths. This is the first study to test SC-CII in a middle-income country, bringing significant knowledge to the literature on self-care. Additionally, we enrolled an adequate sample size of participants, and we used robust psychometric testing and a rigorous methodology to establish validity and reliability. Despite its strengths, our study also has several limitations. First, we enrolled a convenience sample. We tried to overcome this limit by enrolling people of different sexes and ages, with a number of various chronic diseases, and from several centers in central and south Albania. However, the SC-CII needs further testing in other middle- and low-income countries to confirm or reject the findings of this study. ## 4.6. Implication for Clinical Practice and Research The Albanian SC-CII is a reliable and valid instrument to use in clinical practice. Clinicians can use the SC-CII to identify the self-care behaviors of people with chronic illness and use that information to improve patients’ and their caregivers’ knowledge regarding healthy lifestyles and symptoms of diseases, and teach them appropriate management skills, or to monitor variations in the patient’s ability to self-care over time. Additionally, healthcare professionals can use this scale to compare general self-care behaviors of patients with behaviors regarding specific chronic diseases, and, therefore, they will be able to integrate the plan of care for the patient better and define appropriate interventions. ## 5. Conclusions The Self-Care in Chronic Illness Inventory (SC-CII) is a theory-based, valid, and reliable instrument to measure self-care behaviors in the chronic illness adult population. 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--- title: Neoplastic and Autoimmune Comorbidities in Patients with Primary Cutaneous B-Cell Lymphoma authors: - Roberto Mazzetto - Jacopo Tartaglia - Alvise Sernicola - Mauro Alaibac journal: Hematology Reports year: 2023 pmcid: PMC10048514 doi: 10.3390/hematolrep15010016 license: CC BY 4.0 --- # Neoplastic and Autoimmune Comorbidities in Patients with Primary Cutaneous B-Cell Lymphoma ## Abstract Primary cutaneous B-cell lymphomas (PCBCLs) constitute a rare subset of non-Hodgkin lymphoma (NHL), with distinctive clinical and biological characteristics. The risk of autoimmune or neoplastic comorbidities in subjects with NHL has been extensively reported in the literature, but the data available are not directly applicable to PCBCLs. The aim of our study was to determine the frequency of relevant medical conditions, with a primary focus on autoimmune and neoplastic disorders, in subjects with PCBCL. We performed a retrospective observational study involving 56 patients diagnosed histologically with PCBCL and 54 sex- and age-matched controls. Our results show a statistically significant association for neoplastic comorbidities in general ($41.1\%$ vs. $22.2\%$, $$p \leq 0.034$$) and hematological malignancies specifically ($19.6\%$ vs. $1.9\%$, $$p \leq 0.0041$$) with PCBCL compared to controls. We did not highlight a statistically significant difference in the frequency of autoimmune comorbidities ($21.4\%$ vs. $9.3\%$, $$p \leq 0.1128$$) and of chronic viral hepatitis ($7.1\%$ vs. 0, $$p \leq 0.1184$$). Finally, type 2 diabetes ($19.6\%$ vs. $1.9\%$, $$p \leq 0.0041$$) was significantly associated with PCBCL. Our preliminary data supporting the association between PCBCLs and neoplastic disorders suggest that altered immune surveillance may be a common predisposing mechanism. ## 1. Introduction Despite the abundance of data in the literature regarding the association between non-Hodgkin lymphoma (NHL) and autoimmune or neoplastic comorbidities, there is a lack of studies evaluating the association between these comorbidities and primary cutaneous B-cell lymphoma (PCBCL). This subgroup has distinct biological and clinical characteristics compared to the nodal types of non-Hodgkin lymphoma and is generally associated with a more indolent course [1]. Specifically, the PCBCLs include three major entities: primary cutaneous follicular lymphoma, primary cutaneous lymphoma of the marginal zone and primary diffuse cutaneous large B-cell lymphoma, leg type [2]. Primary cutaneous follicular lymphoma and primary cutaneous lymphoma of the marginal zone are considered particularly indolent and are associated with a disease-specific 5-year survival greater than $95\%$ [3]. By contrast, primary cutaneous diffuse large B-cell lymphoma is characterized by biologically aggressive behavior, a higher frequency of extra-nodal involvement and a significantly worse prognosis, with a 5-year disease-specific survival of $50\%$ [2]. Since these differences set PCBCLs significantly apart from other subtypes of NHL, the disease associations established in the literature for extra-cutaneous NHL may not apply to PCBCLs. In this regard, there is a lack of published evidence in the literature concerning the comorbidities associated with PCBCLs. Some data were provided by Guitart et al., who investigated possible associations between PCBCLs and other relevant medical conditions in a cohort of 80 patients, highlighting a high incidence of gastrointestinal diseases (a broad category of conditions including gastroesophageal reflux, gastric ulcer, irritable bowel syndrome and inflammatory bowel disease) [4]. Moreover, the authors reported a significantly increased incidence of autoimmune diseases, such as Hashimoto’s thyroiditis, systemic lupus erythematosus (SLE) and Sjogren’s syndrome. Finally, a history of extracutaneous malignant tumors was found to be more frequent in patients with cutaneous marginal zone B-cell lymphomas ($$p \leq 0.05$$). Recently, S. Hu et al. evaluated the association between NHL and autoimmune comorbidities in a large cohort of patients [5]. In total, $2.9\%$ of the patients were affected by autoimmune comorbidities and the diseases most commonly associated with NHL were Sjogren’s syndrome, autoimmune cytopenia, rheumatoid arthritis, SLE, Hashimoto’s thyroiditis, and dermatomyositis/polymyositis. Other studies evaluated these associations. For example, M. Fallah et al. studied a cohort of 878,161 patients with autoimmune diseases to determine whether there was an increased risk of NHL in this patient group [6]. A significant increase in the risk of NHL in patients with autoimmune comorbidities was found in both sexes. The risk of all the most common histological subtypes of NHL was found to be significantly higher in patients with autoimmune diseases. Although subtype analysis highlighted an increased risk for cutaneous/peripheral T-cell and anaplastic T-cell lymphomas, the risk for PCBCLs was not assessed individually. In physiopathological terms, the causes of NHL remain poorly understood. Nonetheless, the association with autoimmune diseases shown by several large studies in the literature strongly supports their role as important predisposing factors [7,8]. First, the presence of continuous antigenic pressure and chronic inflammation in patients with autoimmune conditions stimulates the proliferation of T and B lymphocytes, which leads to an increased risk of the accumulation of genetic mutations in these cells, which in turn is associated with lymphomagenesis [9]. Second, the chronic use of immunosuppressive drugs, whose role in promoting oncogenesis is well-known, in patients with autoimmune diseases could be an additional predisposing factor [10]. Finally, the literature shows an increased risk of neoplastic comorbidities, with Hodgkin’s lymphoma, lung cancer, brain cancer, melanoma, and non-melanoma skin cancer being the most common in patients with NHL [11,12,13,14]. The aim of our study is to assess whether PCBCL patients are at risk for other relevant medical conditions, with a primary focus on autoimmune and neoplastic disorders. ## 2.1. Study Design and Setting We performed a retrospective observational study employing the Galileo e-health application (Dedalus Italia S.p. A., Florence, Italy), collecting our hospital’s electronic medical records. This platform registers sociodemographic data, as well as diagnoses and written reports generated during outpatient visits and hospitalization. Electronic medical records were reviewed to collect data for comorbidities in patients diagnosed histologically with PCBCL from inception to 13 April 2022. Our study included 56 patients with a histological diagnosis of PCBCL according to the 2018 update of the WHO-EORTC classification for primary cutaneous lymphomas [14]. Specifically, 37 of the patients had primary cutaneous follicle center lymphoma, 13 primary cutaneous marginal zone lymphomas, and 6 had primary cutaneous diffuse large B-cell lymphoma. Our patient population consisted of 32 females and 24 males with a median age of 66 years. Subjects attending our outpatient office for routine skin examination were included in a group of 54 sex- and age-matched healthy controls. The control group was chosen from individuals who visited our department, either voluntarily or in response to awareness campaigns, for a general dermatologic assessment and without a specific request for a suspicious skin lesion to be evaluated. Health data were retrospectively collected from our hospital’s electronic medical records. Demographic characteristics are reported in Table 1. In both the PCBCL and the control groups, additional diagnoses were systematically investigated demonstrating the following comorbidities: thyroiditis, systemic lupus erythematosus, Behcet’s disease, lung cancer, monoclonal gammopathy of undetermined significance (MGUS), breast cancer, chronic lymphocytic leukemia/lymphoma, thyroid cancer, essential thrombocythemia, prostate cancer, colon cancer, melanoma, basal cell carcinoma, gastroenteritis, gastroesophageal reflux disease, heart transplant, HIV, hypogammaglobulinemia, HBC, HCV, type 2 diabetes (DM2), ulcerative colitis, and psoriasis. To mitigate the confounding effect of age, we compared two samples with similar ages considering that the assessed comorbidities are more prevalent among the elderly: mean age ± standard deviation was 68.7 ± 15.4 years in the PCBCL group and 66.8 ± 7.99 years in the control group (Table 1). ## 2.2. Ethics Ethical review and approval were waived for this study, as they were not required by our Institutional Ethics Committee considering the retrospective nature of this observational registry study. Our hospital’s electronic medical records database was used in compliance with relevant legislation regarding data protection and patient privacy and the study was performed according to the principles of good clinical practice. Informed consent was obtained from all subjects involved in the study. ## 2.3. Statistical Analysis Descriptive statistics were expressed as median and mean values or as absolute and relative frequencies (percentages). The chi-squared test was performed to compare frequencies of comorbidities between patients and controls. Fisher’s exact test was also used for analysis in cases in which frequency was below 10. Statistical significance was set for a value of $p \leq 0.05.$ Statistical analyses were performed using Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). ## 3.1. Malignant Neoplasms A history of systemic malignant neoplasms was observed in twenty-three of the patients ($41.1\%$ vs. $22.2\%$, $$p \leq 0.034$$, chi-squared test), eleven of whom ($19.6\%$) had a hematological disorder, including MGUS, chronic lymphocytic leukemia or other non-cutaneous lymphomas and essential thrombocythemia. Therefore, we found a statistically significant association between PCBCL and hematological diseases ($19.6\%$ vs. $1.9\%$, $$p \leq 0.0041$$, Fisher’s exact test). The MGUS had a higher frequency in the PCBCL than in the controls ($12.5\%$ vs. $1.9\%$); however, this difference did not reach statistical significance ($$p \leq 0.0608$$, Fisher’s exact test; Table 2). To further investigate this aspect, we obtained available data on the immunoglobulin phenotypes of MGUS and PCBCL in our patients, which are presented in Table 3. We are unable to provide the immunoglobulin heavy-chain profile of the cutaneous lymphoma clone in all the MGUS patients, since this assessment was not performed by our laboratory; however, the light-chain expression of PCBCL is reported when present. Finally, the diagnosis of basal cell cancer was considered separately from the other neoplastic diseases in our analysis in consideration of the high frequency of this condition in the age groups represented in our study population: this type of skin tumor was diagnosed in 7 ($12.5\%$) of the subjects with PCBCL and in 10 ($18.5\%$) of the controls, showing no statistically significant difference between the two groups ($$p \leq 0.4367$$, Fisher’s exact test). ## 3.2. Autoimmune Diseases Furthermore, our study showed no statistically relevant association between autoimmune diseases and PCBCL. We reported autoimmune thyroiditis as the most common pathology both in patients and controls, while SLE and Behcet’s disease were observed only in the PCBCL cohort group; in any case, none of these comorbidities showed a statistical association with PCBCL ($21.4\%$ vs. $9.3\%$, $$p \leq 0.1128$$, Fisher’s exact test; Table 4). ## 3.3. Other Comorbidities Even though three patients ($5.4\%$) suffered from a form of immunosuppression (hypogammaglobulinemia, HIV and heart transplant) while none of the controls did, we did not find a statistically significant association with an immune disorder ($$p \leq 0.2434$$, Fisher’s exact test). Surprisingly, we found eleven patients with DM2 whose results were significantly associated with PCBCL ($19.6\%$ vs. $1.9\%$, $$p \leq 0.0041$$, Fisher’s exact test). Moreover, $7.1\%$ of the cases had chronic viral hepatitis (HBV or HCV), which was found in none of the controls, although a statistical association was not reached ($$p \leq 0.1184$$, Fisher’s exact test). Finally, eleven controls ($20.4\%$) had a history of gastrointestinal tract disorders (gastroenteritis, GERD, ulcerative colitis) while just eight ($14.3\%$) among patients with PCBCL reported this ($$p \leq 0.4558$$, Fisher’s exact test). Other notable comorbidities, such as psoriasis, did not reach statistical significance. Table 5 summarizes the results for the other conditions not included in the previous groups. ## 4. Discussion Our data suggest an association between neoplastic comorbidities and hematological disorders with PCBCL. The relationship between NHL and other oncological comorbidities is well documented in the literature, with several published studies reporting this association. In particular, Chattopadhyay et al. investigated the risk of second primary cancers (SPC) in NHL patients by conducting a bidirectional analysis (that is, of second primary cancers after NHL and NHL as SPC) [15]. This analysis was performed using data from a large cohort of 19,833 patients. After NHL diagnosis, there was an increased risk of Hodgkin’s lymphoma, squamous cell carcinoma, kidney cancer, melanoma, bladder cancer, colorectal cancer, and upper aerodigestive tract cancer. The highest relative risk was found with hematological SPCs (primarily Hodgkin’s lymphoma). Our data show a statistically significant association between PCBCL and neoplastic comorbidities in general and hematological neoplasms specifically. The mechanism underlying this association is not known, but several hypotheses have been formulated. Radiotherapy and chemotherapy, which are widely used treatments for NHL, may be the underlying causes of many SPCs in NHL patients, as their mutagenic and oncogenic potential is a well-known side effect [16]. However, this hypothesis alone may not be sufficient to explain the increased incidence of neoplastic comorbidities in PCBCL patients. As a matter of fact, given their particularly indolent course, PCBCLs are rarely treated with the chemo- and radiotherapy approaches often used for extracutaneous NHL. One of the hypotheses proposed to explain this association is the presence of individual immune-system dysfunction in patients with NHL [17], regardless of iatrogenic immune suppression. It has been proposed that this immune imbalance may depend on the down-regulation of T-cell function driven by NF-kB, which contributes to immune suppression by inducing the activation of immune-suppressor cells [18]. Among the hematological disorders, the $12.5\%$ frequency of MGUS in PCBCL, although not statistically significant, may hint at an altered immune surveillance, which might favor PCBCL onset. Furthermore, it may also suggest a hypothetical pathological mechanism in common. In this regard, a similar association was observed in Waldenstrom macroglobulinemia (WM) patients by Varettoni et al. who found a higher risk of second cancers compared with the general population [19]. On the other hand, monoclonal gammopathies might also be the result of immunoglobulin production by PCBCL and, in this case, would be regarded as serological markers. A retrospective study conducted on 23 patients with PCMZL demonstrated concordant paraproteins in tissue samples and in the blood, suggesting that in PCMZL paraproteinemia may serve as a tumor marker [20]. In our study, there was no shared light-chain expression between MGUS and PCBCL, considering cases for which these data were available. According to these observations, we hypothesized that MGUS might be a comorbidity rather than just a serological marker of PCBCL. However, we were not able to demonstrate a significant association between MGUS and PCBCL in our sample and further data will be required to support this hypothesis. We found a statistically significant association between DM2 and PCBCLs. This association could also indicate the presence of a “dys-immune” condition in PCBCL patients. The DM2 was diagnosed before the discovery of PCBCL in our patient group: this temporal association allowed us to rule out the possibility that DM2 could be a side effect of subsequent steroid therapy administered for the treatment of PCBCL. Although it is well known that DM2 and metabolic syndrome are risk factors for many neoplastic disorders [21], we are the first to report a significantly higher prevalence of DM2 in PCBCL patients. Hyperglycemia in diabetes is thought to cause dysfunction in the immune system through a wide range of mechanisms, which include impaired cytokine production, the inhibition of leukocyte recruitment, dysfunction in natural killer cells and the inhibition of antibodies and complement function [22]. All these mechanisms could contribute to a reduction in the effectiveness of tumor surveillance and, ultimately, to an increase in carcinogenesis. Another mechanism through which diabetes is hypothesized to promote carcinogenesis is the insulin-like growth factor 1 (IGF-1)-mediated stimulation of cell proliferation. Since IGF-1 receptors have been found in a variety of human malignancies, insulin may have an influence on cancer cell proliferation in vivo. Insulin stimulates the synthesis of IGF-1 in the liver by up-regulating growth hormone receptors. Hyperinsulinemia can also raise IGF-1 levels by lowering IGF-binding-protein production in the liver [23]. Moreover, our data show hepatotropic virus infection (HBV and HCV) in $7.1\%$ of the subjects with PCBCLs and in none of the controls, although this difference did not reach statistical significance. The association between chronic hepatotropic virus infection and extrahepatic carcinogenesis (most notably NHL) is well known in the literature [24]. The very high prevalence of HCV infection in individuals with mixed cryoglobulinemia was the original observation that prompted the further exploration of this link [25]. The relative risk (RR) of all forms of NHL among HCV-positive patients was determined to be 2.5 in a meta-analysis that included 15 studies [26]. The link between NHL and HBV has received far less attention than the link between NHL and HCV. Nonetheless, a meta-analysis taking into account 12 studies confirmed the presence of a statistically significant association between chronic HBV infection and NHL [27]. Although the exact mechanism underlying this association is not known, several hypotheses have been formulated. The first proposed mechanism is chronic antigenic stimulation: when B cells undergo chronic, antigen-driven proliferation, there is the possibility of these cells accumulating mutations in their DNA, leading to the emergence of a malignant B-cell clone [28]. Another proposed mechanism is the direct viral infection of B lymphocytes: HCV-infected cells, including B cells, have been found to have a mutator phenotype due to the induction of activation-induced cytidine deaminase and the production of error-prone DNA polymerase [29]. While each of these mechanisms could account for an increased frequency of PCBCLs in patients affected by chronic hepatitis, our results did not demonstrate a statistically significant correlation between the two conditions, and additional data will be necessary to support this potential association. Further in-depth research is required to better understand how these comorbidities are related to PCBCL and how they may affect the prognoses of these individuals. While we were not able to assess the influence of all the comorbidities on disease, we obtained specific data on the immunoglobulin phenotypes in a subset of patients with PCBCL and MGUS, which are presented in Table 3. Although immunosuppression is a known risk factor in lymphoproliferative disorders, this study highlights how patients with PCBCL are not affected by conditions that down-regulate the immune system, such as organ transplant or HIV infection. Due to the rarity of PCBCL, our investigation did not deal with the different lymphoma subtypes separately, grouping together entities with indolent as well as aggressive behavior. In this regard, it has been reported that aggressive forms of CBCL are generally observed in the context of immunosuppression [30,31]. Contrary to reports by Guitart et al., we did not find a statistically significant association with autoimmune diseases [4]. From the findings in the literature, it is known that the chronic inflammatory stimulus caused by autoimmune diseases increases the risk of NHL [31]. The lack of a statistically significant association between autoimmune conditions and PCBCLs in our patient cohort most likely depends on the small size of our sample. In contrast to Guitart et al. ’s observations, we did not find a statistically significant increase in gastrointestinal tract disorders in patients with PCBCL, hinting at probable mechanisms related to PCMZL pathogenesis that are not shared by the other subtypes of PCBCL included in our cohort. ## 5. Conclusions In conclusion, our data show a statistically significant increase in the frequency of neoplastic comorbidities in patients with PCBCL compared to controls. More specifically, we found a significant association between PCBCLs and hematological disease. By contrast, autoimmune diseases did not show a statistical association. Moreover, DM2 was significantly associated with PCBCL. The immunological events induced by these conditions are responsible for different types of immune dysregulation, suggesting that altered immune surveillance might favor PCBCL onset. The main limitation of our study was the insufficient number of subjects in the PCBCL cohort and the healthy control cohort, which prevented further robust conclusions and an additional analysis of the reciprocal associations between comorbidities based on this set of data. While we were unable to include more patients due to the rarity of this condition, our sample size is consistent with that of other studies in the published literature on PCBCL [4,20]. We hope to be able to contribute to future multicenter studies that can collect robust data to support our preliminary findings on the risk factors and comorbidities in patients with PCBCL. A further limitation of the present study was that our sample of healthy subjects was not well suited to the controls, constituting a quite heterogenous population compared with that of the subjects with PCBCL. Specifically, our age- and sex-matched controls showed a relatively high rate of malignancy, but their characteristics were similar to those of healthy controls included in previous studies [4]. To mitigate the risk of bias, an additional control group should be enrolled to provide a validation cohort for future studies. Moreover, the possibility of using patients with systemic DLBCL also presenting with skin lesions as a control group to overcome these limitations was not feasible for the present study due to the inadequate number of controls with these characteristics. 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--- title: Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement authors: - Ghadah Alwakid - Walaa Gouda - Mamoona Humayun journal: Healthcare year: 2023 pmcid: PMC10048517 doi: 10.3390/healthcare11060863 license: CC BY 4.0 --- # Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement ## Abstract Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement. Augmentation techniques were then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of $98.7\%$ for case 1 and $80.87\%$ for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model’s performance and learning ability. ## 1. Introduction The progressive eye disease known as DR is a direct result of having mellitus. Increases in blood glucose occur chronically in people with diabetes mellitus where the pancreas does not generate or release enough blood adrenaline [1,2]. Most diabetics go blind from DR, especially those of retirement age in low-income nations. Early identification is crucial for preventing the consequences that can arise from chronic diseases such as diabetes [3,4]. Retinal vasculature abnormalities are the hallmark of DR, which can progress to irreversible vision loss due to scarring or hemorrhage [1,5]. This may cause gradual vision impairment and, in its most severe form, blindness. It is not possible to cure the illness, so treatment focuses on preserving the patient’s present level of eyesight [6,7]. In most cases, a patient’s sight may be saved if DR is diagnosed and treated as soon as possible. In order to diagnose DR, an ophthalmologist should inspect images of the retina manually, which is an expensive and time-consuming process [8]. The majority of ophthalmologists today still use the tried-and-true method of analyzing retinal pictures for the presence and type of different abnormalities in order to diagnose DR. Microaneurysms (MIA), hemorrhages (HEM), soft exudates (SOX), and hard exudates (HEX) are the four most common forms of lesions identified [1,9], which can be identified as the following:In earlier DR, MA appear as tiny, red dots on the retina due to a weakening in the vessel walls. The dots have distinct borders and a dimension of 125 μm or less. There are six subtypes of microaneurysms, but the treatment is the same for all of them [10,11].In contrast to MA, HM are characterized by big spots on the retina with uneven edge widths of more than 125 μm. A hemorrhage can be either flame or blot, according to whether the spots are on the surface or deeper in the tissue [12,13].The swelling of nerve fibers causes soft exudates, which appear as white ovals on the retina as defined as SX [1,9].Yellow spots on the retina, known as EX, are the result of plasma leakage. They extend across the periphery of the retina and have defined borders [1,2].Lesions caused by MA and HM tend to be red, while blemishes caused by the two forms of exudates tend to be bright. There are five distinct stages of DR that can be detected: no DR, mild DR, moderate DR, severe DR, and proliferative DR [13], as shown in Figure 1.For DR diagnosis to be performed manually, experts in the field are needed, even though the most expert ophthalmologists have problems due to DR variability. Accurate machine learning techniques for automated DR detection have the ability to those defects [2,8].Our objective was to develop a quick, fully automated DL based DR categorization that may be used in practice to aid ophthalmologists in assessing DR. DR can be prevented if it is detected and treated quickly after it first appears. To achieve this goal, we trained a model using innovative image preprocessing techniques and an Inception-V3 [14,15] model for diagnosis using the publicly available APTOS dataset [16]. Below, we highlight the original contributions of our study. This research presents two cases scenarios. In case 1, an optimal technique for DR stage enhancement using CLAHE followed by ESRGAN techniques was developed. In case 2 no enhancement was applied to the images. Due to the class imbalance in the dataset, oversampling was required using augmentation techniques. In addition, we trained the weights of each model using Inception-V3, and the results of the models were compared using APTOS dataset images. Section 2 provides context for the subsequent discussion of the related work. Section 4 presents and analyzes the results of the technique described in Section 3, and Section 5 summarizes the research. ## 2. Related Work There are various issues with DR picture detection when done manually. Numerous patients in underdeveloped nations face challenges due to a shortage of competence (trained ophthalmologists) and expensive tests. Because of the importance of timely detection in the fight against blindness, automated processing methods have been devised to facilitate accessibility for accurate and speedy diagnosis and treatment. Automated DR classification accuracy has recently been achieved by Machine Learning (ML) models trained on ocular fundus pictures. A lot of work has gone into developing automatic methods that are both efficient and inexpensive [19,20,21]. This means that these methods are now universally superior to their traditional counterparts. Following, we present a deeper examination of the two primary schools of thought in DR categorization research: classical, specialist approaches, and state-of-the-art, machine-learning-based approaches. For instance, Kazakh-British et al. [ 22], performed experimental studies with a relevant processing pipeline that extracted arteries from fundus pictures, and then a CNN model was trained to recognize lesions. Other work presented by Alexandr et al. [ 23] contrasted two widely-used classic designs (DenseNet and ResNet) with a new, enhanced structure (EfficientNet). Use of the APTOS symposium dataset allowed for the retinal image to be classified into one of five categories. Local binary convolutional neural network (LBCNN) deterministic filter generation was introduced by Macsik et al. [ 24] which mimicked the successfulness of the CNN with a smaller training set and less memory utilization, making it suitable for systems with limited memory or computing resources. Regarding binary classification of retinal fundus datasets into healthy and diseased groups, they compared their method with traditional CNN and LBCNN that use probabilistic filter sequence. Al-Antary & Yasmine [19] suggested a multi-scale attention network (MSA-Net) for DR categorization. The encoder network embeds the retina image in a high-level representational space, enriching it with mid- and high-level characteristics. A multi-scale feature pyramid describes the retinal structure in another location. In addition to high-level representation, a multi-scale attention mechanism improves feature representation discrimination. The model classifies DR severity using cross-entropy loss. The model detects healthy and unhealthy retina pictures as an extracurricular assignment using weakly annotations. This surrogate task helps the model recognize non-healthy retina pictures. EyePACS and APTOS datasets performed well with the proposed technique. Medical DR identification was the focus of an investigation by Khalifa et al. [ 25] on deep transfer learning models. A series of experiments was conducted with the help of the APTOS 2019 dataset. Five different neural network architectures (AlexNet, Res-Net18, SqueezeNet, GoogleNet, VGG16, and VGG19) were used in this research. Selecting models with fewer layers than DenseNet and Inception-Resnet was a key factor. Model stability and overfitting were both enhanced by additional data. Hemanth et al. [ 26] presented a convolutional neural network–based approach to DR detection and classification. They employed HIST and CLAHE to improve contrast in the images, and the resulting CNN model achieved $97\%$ accuracy in classification and a $94\%$ F-measure. Maqsood et al. [ 27] introduced a new 3D CNN model to localize hemorrhages, an early indicator of DR, using a pre-trained VGG-19 model to extract characteristics from segmented hemorrhages. Their studies used 1509 photos from HRF, DRIVE, STARE, MESSIDOR, DIARETDB0, and DIARETDB1 databases and averaged $97.71\%$ accuracy. Das et al. [ 28] suggested a unique CNN for categorizing normal and abnormal patients utilizing the fundus images. The blood arteries were recovered from the images using a maximal principal curvature approach. Adaptive histogram equalization and morphological opening were used to correct improperly segmented regions. The DIARETDB1 dataset was considered, and an accuracy and precision of $98.7\%$ and $97.2\%$, respectively, was attained. Wang et al. [ 29] created Lesion-Net to improve the encoder’s representational power by including lesion detection into severity grading. InceptionV3 trained and verified the design. Liu et al. [ 30] used TL with different models to investigate DR from EyePACS. A new cross-entropy loss function and three hybrid model structures classified DR with $86.34\%$ accuracy. Table 1 summarizes the many attempts to detect DR anomalies in photos using various DL techniques [19,24,31,32,33,34,35,36,37]. According to the results of the research into DR identification and diagnostic methods, there are still a lot of loopholes that need to be investigated. For example, there has been minimal emphasis on constructing and training a bespoke DL model entirely from the beginning because of a lack of a large amount of data, even though numerous researchers have obtained excellent dependability values with pre-trained models using transfer-learning. Ultimately, training DL models with raw images instead of preprocessed images severely restricts the final classification network’s scalability, as was the case in nearly all of these studies. In order to resolve these problems, the current research created a lightweight DR detection system by integrating multiple layers into the architecture of pre-trained models. This leads to a more efficient and effective proposed system that meets users’ expectations. ## 3. Research Methodology For the DR detection system to operate, as shown in Figure 2, a transfer DL strategy (Inception-V3) was retrained in the image dataset to learn discriminative and usable feature representations. This section offers a concise summary of the method followed when working with the provided dataset. The preprocessing stage is then clearly outlined, and implementation specifics of the proposed system are covered. These include the two cases scenarios used in this context, the preprocessing techniques proposed, the basic design, and the training methodology for the approach that was ultimately chosen. ## 3.1. Data Set Description Selecting a dataset with a sufficient number of high-quality photos is crucial. This study made use of the APTOS 2019 (Asia Pacific Tele-Ophthalmology Society) Blindness Detection Dataset [16], a publicly available Kaggle dataset that incorporates a huge number of photos. In this collection, high-resolution retinal pictures are provided for the five stages of DR, classified from 0 (none) to 4 (proliferate DR), with labels 1–4 corresponding to the four levels of severity. There are 3662 retinal pictures in total; 1805 are from the “no DR” group, 370 are from the “mild DR” group, 999 are from the “moderate DR” group, 193 are from the “severe DR” group, and 295 are from the “proliferate DR” group, as illustrated in Table 2. Images are 3216 × 2136 pixels in size, and Figure 1 shows some examples of these kind of pictures. There is background noise in the photographs and the labels, much like any real-world data set. It is possible that the provided images will be flawed in some way, be it with artifacts, blurriness, improper exposure, or some other issue. The photos were collected over a long period of time from a number of different clinics using different cameras, all of which contribute to the overall high degree of diversity. ## 3.2. Proposed Methodology An automatic DR classification model was developed using the dataset referenced in this paper; its general process is demonstrated in Figure 1. It demonstrates two different scenarios: case 1 in which the preprocessing step is performed using CLAHE followed by ESRGAN is used, and case 2 in which neither step is performed, while using augmentation of the images to prevent overfitting in both scenarios. Lastly, images were sent into the Inception-V3 model for classification step. ## 3.2.1. Preprocessing Using CLAHE and ESRGAN Images of the retinal fundus are often taken from several facilities using various technologies. Consequently, given the high intensity variation in the photographs used by the proposed method, it was crucial to enhance the quality of DR images and get rid of various types of noise. All images in case 1 underwent a preliminary preprocessing phase prior to augmentation, and training necessitated various stages:CLAHEResize each picture to 224 × 224 × 3 pixels. ESRGANNormalization Figure 3 shows that first, CLAHE (shown in Figure 4) was used to improve the DR image’s fine details, textures, and low contrast by redistributing the input image’s lightness values [38]. Utilizing CLAHE, the input image was first sectioned into four small tiles. Each tile underwent histogram equalization with a clip limit, which involved five steps: computation, excess calculation, distribution, redistribution, and scaling and mapping using a cumulative distribution function (CDF). For each tile, a histogram was calculated, where bins value above the clip limit were aggregated and spread to other bins. Histogram values were then calculated using CDF for the input image pixel scale and then mapped tile to CDF values. To boost contrast, bilinear interpolation stitched the tiles together [39]. This technique improved local contrast enhancement while also making borders and slopes more apparent. Following this, all photos were scaled to suit the input of the learning model, which was 224 × 224 × 3. Figure 3 depicts the subsequent application of ESRGAN on the output of the preceding stage. ESRGAN [40] (shown in Figure 5) pictures can more closely mimic image artifacts’ sharp edges [41]. To improve performance, ESRGAN adopted the basic architecture of SRResNet, in which Residual-in-Residual Dense Blocks are substituted for the traditional ESRGAN basic blocks, as shown in Figure 5. Intensity differences between images can be rather large, thus images were normalized so that their intensities fell within the range −1 to 1. This kept the data within acceptable bounds and removed noise. As a result of normalization, the model was less sensitive to variations in weights, making it easier to tune. Since the method shown in Figure 3 improved the image’s contrast while simultaneously emphasizing the image’s boundaries and arcs, it yielded more accurate findings. ## 3.2.2. Data Augmentation Data augmentation was implemented on the training set to increase the number of images and alleviate the issue of an imbalanced dataset before exposing Inception-V3 to the dataset images. In most cases, deeper learning models perform better when given more data to learn from. We can utilize the characteristics of DR photos by applying several modifications to each image. A deep neural network (DNN) is unaffected by any changes made to the input image, including scaling it up or down, flipping it horizontally or vertically, or rotating it by a certain number of degrees. Regulating the data, minimizing overfitting, and rectifying imbalances in the dataset are all accomplished through the use of data augmentations (i.e., shifting, rotating, and zooming). One of the transformations used in this investigation was horizontal shift augmentation, which involves shifting the pixels of an image horizontally while maintaining the image’s aspect ratio, with the step size being specified by an integer between 0 and 1. Another kind of transformation is rotation, in which the image is arbitrarily rotated by an angle between 0 and 180 degrees. To create fresh samples for the network, all prior alterations to the training set’s images were applied. In this study, two scenarios were utilized to train Inception-V3. The first was to apply augmentation to the enhanced images, as depicted in Figure 6, and the second was to apply augmentation to the raw images, as depicted in Figure 7. Both Figure 4 and Figure 5 are attempts to expand data volume by making slightly modified copies of current data or by synthesizing data generated from existing data while keeping all other parameters constant (Figure 4 and Figure 5), with the same total number of images in both cases. In a second use of data augmentation techniques, the issues of inconsistent sample sizes and complicated classifications were resolved. As seen in Table 2, the APTOS dataset exemplifies the “imbalanced class” because the samples are not distributed evenly throughout the several classes. After applying augmentation techniques to the dataset, the classes are obviously balanced for both scenarios, as depicted in Figure 8. ## 3.2.3. Learning Model (Inception-V3) In this section, the approach’s fundamental theory is outlined and explained. Inception-v3 [11,12] is among transfer learning pretrained models, superseding the original architecture for Inception-v1 [42] and Inception-v2 [43]. The Inception-v3 model is trained using the ImageNet datasets [44,45], which contain the information required for identifying one thousand classes. The error rate for the top five in ImageNet is $3.5\%$, while the error rate for the top one was lowered to $17.3\%$. Inception was influenced in particular by technique of Serre et al. [ 46], which processes information in several stages. By adopting the Lin et al. [ 47] method, the developers of Inception were able to improve the model precision of the neural networks, making them a significant design requirement. As a result of the dimension reduction to 11 convolutions, this also protected them from computing constraints. Researchers were able to significantly reduce the amount of time and effort spent on DL picture classification using Inception [48]. Using only the theoretical explanations offered by Arora et al. [ 49], they emphasized discovering an optimal spot between the typical technique of improving performance—increasing both depth and size—and layer separability. When utilized independently, both procedures are computationally expensive. This was the fundamental goal of the 22-layer architecture employed by the Inception DL system, in which all filters are learned. On the basis of research by Arora et al. [ 49], a correlation statistical analysis was developed to generate highly associated categories that were input into the subsequent layer. The 1 × 1 layer, the 3 × 3 layer, and the 5 × 5 convolution layer were all inspired by the concept of multiscale processing of visual data. Each of these layers eventually becomes a set of 1 × 1 convolutions [48] following a process of dimension reduction. ## 4.1. Instruction and Setup of Inception-V3 To demonstrate the effectiveness of the deployed DL system and to compare results to industry standards, tests were carried out on the APTOS dataset. The dataset was divided into three categories in accordance with the suggested training method. Eighty percent of the data was utilized for training (9952 photographs), ten percent for testing (1012 photos), and the remaining ten percent was randomly selected and used as a validation set (1025 photos) to evaluate performance and save the best weight combinations. All photographs were reduced in size during the training process to 224 × 224 × 3 pixel resolution. We tested the proposed system’s TensorFlow Keras implementation on a Linux desktop equipped with a GPU RTX3060 and 8 GB of RAM. Using the Adam optimizer and a method that slows down training when learning has stalled for too long, the proposed framework was first trained on the APTOS dataset (i.e., validation patience). Throughout the entirety of the training process, hyperparameters were input into the Adam optimizer. We used a range of 1 × 103 to 1 × 105 for the learning rate, 2–64 for the batch size (with an increase of 2× the previous value), 50 epochs, 10 for patience, and 0.90 for momentum. Our arsenal of anti-infectious measures was completed by a method known as “batching” for the dissemination of infectious forms. ## 4.2. Evaluative Parameters This section describes the evaluation methods and their results. Classifier accuracy (Acc) is a standard performance measure. It is determined by dividing the number of successfully categorized instances (images) by the total number of examples in the dataset (Equation [1]). Picture categorization systems are often evaluated using precision (Prec) and recall (Re). As demonstrated in Equation [2], precision improves with the number of accurately labeled photos, whereas recall is the ratio of properly categorized images in the dataset to those related numerically [3]. The higher the F1-score, the more reliable the system is at making predictions about the future. The F1-score can be determined using Equation [4], (F1sc). With respect to the study’s last criterion, top N accuracy, it was found that the highest probability answers from model N should coincide with the expected softmax distribution. An accurate classification is made if at least one of N predictions corresponds to the target label. [ 1]Accuracy=Tp+TnTp+Tn+Fp +Fn [2]Precision=TpTp+Fp [3]Recall=TpTp+Fn [4]F1-score=2∗Prec∗RePrec+Re True positives, represented by the symbol (Tp), are successfully anticipated positive cases, and true negatives (Tn) are effectively predicted negative scenarios. False positives (Fp) are falsely predicted positive situations, whereas false negatives (Fn) are falsely projected negative situations. ## 4.3. Performance of Inception-V3 Model Outcomes Considering the APTOS dataset, two distinct cases sets were investigated, in which Inception-V3 was applied to our dataset in two distinct scenarios, the first with enhancement (CLAHE + ESRGAN) and the second without enhancement (CLAHE + ESRGAN), as depicted in Figure 2. We split it up this way to cut down on the total amount of time needed to conduct the project. To train a model, 50 epochs were used, with learning rates ranging from 1 × 103 to 1 × 105, and batch sizes varying from 2 to 64. To achieve the highest possible level of precision, Inception-V3 was further tweaked by freezing between 140 and 160 layers. Several iterations of the same model with the same parameters were used to generate a model ensemble, since random weights were generated for each iteration, the precision fluctuated from iteration to iteration. Mean and standard deviation statistics for this procedure are displayed in Table 3 and Table 4, respectively, for the cases where the first 143 layers were frozen with CLAHE + ESRGAN and the cases where they were not. The top performance from each iteration was saved and is shown in Table 5 and Table 6, for case 1 and case 2, respectively, revealing that the best results produced with and without preprocessing using CLAHE + ESRGAN were $98.7\%$ and $80.87\%$, respectively. Figure 9 depicts the optimal outcome for the two scenarios based on the utilized evaluation metrics case 1 using CLAHE and ESRGAN, and case 2 without using them. Figure 10 and Figure 11 show the confusion matrix with and without using CLAHE + ESRGAN, respectively. Table 7 and Table 8 show the total number of photos utilized for testing in each class for the APTOS dataset. According to the data, it is clear that the No DR class has the most images with 504, and its Prec, Re, and F1sc give the highest values of 99 100 and $100\%$ for case 1, and 97, 97, and $97\%$ for case 2. Using retinal pictures to improve the accuracy with which ophthalmologists identify infections, while reducing their effort, was demonstrated to be practical in real-world scenarios. ## 4.4. Evaluation Considering a Variety of Other Methodologies Effectiveness was compared to that of other methods. According to Table 9, our method exceeds other alternatives in terms of effectiveness and performance. The proposed inception model achieved an overall accuracy rate of $98.7\%$, surpassing the present methods. ## 5. Discussion Based on CLAHE and ESRGAN, a novel DR categorization scheme is presented in this research. The developed model was tested on the DR images founded in the APTOS 2019 dataset. There were two training scenarios: case 1 with CLAHE + ESRGAN applied to the APTOS dataset, and case 2 without CLAHE + ESRGAN. Through 80:20 hold-out validation, the model attained a five-class accuracy rate of $98.7\%$ for case 1 and $80.87\%$ for case 2. The proposed method classified both cases scenarios using the pretrained Inception-V3 infrastructure. Throughout model construction, we evaluated the classification performance of two distinct scenarios and found that enhancement techniques produced the best results (Figure 9). The main contributing element in our methodology was the general resolution enhancement of CLAHE + ESRGAN, which we proved, with evidence, is responsible for the great improvement in the accuracy. ## 6. Conclusions By identifying retinal images displayed in the APTOS dataset, we established a strategy for quickly and accurately diagnosing five distinct forms of cancer. The proposed method employs case 1 with images enhanced with CLAHE and ESRGAN, and case 2 with original images. The case 1 scenario employs four-stage picture enhancement techniques to increase the image’s luminance and eliminate noise. CLAHE and ESRGAN were the two stages with the best impact on accuracy, as demonstrated by experimental results. State-of-the-art techniques in preprocessed medical imagery were employed to train Inception-V3 with augmentation techniques that helped reduce overfitting and raised the entire competencies of the suggested methodology. This solution showed that when using Inception-V3, the conception model achieved a correctness of $98.7\%$ ≈ $99\%$ for the case 1 scenario and $80.87\%$ ≈ $81\%$ for the case 2 scenario, both of which are in line with the accuracy of trained ophthalmologists. The use of CLAHE and ESRGAN in the preprocessing step further contributed to the study’s novelty and significance. 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--- title: 'Prevalence and Associated Factors for Periodontal Disease among Type I and II Diabetes Mellitus Patients: A Cross-Sectional Study' authors: - Manea Musa Alahmari - Hassan M. AlShaiban - Syed E. Mahmood journal: Healthcare year: 2023 pmcid: PMC10048528 doi: 10.3390/healthcare11060796 license: CC BY 4.0 --- # Prevalence and Associated Factors for Periodontal Disease among Type I and II Diabetes Mellitus Patients: A Cross-Sectional Study ## Abstract In Saudi Arabia, the prevalence of diabetes mellitus (DM) is high. DM is a risk factor for periodontal disease. The current study aimed to estimate the prevalence and potential associated factors for periodontitis among type I and II DM patients in Abha, Saudi Arabia. A cross-sectional study was conducted among patients with DM who attended the Periodontal Consultant Center at King Khalid University and Aseer Central Hospital, in Abha city, from January 2020 to January 2022. A questionnaire was used to collect relevant data. Patients were assessed for the severity of periodontitis. A total of 499 DM patients were enrolled in this study. The prevalence of periodontitis was $7.4\%$ among type I DM and $46.4\%$ among type II DM patients. The prevalence of periodontitis was significantly associated with age among type I DM patients (p-value = 0.001) and type II DM patients (p-value = 0.001), respectively, and smoking among type I DM patients (p-value = 0.002) and among type II DM patients (p-value = 0.000), respectively. Age and smoking were the potential associated factors for the prevalence of periodontitis among type I and II DM. The study provides evidence about the prevalence of periodontitis among DM patients, creates awareness regarding the factors that potentially contribute to worsening periodontal tissues, and the importance of early diagnosis and prevention to avoid the irreversible destruction of the periodontal tissues. ## 1. Introduction Diabetes mellitus (DM) is a metabolic disorder affecting biochemical and cellular processes within the body of diabetic patients, and it leads to decreased immunity, exhibiting increased susceptibility to infection [1]. A total of 7 million individuals develop diabetes yearly, and by the year 2030, it is expected that 366 million individuals will have the disease in the world [2]. DM is a risk factor for periodontal disease [3]. Periodontitis is characterized by microbial-associated, host-mediated inflammation that ends in periodontal attachment loss [4]. It deteriorates the blood glucose control. The Gram-negative bacterial infections of periodontitis reduce the insulin-mediated glucose uptake by skeletal muscle and reduces the insulin resistance of the body. However, management of periodontal disease will reduce glycated hemoglobin (HbA1c) and improve the DM [5]. Globally, many studies have estimated the prevalence and risk factors for periodontitis among DM patients [1,2,6]. It was reported that there is a significant association between the prevalence of periodontitis among DM and age [7]. Other studies showed that periodontitis prevalence among DM is associated with sex [1,6,8]. Similarly, other studies revealed a significant association between smoking and the prevalence of periodontitis [8,9,10,11]. Other studies [7,10,12] reported the association between HbA1c level and prevalence of periodontitis. In the Kingdom of Saudi Arabia (KSA), the prevalence of DM among Saudi adults is $23.7\%$ (a total of $26.2\%$ among males and $21.5\%$ among females, respectively) [13]. Data related to periodontal disease among the Saudi population are poor. A study showed that the prevalence of the periodontal disease among the Saudi people in the Najran region is $39\%$ [14]. A prior survey conducted in Abha showed that the prevalence of periodontitis was $36.8\%$, and the prevalence of mild, moderate, and severe periodontitis was $57.4\%$, $36.6\%$, and $4.95\%$, respectively [15]. There is a need for further studies to be conducted in the region to provide evidence about the prevalence of periodontitis among DM patients and the factors that contribute to the worsening periodontal tissues in these patients. Therefore, the study aimed to estimate the prevalence and potential associated factors for periodontitis among type I and II DM patients in Abha. ## 2.1. Study Design and Population A cross-sectional study was conducted among DM patients who attended the Periodontal Consultant Center at King Khalid University and Aseer Central Hospital, in Abha city, from January 2020 to January 2022. The study population consisted of the known type 1 or 2 diabetic patients attending the outpatient departments of the above-mentioned health facilities. ## 2.2. Inclusion and Exclusion Criteria The study enrolled male and female patients who have been diagnosed with type I or II DM at any age. However, the DM patients who had complications, systemic disease other than DM, and underwent periodontal treatment during the past six months were excluded from this study. Lactating or pregnant women were excluded. Those who did not agree to participate in this study were excluded. ## 2.3. Data Collection and Measurements A questionnaire was used to collect data related to the following variables: age, sex, smoking status (smoking and non-smoking), DM type, HbA1c level, periodontal status (presence or absence of periodontal disease), the severity of periodontal disease (periodontitis stages), and periodontitis grade. Based on answers at the time of data collection, a smoker was defined as a person who has been smoking every day or occasionally and a non-smoker was a person who had never smoked. ## 2.4. Sample Size and Sampling Technique Optimal sampling size was calculated on the basis of prior prevalence rate of periodontitis of $32\%$ [16]. The sample size was calculated by the formula 4PQ/L 2, where P is the prevalence, Q is 100-P, and L is the absolute precision, i.e., $5\%$. Approximate minimum sample size came out to be 348. However, 499 individuals were included. Convenient sampling technique was used. ## 2.5. Diabetes Mellitus Diagnosis In addition, blood chemical analysis for HbA1c level was tested for DM patients; patients who used insulin or hypoglycaemic medicines were diagnosed with DM. Based on the blood chemical analysis of HbA1c level diabetes, control of diabetes was classified into good glycemic control at HbA1c ≤ $7\%$, moderately glycemic control at HbA1c > 7.01 and ≤ $8.5\%$, and poorly glycemic control at HbA1c > $8.5\%$ [17]. ## 2.6. Periodontitis Diagnosis The participant was considered to have a healthy periodontal status if the participant had zero clinical attachment loss (CAL) and was considered to have periodontitis if CAL ≥ 1 mm [4]. The severity of periodontitis was classified into four stages based on the degree of CAL. Stage I (early/mild periodontitis) if CAL is between 1.0 –and 2.0 mm. Stage II (moderate periodontitis) if CAL is between 3.0 –and 4.0 mm. Stage III (severe periodontitis) if CAL ≥ 5.0 mm [1, 4]. Moreover, the periodontitis patients were classified into three grades based on the estimated future risk of periodontitis progression and responsiveness to standard therapeutic principles [4]. In the clinical context, a patient is considered a periodontitis case if interproximal CAL is detectable at two or more non-adjacent teeth and when [1] [2] buccal or oral CAL ≥3 mm with pocketing >3 mm is detectable at two or more teeth, but the observed CAL cannot be due to non-periodontitis related causes such as 8 a. gingival recession of traumatic origin, b. dental caries extending in the cervical area of the tooth, c. presence of CAL on the distal aspect of a second molar and associated with malposition or extraction of a third molar, d. endodontic lesion draining through the marginal periodontium, and e. occurrence of a vertical root fracture. Stage one 1–2 mm; Stage two 3–4 mm; Stage three equal or more than 5; Stage four the same with 3 but advanced form. The following grading in periodontitis diagnosis was used [18]: Grade A: slow rate of progression, Grade B: Moderate rate of progression, Grade C: rapid rate of progression. All calibrations were examined by the principal investigator himself. ## 2.7. Data Analysis The data were coded, entered, and analysed by SPSS (Statistical Package for Social Sciences) version 26.0 version (SPSS Inc., Chicago, IL, USA). According to the Kolmogorov–Smirnov test, the age and HbA1c levels were not in a normal distribution (p-value < 0.05). Therefore, the age and HbA1c levels were presented as the median and interquartile range (IQR) at quartile 1 and quartile 3. The age was categorized by the median into ≥37 years and <37 years. The HbA1c levels are categorized by the cut-off point ($7\%$) into good glycemic control (≤$7\%$) and poor glycemic control (>$7\%$). For statistical analysis, a chi-square test was used. The prevalence ratio (PR) with a $95\%$ confidence interval (CI) was calculated. p-value < 0.05 was considered statistically significant. Multivariate analysis (multinomial regression model) was further used. Regression analysis was performed using ‘Periodontal disease’ as the dependent variable and the select variables as independent variables. ## 2.8. Ethics Approval and Consent to Participate Ethical clearance (IRB/KKUCOD/ETH/2021-$\frac{22}{053}$) was obtained from the Research and Ethics Committee of King Khalid University in Abha city. The aim of the study was explained to all participants. It was carried out following the Declaration of Helsinki. Informed consent was taken from each participant. Assent from the juvenile patients was obtained. Confidentiality of data was assured and ensured. ## 3.1. Characteristics of the Study Participants A total of 499 DM patients were included in the present study. Table 1 shows the characteristics of the patients. Out of them, 268 ($53.7\%$) were aged between 21 and 40 years of age, with a median (IQR) age of 37.0 (28.0 and 50.0) years. Nearly 343 ($68.7\%$) DM patients were female, and only 84 ($16.8\%$) were smokers. The majority, 351 ($70.3\%$) DM patients, had type II DM. Nearly 244 ($48.9\%$) of DM patients had good glycemic control, with a median (IQR) HbA1c level of $7.2\%$ ($6.4\%$ and $8.0\%$). Almost 174 DM patients had periodontitis. The percentage of type I and II DM patients between the age of 21 and 40 years were $78.4\%$ and $43.3\%$, respectively, and the median (IQR) age of type I and II DM patients was 25.0 (21.0 and 28.0) years and 44.0 (36.0 and 55.0) years, respectively. The females among type I and II DM patients were $72.2\%$ and $67.2\%$, respectively. The prevalence of smokers was only $6.1\%$ among type I DM and $21.4\%$ among type II DM patients. The percentage of type I and II DM patients who had good, moderate, and poor glycemic controls were ($27.0\%$, $43.9\%$, and $29.1\%$) and ($58.1\%$, $35.6\%$, and $6.3\%$), respectively. ## 3.2. Prevalence of Periodontitis among DM Patients The prevalence of periodontitis was $7.4\%$ among type I DM patients and $46.4\%$ among type II DM patients. The prevalence of mild, moderate, and severe periodontitis among type I and II DM patients were ($36.4\%$, $9.1\%$, and $54.5\%$) and ($9.2\%$, $23.9\%$, and $66.9\%$), respectively (Table 1). Moreover, the overall prevalence of periodontitis among DM patients was $34.9\%$. The overall prevalence of severe and grade A periodontitis was $66.2\%$ and $60.3\%$, respectively. ## 3.3. Potential Associated Factors for Periodontitis among Type I Diabetes Mellitus Patients Table 2 shows the associated factors for periodontitis among type I DM patients. The prevalence of periodontitis among those aged ≥ 37 years was $100.0\%$, while the prevalence of periodontitis among those aged < 37 years was $6.8\%$. However, the prevalence of periodontitis was significantly higher among those aged ≥ 37 years than those aged < 37 years (PR = 14.7, $95\%$ CI:8.1–26.7, and p-value = 0.001). Moreover, the prevalence of periodontitis was significantly higher among smokers than non-smokers ($33.3\%$ versus $5.8\%$, PR = 8.2, $95\%$ CI: 1.7–38.9, and p-value = 0.002). However, the results show that age and smoking were potential factors associated with periodontitis among type I DM. Regarding the severity of periodontitis among type I DM patients, the results show that there is not a significant factor associated with the severity of periodontitis among type I DM (p-value > 0.05) ## 3.4. Potential Associated Factors for Periodontitis among Type II Diabetes Mellitus Patients Table 3 shows the associated factors for periodontitis among type II DM patients. The prevalence of periodontitis among those aged ≥ 37 years was $15.3\%$, while the prevalence of periodontitis among those aged < 37 years was $23.5\%$. However, the prevalence of periodontitis was significantly higher among those aged ≥ 37 years than those aged < 37 years (PR = 4.0, $95\%$ CI:2.4–6.9, and p-value = 0.001). Moreover, the prevalence of periodontitis was significantly higher among smokers than non-smokers ($78.7\%$ versus $37.7\%$, PR = 2.1, $95\%$ CI: 1.7–2.5, and p-value = 0.000). However, the results show that age and smoking were potential factors associated with periodontitis in type II DM. Concerning the severity of periodontitis among type II DM patients, the prevalence of mild, moderate, and severe periodontitis among those aged ≥ 37 compared to those aged < 37 years was ($7.9\%$, $20.0\%$, and $72.1\%$ versus $17.4\%$, $47.8\%$, and $34.8\%$), respectively. Moreover, the severity of periodontitis was significantly higher among ages ≥ 37 years than < 37 years (χ2 = 12.5 and p-value = 0.002). However, the results show that age was a potential factor for periodontitis among type II DM. Although the prevalence of severe periodontitis was higher among males, smokers, and poor glycemic control as compared to females, non-smokers, and proper glycemic management of type II DM ($76.8\%$, $72.9\%$, and 75.0 versus $61.7\%$, $63.5\%$, and 59.8), there is not statistical significance between the severity of periodontitis among type II DM and sex, smoking, and HbA1c level (p-value > 0.05). As shown in Table 4, type I DM and age up to 37 years was found to be a significant predictor for periodontal diseases in the study sample. ## 4. Discussion The study aimed to estimate the prevalence of periodontal diseases and associated factors among type I and II DM patients. It showed that type II DM was more prevalent than type I DM, and periodontitis was more prevalent among type II DM than type I DM patients. Age and smoking are potential associated factors for periodontitis prevalence among type I and II DM patients. Smoking prevalence was $6.1\%$ among type I DM patients and $21.4\%$ among type II DM patients. Regarding HbA1c level, most type I DM patients had moderate glycemic control, while type II DM patients had reasonable glycemic control. ## 4.1. Prevalence of Periodontitis among DM Patients This study indicates that the prevalence of periodontitis was $7.4\%$ among type I DM patients compared to $46.4\%$ among type II DM patients. Moreover, the overall prevalence of periodontitis among type I and II DM patients was $34.9\%$. Most periodontitis patients had severe periodontitis in type I and II DM patients. It may be attributable to the effect of DM in damaging the periodontium [16]. Our result agrees with studies in the Najran province of KSA [14] and Hungary [16]. However, our result disagrees with other studies; the prevalence of periodontitis among DM patients is low in a survey carried out in India ($25.3\%$) [2] and high in a study conducted in Korea ($43.7\%$) [7]. Additionally, other studies reported that the prevalence of periodontitis among type II DM patients was high in India ($95.1\%$, $84.5\%$) [3, 5] and Iraq ($95.9\%$) [6]. The variation might be attributable to the difference in the definition and methods used to diagnose periodontitis, the ages of enrolled participants, and sampling design methods. ## 4.2. Potential Associated Factors for Periodontitis among Type I Diabetes Mellitus Patients Our results indicated that the prevalence of periodontitis among type I DM patients is 12.7 times more likely to occur in the age group ≥ 37 years than in the age group < 37 years. However, there is a significant association between the prevalence of periodontitis among type I DM and age. Our finding is dissimilar to studies that included participants aged ≥ 30 years in India [2] and Korea [7]. Age itself is not a risk factor, but age-related disorders may facilitate the microbial–inflammatory dysregulation [19]. This study found that there is not a significant association between the prevalence of periodontitis among type I DM and sex. Our finding is dissimilar to studies in India [2] and Korea [7]. This discrepancy might be attributable to the characteristics of our study’s participants (all ages are included, and there are more females than males). This study revealed a significant association between the prevalence of periodontitis among type I DM and smoking. The prevalence of periodontitis is 2.7 times more likely to occur among smokers than non-smokers. However, smoking is an associated factor for the prevalence of periodontitis, which may be because of the systemic and local effects of smoking on periodontal tissues. Our result agrees with the results of studies in India [2], Iraq [6], Korea [7], Serbia [9], Iran [11], and Hungary [17]. However, it disagrees with other studies in Lithuania [10]. Our study indicated that there is not a significant association between the prevalence of periodontitis and the HbA1c level of type I DM. This finding agrees with studies in Korea [7] and Lithuania [10]. Regarding the severity of periodontitis among type I DM patients, the results showed that there is not a significant association between the severity of periodontitis among type I DM patients and age, sex, smoking, and HbA1c level. Our result agrees with a study in Hungary [1], which found no significant association between HbA1c level and the severity of periodontitis. ## 4.3. Potential Associated Factors for Periodontitis among Type II Diabetes Mellitus Patients Our results indicated that the prevalence of periodontitis among type II DM patients is 4.0 times more likely to occur in the age group ≥ 37 years than in the age group < 37 years. However, there is a significant association between the prevalence of periodontitis among type II DM and age. Our finding is similar to the result of a study in India [5] and dissimilar to other studies, which included participants aged ≥ 30 years in India [1,2], Iraq [6], and Korea [7]. It might be attributable to the nature of our study, which includes participants of all ages. This study found that there is not a significant association between the prevalence of periodontitis among type II DM and sex. It might be attributable to the fact that there are more females than males. Our finding is similar to the results of studies in India [1], Iraq [6], and Nepal [8] and dissimilar to others in India [2,5] and Korea [7]. This study revealed a significant association between the prevalence of periodontitis among type II DM and smoking. The prevalence of periodontitis is 2.1 times more likely to occur among smokers than non-smokers. However, smoking is an associated factor for the prevalence of periodontitis. It may be explained by the systemic and local effects of smoking on periodontal tissues. Our result agrees with the results of two studies in India [2,9], along with others in Iraq [6], Korea [7], Nepal [8], Lithuania [10], Iran [11], Hungary [17], Serbia [20], and Saudi Arabia [21]. Although the prevalence of periodontitis among type II DM patients is higher among those with poor glycemic control than those with reasonable glycemic control, there is not a significant association between the prevalence of periodontitis and HbA1c level. This finding agrees with studies in Saudi Arabia [20], Italy [21], India [1], and Lithuania [10]. However, it disagrees with the results of two studies in India [3,5]. The variation might be attributable to the difference in the definition and methods used to diagnose periodontitis and HbA1c estimation. Regarding the severity of periodontitis among type II DM patients, our findings indicated a significant association between the severity of periodontitis among type II DM patients and age. Our finding is similar to studies in Lithuania [10] and Bangladesh [12]. Although severe periodontitis is increased among males, smokers, and those with poor glycemic control as compared to females, non-smokers, and those with proper glycemic management of type II DM, there is not a significant association between the severity of periodontitis among type II DM and sex, smoking, and HbA1c level. Our result agrees with a study in Bangladesh [12], which showed that sex and smoking are not associated factors for the severity of periodontitis. However, our result disagrees with two studies in India [13] and Bangladesh [14], which found a significant association between HbA1c level and the severity of periodontitis. The variation might be attributable to the difference in the ages of enrolled participants and sampling methods. Thus, periodontitis is an early sign of diabetes mellitus and may therefore serve as a valuable risk indicator. Both dentists and physicians need to be aware of the strong connection between periodontitis and T2DM. Controlling these two diseases might help prevent each other’s incidence [22]. The mechanisms that link diabetes and periodontitis are not completely understood, but involve aspects of inflammation, immune functioning, neutrophil activity, and cytokine biology [23]. Both type I and type II diabetes are associated with elevated levels of systemic markers of inflammation [24]. Diabetes increases inflammation in periodontal tissues, with higher levels of inflammatory mediators such as interleukin-1β (IL-1β) and tumour necrosis factor-α (TNF-α) [25,26]. Periodontal disease has been associated with higher levels of inflammatory mediators such as TNF-α in people with diabetes [27]. Accumulation of reactive oxygen species, oxidative stress, and interactions between advanced glycation end products (AGEs) in the periodontal tissues and their receptor (RAGE, the receptor for advanced glycation end products) all contribute to increased inflammation in the periodontal tissues in people with diabetes [25]. For patients with both type II diabetes and periodontitis, nonsurgical periodontal treatment and periodontal maintenance may help to control HbA1c levels [28]. A dental office that treats patients with periodontitis is a suitable location for screening for diabetes by a simple finger stick and validated HbA1c dry spot analysis [29]. An emerging role for dental professionals is envisaged, in which diabetes screening tools could be used to identify patients at high risk of diabetes, to enable them to seek further investigation and assessment from medical healthcare providers [30]. The patients with diabetes mellitus should be informed about their higher risk of developing periodontal diseases [31]. Our study had some limitations. Firstly, a convenience sample is possibly the most important limitation of this study, which prevented some of the variables from being significant. Secondly, a non-random selection of the study participants is also a considerable limitation that might affect the representation of the participants. The status of plaque control, genetic predisposition, the duration of disease, and amount of smoking based on pack per day are important factors which are not considered in the survey. We hope in the future to have all the required resources to conduct multicentric/nationwide studies and include all the important factors in the analysis. However, the data which included diabetic males and females of all ages and the extensive analysis are the strengths of our study. ## 5. Conclusions The study concludes that the overall prevalence of periodontitis among DM patients was $34.9\%$, ranging from $7.4\%$ among type I DM patients to $46.4\%$ among type II DM patients. Moreover, age and smoking were the potential factors associated with periodontitis prevalence among type I and II DM. Age was the only possible factor related to periodontitis severity among type II DM patients. Therefore, an analytic study design using a comparison group, e.g., case-control, is recommended to identify the risk factors associated with periodontitis. 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--- title: Taste Responses and Ingestive Behaviors to Ingredients of Fermented Milk in Mice authors: - Yuko Yamase - Hai Huang - Yoshihiro Mitoh - Masahiko Egusa - Takuya Miyawaki - Ryusuke Yoshida journal: Foods year: 2023 pmcid: PMC10048529 doi: 10.3390/foods12061150 license: CC BY 4.0 --- # Taste Responses and Ingestive Behaviors to Ingredients of Fermented Milk in Mice ## Abstract Fermented milk is consumed worldwide because of its nutritious and healthful qualities. Although it is somewhat sour, causing some to dislike it, few studies have examined taste aspects of its ingredients. Wild-type mice and T1R3-GFP-KO mice lacking sweet/umami receptors were tested with various taste components (sucrose, galactose, lactose, galacto-oligosaccharides, fructo-oligosaccharides, l- and d-lactic acid) using 48 h two-bottle tests and short-term lick tests. d-lactic acid levels were measured after the ingestion of d- or; l-lactic acid or water to evaluate d-lactic acidosis. In wild-type mice, for the sweet ingredients the number of licks increased in a concentration-dependent manner, but avoidance was observed at higher concentrations in 48 h two-bottle tests; the sour ingredients d- and l-lactic acid showed concentration-dependent decreases in preference in both short- and long-term tests. In 48 h two-bottle tests comparing d- and l-lactic acid, wild-type but not T1R3-GFP-KO mice showed higher drinking rates for l-lactic acid. d-lactic acidosis did not occur and thus did not contribute to this preference. These results suggest that intake in short-term lick tests varied by preference for each ingredient, whereas intake variation in long-term lick tests reflects postingestive effects. l-lactic acid may have some palatable taste in addition to sour taste. ## 1. Introduction The foods that humans consume are diverse. Among them, fermented milk originated in the Middle East at least as early as 1000 B.C. and has long been consumed by people around the world [1] for both its nutritive and functional qualities, such as good preservability, good taste quality, and good effects on our health. Several studies have associated yogurt consumption with lower anthropometric indicators [2] and reduced risk of type 2 diabetes mellitus [3] and cardiovascular diseases [4]. One of the flavor characteristics of fermented milk is its strong sour taste, thought to be elicited by lactic acid. Of the two enantiomers, d- and l-lactic acid, l-lactic acid is a common compound of human metabolism, whereas d-lactic acid is produced by some strains of microorganisms or by some less relevant metabolic pathways [5]. The amount of total lactic acid in fermented milk products ranges from $0.6\%$ to $1.2\%$, with l-lactic acid the major enantiomer [6]. For example, about $60\%$ of the total lactic acid is the l-form enantiomer in yogurt [6]. Lactic acid is also involved in other fermented foods and drinks such as wine [7] and sake [8] and may contribute greatly to their taste. Sour taste is an innate aversive taste quality, and the strong sourness of fermented milk may discourage its intake. On the other hand, sourness has some beneficial functions for the body, such as increasing salivary secretion [9], and could improve health if used properly. Very few studies have investigated the beneficial effects of sourness. In Drosophila, lactic acid is an appetitive and energetic tastant since it stimulates feeding through the activation of sweet gustatory receptor neurons [10]. Taste substances are usually categorized to a single taste modality sensed by a single receptor or family of receptors. However, taste detection for some chemical species could be complex, with certain molecular properties acting differently on multiple receptors. For example, artificial sweeteners such as saccharin and cyclamate have been reported to activate bitter taste receptors along with sweet taste receptors [11]. It may be possible that in some animals lactic acid is detected by not only the sour taste receptor, otopetrin-1 (OTOP1) [12,13], but also by other receptors such as sweet receptors. Fermented milk also contains sugars such as lactose and galactose and oligosaccharides. Recently, oligosaccharides such as galacto-oligosaccharides (GOSs) and fructo-oligosaccharides (FOSs) have been added to commercially available yogurt as prebiotics. Lactose is the major carbohydrate in breast milk, followed by oligosaccharides and glucose [14]. Lactose, galactose, and oligosaccharides are important nutritional sources during the suckling period in mammals, including humans. Infants fed breast milk or artificial milk containing lactose had higher levels of glucose and essential amino acids (leucine, isoleucine, valine, and proline) in their blood than those fed artificial milk without lactose [15]. However, the downregulation of lactase after infancy has also been found to cause lactose intolerance [16]. Fructo-oligosaccharides and GOSs act as prebiotics when added to artificial milk, bringing the intestinal microflora of artificially fed infants closer to the bifido-dominant microflora of breast-fed infants [17]. Taste receptors are expressed throughout the body; for example, sweet taste receptors in the gastrointestinal tract are involved in the enhancement of the absorption of glucose [18] and bitter taste receptors function in biological defense in the trachea [19]. Fermented milk ingredients may have meaningful functions for the body via taste sensors. If the functionality of these components derived from fermented milk can be elucidated, it will provide new motivation for the consumption of fermented milk, which may increase its consumption and contribute greatly to the maintenance and promotion of people’s health. To elucidate the taste aspects of fermented milk, here we investigated taste responses and ingestive behaviors to the ingredients of fermented milk in mice. As a characteristic point, we used two types of licking tests. One was a short-term (5 s) lick test to purely examine taste responses to each ingredient. The other was a long-term (48 h) two-bottle test, which involved the post ingestive effect of each ingredient in addition to taste response. We chose galactose, lactose, GOSs, and FOSs as sweet ingredients derived from fermented milk, and d- and l-lactic acid as sour ingredients. In addition, preference for the two enantiomers d- and l-lactic acid was compared in wild-type (WT) mice and T1R3-GFP-KO mice lacking the sweet receptor component T1R3 and expressing green fluorescent protein (GFP). In 48 h two-bottle tests, WT mice showed the concentration-dependent increased intake of the sweet ingredients (but avoided higher concentrations), and both WT and T1R3-GFP-KO mice showed concentration-dependent decreases for the sour ingredients d- and l-lactic acid. The WT but not the T1R3-GFP-KO mice had higher intake rates for l- than for d-lactic acid; d-lactic acidosis did not occur and thus cannot explain the preference for l-lactic acid, suggesting l-lactic acid may have some palatable taste in addition to sour taste. ## 2.1. Ethical Approval All animal experiments were performed in accordance with the National Institute of Health Guide for the Care and Use of Laboratory Animals and approved by the Committee for Laboratory Animal Care and Use and the local ethics committee at Okayama University, Japan. ## 2.2. Animals These experiments used male C57BL/6J (WT) mice (purchased from CLEA Japan, Tokyo, Japan) and mice lacking the Tas1r3 gene but expressing green fluorescent protein in cells that usually express T1R3 (T1R3-GFP-KO mice), generated by crossing T1R3-KO mice [20] and T1R3-GFP mice [21] originally generated at Mount Sinai Medical School from the C57BL/6J strain and maintained in this background. We used 54 WT mice and 18 T1R3-GFP-KO mice for 48 h 2-bottle tests, 7 WT mice and 8 T1R3-GFP-KO mice for short-term lick tests, and 9 WT mice for measurement of blood d-lactic acid levels. All mice were maintained in a $\frac{12}{12}$ h light/dark cycle and fed standard rodent chow (MF, Oriental yeast co., Tokyo, Japan). Animals were 8–20 weeks of age, weighing 20–35 g. ## 2.3. Short-Term Lick Test The WT mice ($$n = 7$$) and T1R3-GFP-KO mice ($$n = 8$$), housed in individual cages, were used as experimental subjects. On day 1 of training, each animal was water deprived for 12 h and then placed in the test cage and given free access to deionized water during the 1 h session. Days 2–5 were training sessions: animals were trained to drink deionized water on an interval schedule, consisting of 5 s periods of deionized water presentation alternating with 10 s intertrial intervals. From day 6, the numbers of licks for each taste solution and deionized water were counted during the first 5 s after the animal’s first lick, using a lick meter (Yutaka Electronics Co., Gifu, Japan). The test solutions used were 1–1000 mM sucrose + 1 mM quinine hydrochloride (QHCl), 1–1000 mM galactose + 1 mM QHCl, 1–444 mM lactose + 1 mM QHCl (444 mM is saturated lactose solution at 20 °C), 1–$30\%$ GOSs + 1 mM QHCl, 1–$30\%$ FOSs + 1 mM QHCl, 1–100 mM citric acid, 1–100 mM d-lactic acid, and 1–100 mM l-lactic acid. One tastant, at varying concentrations, was tested on any given test day. To examine lick responses to preferred solutions (sucrose, galactose, lactose, GOSs, and FOSs), mice were deprived of both food and water 12 h before the experiment and 1 mM QHCl was added to test solutions (bitter-sweet mixture) to obtain clear concentration-dependent preference to sweeteners [22]. On each test day, mice were given test solutions with concentrations of descending order (from highest concentration to deionized water) in first trial then randomized order in second and further trials. To examine lick responses to aversive solutions (citric acid and d- and l-lactic acid), mice were deprived of water 12 h before beginning of experiment. On each test day, mice were given test solutions in ascending concentration order (from deionized water to highest concentration) in first trial and then randomized order in second and further trials. The number of lick trials for each solution was at least three, and their values were averaged for data analysis. l- and d-lactic acid were purchased from Musashino Chemical Laboratory (Tokyo, Japan); other ingredients were purchased from Nakarai Tesque (Kyoto, Japan) or FUJIFILM Wako Pure Chemical Corporation (Osaka, Japan). ## 2.4. Long-Term Intake Test: The 48 Hour 2-Bottle Test The WT mice (9 groups, 6 mice each) and T1R3-GFP-KO mice (3 groups, 6 mice each) were used as experimental subjects. Mice were caged individually and given access for 48 h to two bottles, one containing deionized water and the other containing the test solution. The bottles were inserted into the individual’s home cage, and their positions were switched after 24 h to avoid location preference. Total intake of each solution after 48 h was measured, and the preference ratio (PR) was calculated as the amount of ingested test solution divided by the total amount ingested (water + test solution). Preference ratio > 0.5 indicated preference; PR < 0.5 indicated avoidance. For each tastant, presentation was within an ascending concentration series. The test solutions used were 10–1000 mM sucrose, 10–1000 mM galactose, 10–444 mM lactose, 1–$30\%$ GOSs, 1–$30\%$ FOSs, 1–100 mM citric acid, 1–100 mM l-lactic acid, 1–100 mM d-lactic acid. For comparison between d- and l-lactic acid, mice were given access for 48 h to two bottles, one each containing d- or l-lactic acid at the same concentration. Preference ratio for l-lactic acid was calculated as the amount of ingested l-lactic acid divided by the total amount of ingested solution (l- + d-lactic acid). ## 2.5. Measurement of Blood d-Lactic Acid Male WT mice ($$n = 9$$), housed in individual cages, were used as experimental subjects. On test day 1, at around 8 pm mice were weighed and placed in individual cages with food and one bottle containing 30 mM l-lactic acid, 30 mM d-lactic acid, or deionized water. After 12 h of presentation, body weight and intake of food and solution were measured for each mouse and a blood sample was collected from the tail vein. On days 3 and 5, this procedure was repeated with a different solution; on days 2 and 4, mice were maintained in their home cage with food and water. Mice were divided into three groups ($$n = 3$$ each); for each group, solutions were presented with the same order (deionized water, l-lactic acid, or d-lactic acid) but starting with a different solution, to test for order effects. Blood d-lactic acid concentration was measured using ELISA (PicoProveTM d-Lactate Fluorometric Assay Kit, BioVision, Boston, MA, USA) following the manufacturer’s instructions. ## 2.6. Statistical Analysis Prior to testing, the Shapiro–Wilk test was used to check whether each variable followed a normal distribution. For short-term lick tests, and long-term 48 h 2-bottle tests, differences among concentrations of each ingredient were statistically analyzed by one-way ANOVA and post hoc Tukey highest-significant-difference (HSD) test. Differences among taste ingredients (GOSs vs. FOSs, d-lactic acid vs. l-lactic acid (vs. CA)) and concentration were statistically analyzed by two-way ANOVA. Differences among species (WT vs. T1R3-GFP-KO) and concentration were statistically analyzed by two-way ANOVA. Differences in blood d-lactic acid levels, weight changes, and amount of feeding and drinking were statistically analyzed by one-way ANOVA. Statistical analyses were performed using EZR software [23]. A p-value < 0.05 was considered significant. ## 3.1. Taste Response and Ingestive Behaviors to Sugars in WT Mice In the short-term lick test, WT mice showed a concentration-dependent increase in the number of licks of sucrose+quinine ($F = 137.6$, $p \leq 0.001$), lactose+quinine ($F = 30.3$, $p \leq 0.001$), and galactose+quinine solution ($F = 128$, $p \leq 0.001$; one-way ANOVA) (Figure 1A), suggesting strong taste preference for these sugars at high concentrations. In the 48 h two-bottle test, WT mice also showed a concentration-dependent increase in PR for sucrose ($F = 74.7$, $p \leq 0.001$, one-way ANOVA); although PRs for lactose and galactose solutions at ≤300 mM increased in a concentration-dependent manner (lactose: $F = 36.7$, $p \leq 0.001$; galactose: $F = 21.2$, $p \leq 0.001$; one-way ANOVA), mice apparently avoided the highest concentration of lactose (10 mM vs. 444 mM: $p \leq 0.001$, post hoc Tukey HSD test) and showed decreased preference for the highest concentration of galactose (10 mM vs. 1000 mM: $$p \leq 0.419$$, post hoc Tukey HSD test) (Figure 1B). It is worth noting that calorie content and sugar concentration are not matched on galactose versus lactose and sucrose. ## 3.2. Taste Response and Ingestive Behaviors to Oligosaccharides in WT Mice In the short-term lick test, WT mice showed a concentration-dependent increase in the number of licks of solutions of GOSs+quinine ($F = 35.6$, $p \leq 0.001$) and FOSs+quinine ($F = 173.7$, $p \leq 0.001$; one-way ANOVA) (Figure 2A). The WT mice showed a stronger taste preference for FOSs than for GOSs at the same concentration (effect of ingredient, F[1,60] = 49.65, $p \leq 0.001$, two-way ANOVA). In the 48 h two-bottle test, WT mice showed increased preference for GOSs ($F = 24.3$, $p \leq 0.001$) and FOSs ($F = 24.0$, $p \leq 0.001$; one-way ANOVA) at ≤$10\%$ but avoided them at the highest concentration ($30\%$) (GOSs: $1\%$ vs. $30\%$, $p \leq 0.001$; FOSs: $1\%$ vs. $30\%$, $$p \leq 0.006$$; post hoc Tukey HSD test) (Figure 2B). Mice were likely to ingest more FOSs than GOSs at the same concentration (effect of ingredient, F[1,40] = 16.63, $p \leq 0.001$, two-way ANOVA). ## 3.3. Taste Responses and Ingestive Behaviors to Acids in WT Mice Both in the 48 h two-bottle test and in the short-term lick test, WT mice showed a concentration-dependent avoidance to citric acid (short-term lick test: $F = 76.3$, $p \leq 0.001$; two-bottle test: $F = 52.6$, $p \leq 0.001$), d-lactic acid (short-term lick test: $F = 524.2$, $p \leq 0.001$; two-bottle test: $F = 2.3$, $$p \leq 0.086$$), and l-lactic acid solution (short-term lick test: $F = 192.3$, $p \leq 0.001$; two-bottle test: $F = 31.5$, $p \leq 0.001$; one-way ANOVA) (Figure 3A,B). We found no statistically significant differences among these acids in numbers of licks (effect of ingredient, F[2,90] = 0.93, $$p \leq 0.40$$) or PR (effect of ingredient, F[2,75] = 0.41, $$p \leq 0.67$$, two-way ANOVA). We also compared preference for enantiomers of lactic acid (Figure 3C). At the lowest concentration (1 mM), the PR for l-lactic acid was almost chance level ($50\%$). The WT mice showed no preference or avoidance for l- over d-lactic acid at 3–10 mM but a preference for l- over d-lactic acid at 30 and 100 mM ($F = 4.74$, $p \leq 0.01$, one-way ANOVA). Post hoc tests showed a statistically significant difference between 1 and 30 mM ($p \leq 0.05$) and between 1 and 100 mM ($p \leq 0.05$) (Figure 3C). ## 3.4. Preference for l- over d-Lactic Acid in T1R3-GFP-KO Mice Why did mice prefer l-lactic acid over d-lactic acid at the same concentration? We hypothesized that l-lactic acid, unlike d-lactic acid, might include a more “palatable” taste quality, such as sweet or umami, since different tastes for l- and d-amino acid have been reported [24]. To test this, we used mice lacking the sweet and umami receptor component T1R3 (T1R3-GFP-KO mice). In short-term lick tests, T1R3-GFP-KO mice did not show any increase in the number of licks for sugars+quinine (Figure 4A) or oligosaccharides+quinine (Figure 4B) although they licked well water (34.5–36.7 licks/5 s), suggesting that T1R3-GFP-KO mice have no or reduced taste sensitivity to these sweet compounds, due to a lack of the sweet receptor component T1R3. Similar to WT mice, T1R3-GFP-KO mice showed concentration-dependent avoidance to d- and l-lactic acid in the short-term lick test (l-lactic acid: $F = 145$, $p \leq 0.001$; d-lactic acid: $F = 182.7$, $p \leq 0.001$; one-way ANOVA). The same tendency was observed in the 48 h two-bottle test (l-lactic acid: $F = 5.89$, $p \leq 0.01$; d-lactic acid: $F = 17.42$, $p \leq 0.001$; one-way ANOVA) (Figure 4C,D). There was no significant difference between d- and l-lactic acid in the number of licks (effect of ingredient, F[1, 56] = 2.69, $$p \leq 0.11$$) or PR (effect of ingredient, F[1,50] = 3.04, $$p \leq 0.09$$; two-way ANOVA) (Figure 4C,D). Unlike WT mice, T1R3-GFP-KO mice showed no preference for l- over d-lactic acid at any concentration ($F = 0.87$, $$p \leq 0.5$$, one-way ANOVA) (Figure 4E), suggesting that T1R3-GFP-KO mice could not distinguish between l- and d-lactic acid. We also compared the number of licks in the short-term lick test between WT and T1R3-GFP-KO mice (Figure 5). The number of licks to sugars and oligosaccharides were significantly different between WT and T1R3-GFP-KO mice (Figure 5A–E, Table 1). However, the number of licks to l- and d-lactic acid were not significantly different between WT and T1R3-GFP-KO mice (Figure 5F,G, Table 1). Because of the lack of T1R3, sensitivity to sugars and oligosaccharide, which have a strong sweet taste at higher concentration, may be different between WT and T1R3-GFP-KO mice. ## 3.5. Blood d-Lactic Acid Levels after Ingestion of d- and l-Lactic Acid The WT mice preferred l- over d-lactic acid in the 48 h two-bottle test (Figure 3C). This preference may be due to d-lactic acidosis, which might occur by drinking d-lactic acid; mice might thus show aversion to d-lactic acid, leading them to drink more l- than d-lactic acid in the 48 h two-bottle test. To test this possibility, we measured blood d-lactic acid levels after mice drank d- or l-lactic acid or water for 12 h (Figure 6). At 12 h after ingestion, there were no statistically significant differences in weight change (Figure 6A; $$p \leq 0.699$$), total amount of food intake (Figure 6B; $$p \leq 0.159$$), or total amount of drinking solution (Figure 6C; $$p \leq 0.082$$; one-way ANOVA), and blood d-lactic acid levels were almost similar among the three groups (Figure 6D; $$p \leq 0.949$$, one-way ANOVA), indicating that drinking d-lactic acid did not lead to d-lactic acidosis. Taken together, these data suggest that l-lactic acid may have some preferable taste detected by T1R3-dependent sweet/umami receptors in mice. ## 4. Discussion In this study, we examined taste responses to components of fermented milk in mice. We demonstrated that WT mice but not T1R3-GFP-KO mice preferred galactose, lactose, GOSs, and FOSs—all sweet components in fermented milk—in a concentration-dependent manner in the short-term lick test (Figure 1A, Figure 2A, Figure 3A,B) but showed avoidance of these ingredients at higher concentrations in the long-term, 48 h two-bottle preference test (Figure 1B and Figure 2B). The WT mice showed similar avoidance to both l- and d-lactic acid—sour ingredients of fermented milk—in both short-term lick tests and long-term preference tests (Figure 3A,B) but preferred l- over d-lactic acid in the long-term test (Figure 3C). Although T1R3-GFP-KO mice also showed similar avoidance to both l- and d-lactic acid in both short-term lick tests and long-term preference tests (Figure 4C,D), they did not prefer l- over d-lactic acid in the long-term test (Figure 4E). These results suggest that l-lactic acid but not d-lactic acid may be detected by taste receptors containing T1R3, inducing a sweet or umami taste in addition to a sour taste. We also examined the postingestive effect of d-lactic acidosis but found no significant increase in blood d-lactic acid levels after the ingestion of d-lactic acid (Figure 6D), suggesting that d-lactic acidosis may not contribute to preference for l- over d-lactic acid. To the best of our knowledge, this is the first study to comprehensively examine the taste responses to fermented milk components in a behavioral experiment. The WT mice showed avoidance to galactose, lactose, GOSs, and FOSs at high concentrations in the long-term preference test, possibly due to the negative postingestive effect of these sweet ingredients. Galactose, produced by the digestion of lactose and GOSs, has been found to bind to sodium glucose transporter 1 (SGLT1) in the small intestine and cause postprandial effects, and mice showed a concentration-dependent preference for galactose but decreased preference for a $16\%$ galactose solution [25]. In the present study, we used galactose at 1000 mM (~$18\%$ solution) and 300 mM (~$5.4\%$). Thus, our results are consistent with this previous study. Lactose is an important carbohydrate in weaning and is mainly digested by lactase in the small intestine into galactose and glucose before being used by the organism [26]. In an earlier study, weaning rats preferred low-lactose ($12\%$) over high-lactose ($47\%$) liquid diets because of negative postingestive effects [27]. In the present study, we used adult mice (8–20 weeks of age) and used lactose at 444 mM (~$16\%$ solution) and 300 mM (~$10.8\%$). Thus, our results are similar to this previous study. Mice learn to associate a novel taste with poor physical condition and, as a consequence, avoid drinking fluid with this specific taste; this is conditioned taste aversion [28]. Lactose is digested by lactase. Most mammals, including humans, have very high amounts of latent lactase during suckling, but after weaning the amount of lactase decreases [29]. This leads to lactose malabsorption, which may induce gastrointestinal symptoms such as bloating, borborygmi, flatulence, abdominal pain, and diarrhea [30]. Therefore, mice might associate the ingestion of high concentrations of lactose with gastrointestinal symptoms and then avoid drinking lactose solutions. Galacto-oligosaccharides consist of 2–8 monomeric units containing galactose and glucose; FOSs consists of 3–5 monomeric units containing fructose and glucose. Both GOSs and FOSs are not digested by digestive enzymes but are known to be degraded by intestinal bacteria such as Bifidobacterium [31]. Therefore, it is possible that the galactose produced by the breakdown of GOSs by intestinal bacteria might induce a negative postingestive effect, leading to the avoidance of GOSs in long-term tests. However, fructose does not have a negative postingestive effect, and glucose has only a positive postingestive effect [25]. Thus, the breakdown of FOSs by intestinal bacteria may contribute not to avoidance but to preference for FOSs. Therefore, similar to lactose, the maldigestion of GOSs and FOSs may lead to gastrointestinal symptoms [32], and then mice avoid intake of solutions containing GOSs and FOSs at higher concentrations. Why did WT mice prefer l-lactic acid compared to d-lactic acid at high concentrations in long-term preference tests? One possible reason may be the sweet or umami taste of l-lactic acid: T1R3-GFP-KO mice showed no preference for l- over d-lactic acid at high concentrations (Figure 4E). In Drosophila, lactic acid is an appetitive and energetic tastant, and it stimulates feeding through the activation of sweet gustatory receptor neurons [10]. Human studies have shown that there are significant differences in taste among enantiomers of amino acids. For example, l-leucine has a bitter taste, while d-leucine has a sweet taste [24]. The enantiomers of lactic acid may have a similar effect on taste receptors. We hypothesize that T1R3, a component of “highly palatable” sweet and umami taste receptors, could detect l-lactic acid but not d-lactic acid, leading to a preferable taste of l- over d-lactic acid. This possibility should be investigated in future studies. The second possibility is that drinking d-lactic acid may lead to d-lactic acidosis in mice. The standard metabolism of l-lactic acid in most organisms is mediated by l-lactate dehydrogenase, which is involved in a basic metabolism tightly linked to glycolysis and gluconeogenesis, and it is a crucial part of the Cori cycle in humans and other higher mammals [5]. In contrast, d-lactic acid is known as a harmful enantiomer and can cause d-lactic acidosis if excess d-lactate is not fully metabolized by d-lactate dehydrogenase [5]. However, we examined d-lactic acid levels in blood after mice ingested d- or l-lactic acid or water and found almost similar levels (Figure 6D). Thus, d-lactic acidosis may not contribute to the preference for l- over d-lactic acid. The third possibility is that wild-type mice may be better able to metabolize l-lactic acid than T1R3-GFP-KO mice. Sweet taste receptors are known to be involved in digestion and absorption in the intestine [18]. Such sweet receptors in the gastrointestinal tract may be involved in the metabolism of l-lactic acid, inducing some positive postingestive effect in WT mice but not in T1R3-GFP-KO mice. Such a possibility should also be investigated in future studies. In summary, we demonstrated that mice showed avoidance to sweet ingredients in fermented milk at high concentrations in 48 h two-bottle tests even though they prefer these ingredients in short-term lick tests. We also found that mice preferred l-lactic acid over d-lactic acid, both of which are sour ingredients in fermented milk. The avoidance of sugars and oligosaccharides at high concentrations may be due to the postingestive effects of the sweet ingredients. l-lactic acid may have some sweet or umami taste in addition to sour taste, leading to a preference for l-lactic acid over d-lactic acid. ## References 1. Gasbarrini G., Bonvicini F., Gramenzi A.. Probiotics History. J. Clin. Gastroenterol. (2016) 50 S116-S119. 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--- title: 'Phospholipid-Based Topical Nano-Hydrogel of Mangiferin: Enhanced Topical Delivery and Improved Dermatokinetics' authors: - Faisal K. Alkholifi - Aftab Alam - Ahmed I. Foudah - Hasan S. Yusufoglu journal: Gels year: 2023 pmcid: PMC10048531 doi: 10.3390/gels9030178 license: CC BY 4.0 --- # Phospholipid-Based Topical Nano-Hydrogel of Mangiferin: Enhanced Topical Delivery and Improved Dermatokinetics ## Abstract Mangiferin is a herbal drug that has proven anticancer potential. Owing to its lower aqueous solubility and poor oral bioavailability, the full pharmacological potential of this bioactive drug has not fully been explored. In the present study, phospholipid-based microemulsion systems were developed to bypass oral delivery. The globule size of the developed nanocarriers was less than 150 nm and the drug entrapment was >$75\%$ with a drug loading ~$25\%$. The developed system offered a controlled release pattern following the Fickian drug release. This enhanced mangiferin’s in vitro anticancer activity by four-fold, the cellular uptake was observed to be improved by three-fold on the MCF-7 cells. Ex vivo dermatokinetic studies showed substantial topical bioavailability with a prolonged residence time. The findings provide a simple technique to administer mangiferin via a topical route promising a safer, topically bioavailable and effective treatment option for breast cancer. Such scalable carriers with immense topical delivery potential may provide a better option for present-day topical products of a conventional nature. ## 1. Introduction Mangiferin, chemically known as 2-β-D-glucopyranosyl-1,3,6,7-tetrahydroxy-9H-xanthen-9-one, is extracted from the seed, peel and kernel of Mangifera indica and other plants of higher order. This plant possesses immense potential for managing breast cancer, as reported in the literature. Recent scientific studies have established the beneficial effects of mangiferin, not only limited to immunomodulation, lipid-lowering, anticancer, antioxidant, antidiabetic, antiallergic and antimicrobial effects, but have been further reported for many lifestyle-related disorders [1,2,3]. The anticancer potential of this bioactive molecule has been explored by researchers all over the globe for benefits in breast, colon, lung and neuronal cancers. This phytoconstituent acts by various mechanisms ranging from free radical scavenging to the induction of apoptosis [4]. It is an acceptable trend that phytochemicals are explored for multiple therapeutic benefits [5,6], but numerous challenges are associated with the delivery of phytochemicals [7,8]. Like most phytochemicals, mangiferin is associated with problems such as higher lipophilicity, lesser solubility in biological fluids and poor bioavailability [9]. The oral bioavailability of mangiferin is reported to be lower than $2\%$ [10]. Numerous reports are available where emulsifying systems have enhanced the bioavailability of poorly bioavailable drugs [11]. Drug delivery carriers such as adhesive nanocarriers [12], PEGylated carbon nanotubes [10], injectable hydrogels [13], nano-mixed micelles [14] and nanostructured lipid carriers [15] have been developed by researchers to deliver this promising drug by one route or another. Since oral delivery represents a challenge, it was envisioned that topical formulations were developed employing the principles of drug delivery and biocompatible components such as phospholipids [16]. Phospholipids, made of biocompatible material and an integral part of biological membranes, were selected as an important component for developing microemulsion-based gel [17]. Microemulsion systems are known to properly dissolve and deliver the drug across the skin barriers [18]. Therefore, it was envisioned to explore the effect of phospholipid-incorporated microemulsion-based topical gel on the topical bioavailability of this promising phytochemical and provide a proof-of-concept to the scientific fraternity along with the possible biological evaluations. However, researchers have utilised microemulsions for topical delivery [18,19], but for mangiferin, no such topical product has been explored for the anticancer potential. A few attempts have been made to develop the nanoemulsions of this bioactive and explore its potential in inflammation and skin regeneration. The materials, process and approach of the previously published literature are different from the present research, vouching for the novelty of the current work [20]. ## 2.1. Construction of Pseudo-Ternary Phase Diagrams A total of three pseudo-ternary phase diagrams were constructed. Isopropyl palmitate (IPP) was used as the oil and Gelucire $\frac{44}{14}$ and Labrasol were used as the surfactant and cosurfactants in Smix ratios of 1:1, 2:1 and 3:1, respectively. Figure 1, Figure 2 and Figure 3 show the pseudoternary phase diagrams prepared with IPP, water and Smix ratios of 1:1, 2:1 and 3:1, respectively. The monophasic area obtained in all three ternary phase diagrams was relatively wider in range. It invariably increased with the increase in the surfactant-to-cosurfactant ratio. Labrasol, a polyethylene glycol derivative of capric acid and caprylic acid triglycerides, offers excellent emulsification properties for various oils. Gelucire $\frac{44}{14}$ is composed of polyoxylglycerides with well-established emulsification attributes [21,22]. From these pseudoternary phase diagrams, a total of 9 formulations were selected and preceded further. ## 2.2. Optimisation of the Microemulsion Composition The nine selected formulations were characterised for particle size, drug entrapment (% EE) and drug loading (% DL). The details of the obtained results are presented in Table 1. The globule size of the selected formulations was below 300 nm and invariably for every oil composition; it was least for the 2:1 surfactant-to-cosurfactant ratio with the least value for the formulation F8. The 2:1 Smix ratio offered better emulsification to all the oil compositions resulting in a smaller globule size. The total formulation-to-drug ratio was of approximately 6.7. Therefore, the entrapment efficiency for each formulation was >$75\%$ with the maximum drug entrapment offered by formulation F3. A pattern analogous to the globule size was also observed in drug entrapment. For each oil composition, the drug entrapment was lower at both the 1:1 and 3:1 Smix ratios and the highest at the intermediate ratio of 2:1. For drug loading, a similar pattern was also observed, which was the best for F8 followed by F2. Based on the least globule size and highest entrapment efficiency/drug loading, formulation F8 was selected as the optimised formulation. ## 2.3. Polydispersity, pH, Zeta-Potential and Morphology The globule size of the optimised microemulsion formulation was 100.7 ± 18.7 nm with a PDI value of 0.239. The lower size range of globules assured a nanoemulsion formulation with a PDI confirming the reliability of the micromeritic data of the dispersed phase. The zeta potential of the developed nanodispersion derived from the F8 formulation was −38.2 ± 7.46 mV, assuring substantial dispersion stability [23,24]. The particle size and zeta potential results are shown in Figure 4A,B, respectively. The transmission electron microscopy confirmed the formation of spherical globules devoid of any aggregation, as shown in Figure 5. The pH of the selected formulation was 6.92 ± 0.21, which is within the range for the topical products that are well tolerated on the skin [25]. ## 2.4. Fourier Transform Infrared Spectroscopy The results of the FT-IR are shown in Figure 6. Figure 6A represents the FT-IR spectrum of mangiferin with secondary hydroxyl peak at 3374 cm−1; anti-symmetric C–H stretching at 2941; symmetric C–H stretching at 2890 cm−1; C=O stretching at 1652 cm−1; CH–CH bending at 1502 cm−1; and C–C stretching at 1103 cm−1. The microemulsion (Figure 6B) peaks appeared for the OH- bond at 3429 cm−1, C–H stretch at 2928 cm−1; C=O stretch at 1739 cm−1; C=O bending at 1648 cm−1; C–H bending at 1467 cm−1; and C-O stretching at 1111 cm−1, indicating no interactions between the drug and the excipients, or even just among the excipients. Figure 6C represents the FT-IR spectra of Labrasol with the characteristic C–H stretch 2904 cm−1; C=O stretch 1739 cm−1; C–H bending at 1473 cm−1; and C–O stretch 1103 cm−1. Figure 6D shows the FT-IR spectrum of the oil with the O–H stretch at 3447 cm−1; C–H stretching asymmetric 2928 cm−1; and C–H symmetric 2862 cm−1; C=O stretch at 1739 cm−1; and C–O stretching at 1117 cm−1. Figure 6E represents the FT-IR spectra of the Gelucire showing characteristic C–H stretch 2932 cm−1, C=O stretch 1745 cm−1 and C–O stretch 1113 cm−1. ## 2.5. Incorporation in the Hydrogel The final composition of the nano-hydrogel was 0.02 g of mangiferin/g of the gel, 0.1578 g of oil/g of gel, 0.6316 g of Smix, 0.0125 g of Carbopol, 934/g of gel, 0.0125 g of triethanolamine/g of gel, 0.01 g of phospholipid/g of gel and 0.1556 g of water/g of gel. The conventional hydrogel contained 0.02 g of mangiferin/g of gel, 0.0125 g of Carbopol 934/g of gel, 0.0125 g of triethanolamine/g of gel, 0.0125 g ethanol/g of gel and 0.9425 g of water/g of gel. ## 2.6. Rheological Studies of the Nano-Hydrogel The graph between the shear stress and shear rate is shown in Figure 7A. The graph shows the variation in the shear stress as a function of shear rate. It is clear that, with an increase in the shear rate, the shear stress of the system increased. The system offered a non-Newtonian behaviour with a plastic flow and offered a yield value of 50 Pa. The average viscosity of the developed system was 8.7 Pa·s. The studies clearly vouch that the developed system behaved as a Newtonian after the yield value. Such behaviour is desired for the gelled system and is suitable for storage in tubes [26]. The rheological profile of the conventional hydrogel is provided in Figure 7B. The viscosity (8.97 Pa·s) and the yield values (61 Pa) of the conventional gel are higher than that of the developed nano-hydrogel owing to the presence of oil and surfactants in the latter. ## 2.7. Drug Permeation and Drug Retention Studies The drug permeation profile of mangiferin from the optimised microemulsion, nano-hydrogel formulation and the conventional gel for 8 h is shown in Figure 8. From the obtained data, it is clear that the drug permeation from the conventional gel was without any resistance across the skin. In contrast, the drug release was sustained for the nano-hydrogel formulation. However, the release from the globules of the microemulsion was intermediate. At every time point, there was a significant difference in the release profile from all the systems ($p \leq 0.05$). The plain drug from the conventional gel was almost completely permeated in 3 h of the study, whereas the nano-hydrogel maintained the drug release for 8 h, advocating a sustained release pattern. On the other hand, the microemulsion could also appreciably control the drug release vis-à-vis the conventional gel. However, the microemulsion’s permeation rate was higher than the nano-hydrogel. The average drug permeation rate for the conventional hydrogel was 0.323 mg/h, which was significantly higher than the drug encapsulated in the nano-hydrogel with a permeation rate of 0.116 mg/h ($p \leq 0.05$). The drug permeation rate for the microemulsion was 0.139 mg/h, which was significantly higher than the nano-hydrogel and lower than the conventional formulation ($p \leq 0.05$). The gelling of the nanocarrier resulted in a more controlled release pattern from the microemulsion. The average permeation rate confirmed the controlled release pattern from the nano-hydrogel and drug-loaded microemulsion, whereas the drug permeation mechanism from the plain drug gel was of first order. The drug retention in the skin is shown in Figure 9. The drug retained in the skin for the developed nano-hydrogel was 4.98 ± $0.02\%$, whereas for the conventional gel, the drug retained in the skin was 0.92 ± $0.01\%$, whereas the drug retention in the skin from the ungelled microemulsion was 3.07 ± $0.02\%$. The drug retention in skin layers by the nano-hydrogel was substantially higher than the drug retention by plain gel as well as the ungelled microemulsion, although the values for the microemulsion were significantly higher than the conventional gel ($p \leq 0.05$). The plausible reasons are the composition and nano-architecture of the microemulsion which resulted in the better adhesion, fusion and depot formation in the skin, which was further facilitated by gelling. Such deposition is desired in topical delivery as the drug will be released from the depot and assured drug concentrations for longer duration [18,27]. ## 2.8. Cancer Cell Viability MTT-based cell cytotoxicity assay was performed on MCF-7 cells. The IC50 values for the plain mangiferin gel (conventional gel) and the nano-hydrogel-incorporated mangiferin were obtained to be 12.5 μg/mL and 6.25 μg/mL, respectively. On the other hand, the plain gel formulation was found to exhibit no significant toxicity, even at the concentrations above 200 μg/mL. Interestingly, the IC50 value of the ungelled microemulsion was found to be of the lowest magnitude, i.e., 5.98 μg/mL. The results are shown in Figure 10. The IC50 values obtained for the drug were in consonance with the previously published results [28]. The substantial decrease in the IC50 value of mangiferin after the encapsulation in microemulsion gel exhibited $100\%$ enhancement in the anticancer activity due to the easy penetration and better availability of drugs as a result of components such as IPP, Gelucire and Labrasol [19,22]. However, the ungelled microemulsion had better cytotoxicity than the nano-hydrogel in the in vitro cytotoxicity assays. This was because the ungelled microemulsion was a low-viscosity liquid that could easily get inside the cancer cells; consequently, the cells were more exposed to the microemulsion. ## 2.9. Dermatokinetic Studies The results obtained from the dermal pharmacokinetic studies are shown in Figure 11 and Table 2. As shown in the figure, the drug concentration offered by the nano-hydrogel formulation in skin was substantially higher than the drug concentration from the plain drug gel at every time point ($p \leq 0.05$). As a poorly absorbed drug, the substantially higher drug concentrations in rodents skin from the nano-hydrogel is a significant achievement [29]. The drug-loaded microemulsion was not the final product for topical application, and the ungelled microemulsion was also studied for comparison. The performance of the microemulsion was slightly better than its gelled version, owing to the apparent reason for diffusion limitation in the gelled system. However, for better retention, the in vivo gels are better. There was 2.5-fold enhancement in the Cmax and an approximately eight-fold improvement in the area under the curve (AUC). The studies clearly demonstrated the enhanced dermal bioavailability potential of a promising drug using biocompatible components such as IPP, Gelucire and Labrasol. The microemulsion-based nano-hydrogel not only improved the topical bioavailability of the drug, but also improved the half-life and bioresidence of the drug molecule in the dermal/epidermal compartments. ## 3. Conclusions The developed microemulsion-based nano-hydrogel formulation with sub-micron size and acceptable zeta-potential not only improved the anticancer activity of mangiferin by two-fold, but also improved the topical bioavailability of this promising bioactive many times. Though the drug was not yet explored in the developed system for topical delivery, the present study provides an option to deliver the drug by topical route with inherent promises such as enhanced permeation, retention, conducive dermal pharmacokinetic profile and improved anticancer potential. Such scalable products using simple preparatory techniques with substantial beneficial outcomes for safety, efficacy and dermal pharmacokinetics provide a ray of hope for the further exploration of such products for better outcomes. A significant understanding of the underlying principles of dermatokinetics, as in this case, can result in the better location of the drug. It can assist in achieving the targets of targeted delivery. ## 4.1. Materials Mangiferin and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) were purchased from Sigma-Aldrich, St. Louis, MO, USA. The hydrochloric acid, chloroform, isopropyl palmitate (IPP) and methanol was obtained from SDFCL Chem. Limited, India, whereas the buffer reagents, i.e., disodium hydrogen phosphate, sodium chloride and potassium dihydrogen phosphate were supplied by CDH Pvt. Ltd., New Delhi, India. Gelucire $\frac{44}{14}$, Labrasol ALF and Labrafil M 1944 CS were procured from Gattefossè, Lyon, France. The acetonitrile was obtained from Spectrochem, Mumbai, India. Phospholipid (Phospholipon 90 G) was procured from Lipoid, Ludwigshafen, Germany. ## 4.2.1. Construction of Pseudo-Ternary Phase Diagrams Series of pseudo-ternary phase diagrams were obtained by the titration method, in which a mixture of oil (isopropyl palmitate) and Smix (surfactant and cosurfactant, i.e., ratios of Gellucire $\frac{44}{14}$ and Labrasol) were titrated with water and vice versa. For Smix, three mass ratios of Gellucire $\frac{44}{14}$ and Labrasol were prepared in 1:1, 2:1 and 3:1. Various mixtures of water and Smix in the ratios of 1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2 and 9:1 were prepared and titrated with oil. For instance, water and Smix were mixed in the weight ratios ranging from 9:1 to 1:9, and every ratio was titrated with oil. During titration, a small volume of oil was added to the mixture and vortexed. Titration was continued until the visual observation of haziness. The volume was noted and all the volumes were converted into mass percentage. Every titration point was plotted on the ternary phase diagram depicting the boundary between the homogeneous and heterogeneous phases. Analogously, various ratios of oil and Smix were prepared and titrated with water until it appeared hazy. The phase diagrams was prepared with the obtained mass percentage data [18,30]. ## 4.2.2. Optimisation of the Microemulsion Composition From the three developed pseudo-ternary phase diagrams, a total of 9 formulations were prepared, based on the pseudo-ternary phase diagrams. These formulations were coded from F1 to F11. From all the ternary phase diagrams, three oil concentrations were selected, i.e., $12.5\%$, $15.78\%$ and $22.22\%$. The composition of the systems is shown in Table 3. The amount of mangiferin was kept constant at the concentration of $2\%$ w/w in consonance with the previously published reports [31]. These 9 formulations were characterised for various attributes that have been discussed in the subsequent sections. Based on the globule size, various microemulsion parameters and the maximum entrapment efficiency, one formulation was selected for further studies. ## 4.2.3. Determination of Drug Entrapment Efficiency and Drug Loading To determine the entrapment efficiency of the developed microemulsion, the dialysis method was employed. In brief, each formulation equivalent to 1 mg of drug was packed in a dialysis bag and sealed. The dialysis bag was suspended in 30.0 mL of methanol and kept for stirring at 50 rpm for 2 h. The dialysis fluid was analysed for the cumulative unentrapped drug diffused from the developed system at the end of the study. The drug entrapment efficiency was reported as the amount of entrapped drug per hundred parts of the theoretical drug. On the other hand, the drug loading was reported as the amount of drug encapsulated per hundred parts of the drug carrier [10,24]. The formulae for the drug entrapment and drug loading were as follows:Drug entrapment efficiency=(Total drug−Diffused Drug)Total Drug ×100 Percent drug loading=Entrapped drugTotal carrier to entrap the drug×100 ## 4.2.4. Micromeritics, pH, Zeta-Potential and Morphology The developed formulations were subjected to particle size, particle size distribution and zeta-potential determination using Zetasizer (Nano ZS 90, Malvern, UK). The measurements were recorded in triplicate and the average value was reported as the result. For morphology, transmission electron microscopy was employed using H7000 model (Hitachi Tokyo, Japan). The samples were stained with $1\%$ phosphotungstic acid and placed over carbon-coated copper grid. Systronics pH meter was employed to determine the pH of the undiluted formulations. The pH meter was calibrated with standard buffered solutions over the pH range of 4.0–7.0 and the recordings were made in triplicate, without any dilution. ## 4.2.5. Fourier Transform Infrared Spectroscopy The Fourier transform infrared spectroscopy (FT-IR) of various samples were performed on Spectrum 3 FT-IR Spectrometer (PerkinElmer, Waltham, MA, USA). In brief, the samples were punched in potassium bromide pellets and scanned over the wavelength range of 200 cm−1 to 4000 cm−1. Liquid samples were adsorbed on the potassium bromide tablets. Interpretations were made using standard reference materials and published reports [32]. ## 4.2.6. Incorporation of the Nanosystem in Hydrogel Phospholipid (equivalent to $1\%$ w/w of the final formulation) was dispersed in water using a magnetic stirrer at 100 rpm to obtain a milky dispersion. A stock of $10\%$ w/w Carbopol 934 was prepared in water-dispersed phospholipid and stored overnight. An equal amount of triethanolamine was added drop-wise and the gel was neutralised. The requisite amount of hydrated gel was added in the selected microemulsion, and properly mixed to obtain the desired gel with a Carbopol concentration of $1.25\%$ w/w [26]. For the preparation of the conventional gel, mangiferin ($2\%$ w/w) was dispersed in ethanol ($1.25\%$ w/w) and Carbopol gel was incorporated by mixing. The swollen gel was neutralised with triethanolamine ($1.25\%$ w/w) and the final Carbopol content was 1.25 % w/w. ## 4.2.7. Rheology of the Nano-Hydrogel The developed nano-hydrogel was characterised for the rheological attributes using a Paar Physica cub and bob rheometer at 37 °C. On average, 5 g of the developed gel was placed in the cup of the rheometer and allowed to equilibrate. The shear stress studied range was 0.1–100 per second and was automatically increased by the software of the equipment, after dipping the bob into the cup. The recordings of the respective shear stress at a particular shear rate were employed for the construction and interpretation of a rheogram. From the rheogram, parameters such as the average viscosity and yield value were determined [33]. ## 4.2.8. Ex Vivo Drug Permeation and Drug Retention Studies For the ex vivo skin permeation studies and drug retention studies, the excised skins of healthy Laca mice were employed. The methodology and execution of the skin permeation studies and dermatokinetics on rodent skin were duly approved by the Standing Committee of Bioethics Research, Prince Sattam bin Abdulaziz University Al-Kharj, Saudi Arabia, (SCBR-024-2022). To excise the skin, the rodents were sacrificed by cervical dislocation and the skin was harvested. After the removal of the hair using depilatory cream, the skin was washed thrice with normal saline. The hairless skin was mounted over the donor compartment of the Franz diffusion cell and placed in such a way that its inner side touched the diffusion medium of the receptor compartment. The diffusion medium employed was 30 mL of phosphate-buffered saline of pH 6.8 containing $1\%$ of Tween 80. Plain mangiferin in Carbopol gel (conventional gel), microemulsion and the developed nano-hydrogel were applied on the upper side of the mouse skin in triplicate and the samples from the donor compartment were withdrawn at predetermined time-points. To maintain the sink volume, an equal volume of diffusion medium was replenished after each sample. After filtration, the samples were analysed by HPLC as reported by Allaw et al. [ 34]. The drug permeation was determined by dividing the amount of drug permeated by the total amount and multiplying it by 100. After the completion of the skin permeation studies, the skin from the donor compartments was removed and washed thrice with water to remove any traces of adhered formulation. The skin was excised into small pieces and placed in measured amount of ethanol overnight for the complete extraction of the drug. After filtration, the amount of drug retained in the skin was determined using HPLC [34,35]. The developed system offered a controlled release pattern following the Fickian drug release and governed by the following equation:j=D(Cd0−Ca)X In the above equation, j is the flux of the drug, Cd0 is the freely dissolved unentrapped drug concentration outside the developed system in the donor compartment, Ca is the acceptor drug concentration, X is the thickness constant and D is the diffusion coefficient. ## 4.2.9. Cancer Cell Viability Assay and Normal Cell Safety The in vitro anticancer activity of the developed system and the plain drug was evaluated on MCF-7 breast cancer cell lines. The cells were cultured in 96-welled plates for 48 h with a supply of $5\%$ carbon dioxide. The cells were treated with various concentrations of the formulations and the conventional gel and incubated for 24 h. To each well, 20 µL of MTT (5 mg/mL) was instilled and incubated for 4 h. In each well, 200 μL of dimethyl sulfoxide was added to dissolve the formazan crystals. The absorbance values were recorded at 560 nm [10]. ## 4.2.10. Dermal Pharmacokinetic Studies The dermatokinetic studies were performed analogous to the skin permeation studies, except that, for each time point, one Franz cell was employed. The method reported by Raza et al. was slightly modified [36,37]. The dermis and epidermis were not separated in the present study, but the whole skin was used. 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--- title: 'Tisochrysis lutea as a Substrate for Lactic Acid Fermentation: Biochemical Composition, Digestibility, and Functional Properties' authors: - Caterina Pagnini - Giacomo Sampietro - Gaia Santini - Natascia Biondi - Liliana Rodolfi journal: Foods year: 2023 pmcid: PMC10048537 doi: 10.3390/foods12061128 license: CC BY 4.0 --- # Tisochrysis lutea as a Substrate for Lactic Acid Fermentation: Biochemical Composition, Digestibility, and Functional Properties ## Abstract Microalgae, because of their high nutritional value and bioactive molecule content, are interesting candidates for functional foods, including fermented foods, in which the beneficial effects of probiotic bacteria combine with those of biomolecules lying in microalgal biomass. The aim of this work was to evaluate the potential of Tisochrysis lutea F&M-M36 as a substrate for Lactiplantibacillus plantarum ATCC 8014 and to verify fermentation effects on functionality. Bacterium selection among three lactobacilli was based on growth and resistance to in vitro digestion. Microalgal raw biomass and its digested residue were fermented in two matrixes, water and diluted organic medium, and analysed for biochemical composition and antioxidant activity along with their unfermented counterparts. Bacterial survivability to digestion and raw biomass digestibility after fermentation were also evaluated. Fucoxanthin was strongly reduced (>$90\%$) in post-digestion residue, suggesting high bioavailability. Raw biomass in diluted organic medium gave the highest bacterial growth (8.5 logCFU mL−1) and organic acid production (5 mg L−1), while bacterial survivability to digestion (<$3\%$) did not improve. After fermentation, the antioxidant activity of lipophilic extracts increased (>$90\%$). Fermentation appears an interesting process to obtain T. lutea-based functional foods, although further investigations are needed to optimize bacterial growth and fully evaluate its effects on functionality and organoleptic features. ## 1. Introduction Microalgae (including cyanobacteria) are a highly diverse collection of microorganisms [1] that consist of approximately 50,000 species distributed in all environments [2]. Their potential as food is due to their balanced biochemical composition and high nutritional value [3,4]. In fact, microalgae are rich (about 40–$50\%$ and, in some species, up to $70\%$ of biomass dry weight) in high-quality proteins, polyunsaturated fatty acids, and bioactive molecules with health-promoting properties [5], which make them strong candidates for the production of nutraceuticals and functional foods [1]. In addition, microalgae are potentially sustainable resources, as their production does not require fertile land or pesticides; they are efficient in the use of nutrients, thus reducing the risk of water body pollution with unused fertilizers; and they can be grown in non-potable water as well as in brackish and seawater [6,7,8]. Nowadays, the global market for dietary supplements is dominated by Arthrospira and Chlorella (about 15,000 and 5000 t of dry biomass annually, respectively [9]), which is also due to their long history of human consumption, which allows them to be considered safe by novel food regulations around the world [10,11]. In the food industry, the current trend is to incorporate microalgal biomass or microalgae-derived compounds (e.g., pigments) as ingredients in food formulations [12]. The use of microalgae in the food industry includes the development of functional fermented foods. Fermentation allows the addition of the beneficial effects of probiotic bacteria to the useful biomolecules present in the microalgal biomass and may improve organoleptic features [13]. Fermentation has been investigated on several microalgae, primarily Arthrospira [14], with variable results. Arthrospira platensis (A. platensis) F&M-C256 was shown to be a suitable substrate for Lactiplantibacillus plantarum (L. plantarum) ATCC 8014 growth, while the fermentation process improved antioxidant capacity and phenolic content [15,16]. Martelli et al. [ 17] observed an increase in the concentration of lactic acid bacteria grown on a commercial A. platensis biomass depending on the microalgal biomass initial concentration, the composition of the bacterial mixed population, and the substrate used. A commercial A. platensis biomass proved to be a suitable substrate for solid-state fermentation with Lacticaseibacillus casei (L. casei) 2240 and Lacticaseibacillus rhamnosus (L. rhamnosus) GG, which also led to an improvement in organoleptic characteristics [18]. Kaga et al. [ 2] obtained low fermentation performances by L. plantarum Urama-SU4 and Lactobacillus lactis Urama-SU1 with several microalgal biomasses (including A. platensis) suspended in water, while better results were achieved with biomasses from a wild-collected Nostoc commune (N. commune) and from Euglena sp. grown on sake lees. The addition of a commercial Euglena gracilis biomass improved the growth of Faecalibacterium prausnitzii JCM31915 [19], while the addition of Klamath Aphanizomenon flos-aquae (A. flos-aquae) biomass at $6\%$ increased the growth of Lactobacillus acidophilus (L. acidophilus) DDS [20]. The marine haptophyte Tisochrysis lutea (T. lutea) is a unicellular biflagellate species covered by several layers of scales [21]. T. lutea is widely used in aquaculture [22,23] since it is a valid source of docosahexaenoic acid (DHA) [24], an omega-3 long-chain polyunsaturated fatty acid constituting an important component of cell membranes, which is considered to play a role in the prevention of cardiovascular diseases and probably of neurological disorders [24]. Besides DHA, it also contains other bioactive compounds, such as fucoxanthin [25,26] and phenols [27,28], together with a high amount of proteins and fibres [26]. The beta-glucan chrysolaminarin is the storage product [29]. Despite its valuable biochemical profile, T. lutea is not much investigated in the functional food industry, and it is not currently approved for use in food; nevertheless, several studies show its safety as a dietary product. Nuno et al. [ 30] observed no acute toxicity in rats fed with T. lutea at a dosage of 50 mg day−1; in addition, after 8 weeks, T. lutea supplementation promoted body weight loss in healthy rats and maintenance in rats with diabetes. Niccolai et al. [ 10] found a rather high (6 g of extracted dry biomass L−1) IC50 in human fibroblasts and in an Artemia salina assay for T. lutea F&M-M36 methanolic extracts. Bigagli et al. [ 31], in an in vivo study with rats fed a diet enriched with T. lutea (equivalent to 159 g of biomass day−1 in a 70 kg man), observed no changes in growth or behaviour; in addition, T. lutea exhibited ipolipidemic effects. The objectives of the present work were to evaluate the potential of T. lutea F&M-M36 lyophilized biomass as a substrate for L. plantarum ATCC 8014 (selected among three lactic acid bacteria) growth, as well as to investigate the role played by the indigestible fraction of the microalgal biomass in bacterial growth (prebiotic effect). Finally, we aimed to verify the effect of fermentation on functional properties (the presence of functional components such as pigments and radical scavenging activity) as well as to evaluate whether fermented microalgal biomass could exert a protective role towards the probiotic bacterium during a simulated digestive process. ## 2.1. Microorganisms The marine haptophyte T. lutea F&M-M36 belongs to the Fotosintetica & Microbiologica Culture Collection (Florence, Italy), and the lyophilized biomass, obtained by cultivating the microalga in Green Wall Panel (GWP®) photobioreactors [32] under natural light, was purchased from Archimede Ricerche S.r.l. ( Camporosso, Imperia, Italy). The biomass was stored at −21 °C until use. The lactic acid bacteria (LAB) used were L. plantarum ATCC 8014, Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) LB28A, and L. casei LB28B. L. plantarum ATCC 8014 was purchased from Cruinn Diagnostic Ltd. (Dublin, Ireland). L. bulgaricus LB28A and L. casei LB28B were provided internally and were originally isolated from fermented products. Cultures were maintained in de Man, Rogosa, Sharpe (MRS) agarised medium (Oxoid Ltd., Basingstoke, United Kingdom). ## 2.2. Experimental Plan This study was organized in three phases (Figure 1): the first comprised the characterization of T. lutea F&M-M36 raw biomass and the choice of LAB strain to be used in the fermentation trial, which was carried out in the second phase, while the third included characterization of the fermented materials (raw biomass and post-digestion residue). More specifically, T. lutea F&M-M36 raw biomass was characterized for its biochemical profile and for in vitro digestibility. The dried digested residue was stored for further use. Concerning bacteria, three LAB strains were characterized for their growth performance and for resistance to in vitro simulated digestion, expressed as cell survivability. Once L. plantarum ATCC 8014 was selected among the bacteria, a fermentation trial was set up with T. lutea F&M-M36 raw biomass and post-digestion residue. The dried products obtained at the end of fermentation were analysed for biochemical composition, digestibility, and radical scavenging activity and compared with the unfermented counterparts. Survivability of L. plantarum ATCC 8014 to in vitro simulated digestion was evaluated also after fermentation with T. lutea F&M-M36 biomass. ## 2.3.1. Biochemical Composition Total protein was estimated following Lowry et al. [ 33] through calibration with bovine serum albumin (Sigma Aldrich, Milan, Italy). Carbohydrates were determined by the phenol-sulphuric acid method [34] and calibration with glucose (Sigma Aldrich). Lipids were analysed by carbonization with sulphuric acid [35] after extraction with chloroform:methanol (CHCl3:MeOH)1:2, phase separation followed by solvent evaporation [36] and calibration with tripalmitin (Sigma Aldrich). Ashes were determined by mineralizing preweighed aliquots of biomass in a muffle furnace at 500 °C for 24 h. Total dietary fibres (TDF) were estimated using an enzyme kit (K-TDFR-100A, Megazyme, Bray, Ireland) following AOAC 985.29 method. Fucoxanthin content was determined as follows: 15 mg of each sample was added with 270 μL of Sudan Red (Sigma Aldrich) solution (1 mg mL−1 in MeOH/methyl tertiary butyl ether (MTBE) 4:1 solution), monitoring standard for UV–Vis lamp, 150 μL of β-apo-carotenal (Sigma Aldrich) solution (1 mg mL−1 in MTBE), internal standard for quantification, and 7.5 mL of pure MeOH. The suspensions were heated at 60 °C for 15 min, at the end of which, they were vortexed and added with 7.5 mL of diethyl ether/petroleum ether solution (50:50) and 5 mL of NaCl solution ($20\%$ in water). The suspensions were vortexed again to allow phase separation, and the upper phase was collected. Steps starting with addition of diethyl ether/petroleum ether solution were repeated twice. The upper phases were collected in a rotary evaporation flask, dried under vacuum (Rotavapor RII, Büchi, Flawil, Switzerland), and resuspended in 3 mL of MeOH/MTBE 4:1 solution. The extracts were analysed by HPLC (1050, Hewlett Packard, Palo Alto, CA, USA) equipped with C30 reverse phase column (YCM Carotenoid, 4.6 mm × 250 mm, 5 μm particle size) (Waters, Millford, MA, USA) and UV photodiode array detector (Hewlett Packard 1050, USA) at 25 °C. A gradient method was adopted: $100\%$ eluent A ($81\%$ MTBE, $10\%$ MeOH, $9\%$ deionised water) for 1 min, passing to $92\%$ eluent A and $8\%$ eluent B ($93\%$ MTBE, $7\%$ MeOH) in 7 min, then to $10\%$ eluent A and $90\%$ eluent B in 8 min 50 s, holding this ratio for 2 min 20 s, then going back to $100\%$ eluent A in 50 s, and holding this condition for further 12 min. The injection volume was 20 μL with a constant flow rate of 1 mL min−1. Detection was held at 450 nm. An aliquot of the extracts obtained for fucoxanthin determination was diluted 1:60 in methanol and read spectrophotometrically (Cary 60, Agilent, Santa Clara, CA, USA) at 470, 652, and 750 to determine total carotenoids according to the equations for pure MeOH of Lichtenthaler and Buschmann [37]. ## 2.3.2. In Vitro Digestibility of Microalgal Raw Biomass In vitro digestibility of T. lutea F&M-M36 raw biomasses (before and after fermentation) was evaluated following Boisen and Fernández’s [38] method modified by Niccolai et al. [ 26], reproducing digestion occurring in the proximal tract (stomach and duodenum) of monogastric animals. Triplicate samples (1 g) (particle size ≤1 mm) were weighed in 100 mL flasks. Concurrently, 3 flasks for blanks were set up. Phosphate buffer (25 mL/sample, 0.1 M, pH 6.0) and HCl (10 mL/sample, 0.2 M, pH 2) were sequentially added to each flask, and then pH was adjusted to 2.0 by 5 M HCl addition. Then, 2 mL of porcine pepsin (0.8 FIP-U mg−1, Applichem, Darmstadt, Germany) solution (10 mg mL−1 in H₂O) was added to each flask. Flasks were incubated for 6 h at 39 °C under constant agitation (150 rpm). After incubation, phosphate buffer (10 mL/sample, 0.2 M, pH 6.8) and NaOH (5 mL/sample, 0.6 M) were sequentially added to each flask, and pH was adjusted to 6.8 with 5 M NaOH. After, 10 mL of a porcine pancreatin (42,362 FIP-U g−1, Applichem) solution (50 mg mL−1 in 1:1 ethanol-EtOH:water) was added, and then the flasks were incubated for 18 h at 39 °C under constant agitation (150 rpm). At the end, flask content was centrifuged (2840 g, with 10 min cycles; NEYA 8, REMI, Mumbai, India) in previously tared tubes. Once the supernatants appeared limpid, they were discarded, and pellets were washed with H₂O to remove salts and centrifuged again (2840 g for 30 min). Then, the pellets in the tubes were dried in an oven at 50 °C until constant weight. Digestibility was calculated as the difference between initial and final (after blank value subtraction) biomass weights divided by the initial biomass weight. ## 2.4.1. Bacterial Growth Bacterial growth was determined in MRS broth. Cultures were held in 250 mL flasks and maintained under constant agitation (150 rpm) at 28–30 °C, starting by diluting an actively growing inoculum culture. Bacterial concentration was measured as optical density (OD) at 600 nm. At the same time, the total number of cells was counted by a Neubauer chamber with 0.01 mm depth (Marienfeld, Lauda-Königshofen, Germany). Growth rate was calculated as the difference between OD natural logarithm at two different times divided by the time interval. ## 2.4.2. Bacterial Survivability to In Vitro Digestion To determine bacterial survivability in a monogastric digestive system, the protocol described by Naissinger da Silva et al. [ 39], simulating the transit through stomach and duodenum, was used and modified as follows. Biomass concentration (dry weight) of inoculum cultures was determined by filtering 2 mL of each culture on preweighed mixed cellulose ester membrane filters (Test Scientific, Perugia, Italy) with 0.22 μm pore size. Membranes were dried at 105 °C until constant weight. Dry biomass concentration (g L−1) was calculated as the difference between postfiltration dry weight and prefiltration (empty filter) dry weight, and the resulting value was divided by filtered volume. Then, culture aliquots corresponding to 0.25 g of dry weight each were centrifuged at 2560× g for 8 min. Supernatants were discharged while pellets were suspended in 6.5 mL of Nutrient Broth (Merck KGaA, Darmstadt, Germany) and transferred in 50 mL flasks. Each culture was tested in duplicate, and two controls (blanks) with only reagents were prepared. To each flask, 1.5 mL of porcine pepsin (0.8 FIP-U mg−1, Applichem) solution (5 mg mL−1 in 0.1 M HCl) was added, and pH was adjusted to 4.6 with 5 M HCl. Flasks were incubated for 20 min at 37 °C under constant agitation (150 rotations per minute, rpm). pH was then adjusted to 2.0 with 5 M HCl, and the flasks were incubated again for 70 min under the same conditions. Samples in two flasks were then adjusted to pH 5.0 with 5 M NaOH and kept for plate counting. The remaining two samples and controls were added with 2.5 mL of porcine pancreatin (42.362 FIP-U g−1, Applichem) solution (12.5 mg mL−1 in 0.1 M NaHCO₃), pH was adjusted to 5.0 with 5 M NaOH, and further incubation for 20 min was performed under the same conditions. Finally, pH was adjusted to 6.5 with 5 M NaOH prior to the last incubation step of 90 min under the same conditions. At the end, samples for plate counting were taken from each test and control flask. Plate counting was performed by serially diluting samples 1:10 in 96-well plates (Evergreen Scientific, Buffalo, NY, USA). Enumeration was performed by drop count technique [40], plating 10 μL of each dilution in triplicate on MRS medium. Colony forming units (CFU) were counted under a microscope (Eclipse 50i, Nikon, Tokio, Japan) after incubation at 37 °C for 24–48 h. Survivability was expressed as percentage compared to counts at the start of the process (just after suspension of the cell pellet in Nutrient Broth). The remaining culture volumes were centrifuged (2560× g for 10 min), supernatants were discharged, while pellets, after washing with deionized water, were dried at 50 °C until constant weight to determine digestibility of bacterial biomass. ## 2.5. Fermentation Trial L. plantarum ATCC 8014 was selected for the fermentation trial with T. lutea F&M-M36 raw biomass post-digestion residue. The fermentation trial was set up and conducted as reported in Figure 2. Two controls were prepared with L. plantarum in standard MRS (hereafter named 1:1) and in MRS with one-third of the dose indicated in the recipe (hereafter named 1:3). The investigated conditions were L. plantarum + T. lutea raw biomass suspended in H₂O, L. plantarum + T. lutea raw biomass suspended in MRS 1:3, L. plantarum + T. lutea post-digestion residue suspended in H₂O, and L. plantarum + T. lutea post-digestion residue suspended in MRS 1:3. All the mentioned treatments and controls were set up in triplicate. A control for prebiotic activity was also prepared in duplicate: L. plantarum + sodium alginate dissolved in MRS 1:3. All the treatments and the controls were sampled just after inoculation (T₀) after 24 (T₂₄), 48 (T₄₈), and 72 (T₇₂) h from the start of fermentation. Prior to the fermentation trial, the bacterial culture was kept in active growth phase, and for inoculation, the total number of cells was estimated by counting with a Neubauer chamber. On the basis of cell count, 30 mL of inoculum culture at 108 cell mL−1 were centrifuged for each treatment/control to be set up. Pellets were resuspended in 30 mL of fermentation matrix (MRS 1:1, MRS 1:3 or H₂O) in 50 mL flasks. Then, 2.5 g of raw microalgal biomass or microalgal post-digestion residue was added to the corresponding flask. A total of 0.5 g of sodium alginate was added to each positive control flask. The microalgal biomasses, sodium alginate and the enzyme powders were not sterile, while all the other materials and solutions were sterile. To evaluate the fermentation process development, bacterial growth was determined at each sampling by drop plating as described in §2.4.2. At each sampling, pH (pH 510, XS Instruments, Carpi, Modena, Italy) of the culture medium after centrifugation (see below for conditions) was measured. For determination of organic acids concentrations, 4 mL aliquots were collected at each sampling and centrifuged (2840× g for 20 min). Supernatants were collected and stored at −21 °C until analysis. Samples were sent to FoodMicro Team S.r.l., spin-off of the University of Florence, for analysis. L-lactate and acetic acid were determined by enzymatic assays [41,42] through an Hyperlab Plus analyzer (Steroglass S.r.l., Perugia, Italy). To obtain net acetate production, acetate concentration present in the medium (5 g L−1 of trihydrate sodium acetate in MRS 1:1, reduced to one-third in MRS 1:3) was subtracted from the acetate concentration obtained from supernatant analysis. At the end of fermentation, cultures with raw T. lutea biomass were tested in duplicate for digestibility: 4.5 mL of an equal mixture of the three replicates was centrifuged (2560× g for 8 min) and resuspended in phosphate buffer. A blank was also set up in duplicate. Digestibility was determined following the protocol used for microalgal biomass (§2.3.2). Bacterial survivability was determined after treatment with pepsin and after pancreatin (end of the process) as previously described. ## 2.6. Determination of Extracts Radical Scavenging Activity, Pigment and Total Phenolic Content Unfermented and fermented raw biomasses and digested residues were characterized for radical scavenging activity (RSA) through DPPH assay [43]. Lyophilized material (about 0.5 g) was extracted using, in succession, three solvents with increasing polarity: hexane, CHCl3:MeOH 1:2, and $30\%$ EtOH in water. Each extraction lasted 8 h. After extraction, solvent was separated by filtration on paper, and then residual solvent was allowed to evaporate from the material before addition of the successive solvent. Separated solvents were evaporated under vacuum, and residues were suspended in 5 mL of methanol (for hexane and CHCl3:MeOH 1:2) or $30\%$ EtOH. Dry weight of the extracts was determined by absorbing a known volume of each extract on preweighed glass fibre filters (Filter Lab, Barcelona, Spain) and then dried at 50 °C until constant weight. Dilutions of the extracts were prepared so as to obtain final solutions of 1 and 2 g L−1 (extract dry weight). These dilutions were tested for RSA: 0.5 mL of each extract was added with 0.5 mL of a 3·10−4 M 2,2-diphenyl-1-picrylhydrazyl (DPPH) (Sigma Aldrich) solution in dimethyl sulfoxide (DMSO) [43], allowed to react in the dark for 30 min, and read at 517 nm. The “analysis blank” was prepared with 0.5 mL of DMSO added with 0.5 mL of DPPH solution. A “sample blank” was also prepared to correct for absorption due to pigments; in this case, 0.5 mL of each extract was added with 0.5 mL of DMSO. To calculate RSA, the following equation was used [44]:RSA (%)=(AS+DPPH−AS+DMSO)/Ab×100 where A= absorbance at 517 nm; S= sample; and b= analysis blank. RSA activity was also expressed as vitamin E (Sigma Aldrich) equivalent antioxidant capacity. Vitamin E was dissolved in methanol, and different concentrations in the range of 0–100 mg L−1 were analysed as described for the samples to obtain a calibration curve. Pigment (chlorophyll a and c and total carotenoids) content in the extracts was determined with spectrophotometric analysis. Extracts were diluted (33 to 200 folds) in pure MeOH, and equations for pure MeOH were used to calculate chlorophyll [45] and total carotenoid [37] concentration. Pigment content was then calculated as mg g−1 of dry extract based on the extract dry weight previously determined. Total phenolic content was determined only for CHCl3:MeOH 1:2 and $30\%$ EtOH extracts and diluted to have a dry weight of 2 g L−1. For each extract, 100 μL were added with $2\%$ Na2CO3 solution in water (2 mL), then, after 2 min, with 100 μL of 1 N Folin Ciocalteu reagent (Sigma Aldrich). The samples were allowed to react in the dark for 30 min and then read at 720 nm. Total phenolic content was calculated as gallic acid equivalents per unit of extract dry weight based on a calibration curve prepared with gallic acid (Sigma Aldrich) (0–300 mg L−1). ## 2.7. Statistical Analysis Data were analysed by means of Student’s t-test or one-way analysis of variance (ANOVA) followed by Tukey’s or Dunnett’s multicomparison test performed using Prism 6 (GraphPad Software, Boston, MA, USA). The level of significance was $p \leq 0.05.$ ## 3.1. Selection of Bacterial Strain The growth rates of the three bacterial strains tested are shown in Table 1. Considering both the OD value and cell counts by counting chamber, L. plantarum showed the highest and L. casei the lowest growth rate. Bacterial survivability to in vitro digestion is reported in Figure 3 and is expressed as a percentage of the value at the start of the trial based on CFU mL−1 counts. In the case of L. casei, the number of CFU was strongly reduced after treatment with pepsin, resulting in a survivability of <$0.01\%$, which further decreased after treatment with pancreatin (<$0.001\%$). L. plantarum showed the best survivability, equal to $2.6\%$ after treatment with pepsin and $1.5\%$ after that with pancreatin. L. bulgaricus showed intermediate survivability ($0.3\%$ and $0.01\%$ after pepsin and pancreatin, respectively). After pepsin, the differences were significant ($p \leq 0.05$) among all strains, while after pancreatin, the survivability of L. plantarum was significantly higher compared to that of the other two strains, which showed no significant difference between them. ## 3.2. Fermentation L. plantarum was chosen for fermentation as it was the most resistant to digestion. L. plantarum growth curves during fermentation in the different conditions tested are shown in Figure 4a. In MRS 1:1, L. plantarum grew up to 7.7 logCFU mL−1 after 48 h, and then the concentration decreased. In MRS 1:3, the peak of growth was reached after 24 h (8.5 logCFU mL−1). In the presence of T. lutea raw biomass, the growth dynamic changed according to the fermentation matrix. In H₂O, the L. plantarum concentration increased until 48 h, reaching a value of 8.8 logCFU mL−1, while in MRS 1:3, the growth was biphasic with the highest concentration value reached after 24 h (8.5 logCFU mL−1) and a new increase (8.2 logCFU mL−1) after 72 h. The T. lutea post-digestion residue led to a lower L. plantarum growth, reaching its maximum after 48 h (7.4 and 7.8 logCFU mL−1 in H₂O and MRS 1:3, respectively). The control with sodium alginate dissolved in MRS 1:3 showed the lowest L. plantarum growth, which reached its maximum after 72 h (6.4 log CFU mL−1). L. plantarum growth was significantly lower compared to the control in MRS 1:3 only after 24 h in the MRS 1:1 control, in the culture with post-digestion residue suspended in H2O, and in that with alginate, which was the only curve to be significantly lower also after 48 h. Curves of pH during fermentation are shown in Figure 4b. In the MRS 1:1 control, the pH decreased from 5.4 to a minimum of 3.7 after 48 h, remaining stable until the end of the trial. In the MRS 1:3 control, the pH showed a sharper decrease in the first 24 h, reaching the minimum value after 48 h (from 6.7 to 3.4). With T. lutea raw biomass suspended in MRS 1:3, a progressive pH decrease was observed until the end of the trial (minimum value 4.4). With T. lutea post-digestion residue suspended in MRS 1:3, the pH was higher and reached its minimum after 48 h (from 6.6 to 5.0). In the presence of sodium alginate, the pH decreased to 4.8 after 24 h remaining constant until the end of the trial. Considering the water matrix, in cultures containing T. lutea raw biomass, the pH reached the minimum after 24 h (5.2), then increased until the end of fermentation, whereas with T. lutea post-digestion residue, the pH remained constant throughout (about 6.7). The initial pH values were all significantly ($p \leq 0.01$) lower compared to the MRS 1:3 control except for the cultures containing alginate and T. lutea post-digestion residue in H2O (Figure 4b). After 24 h, the pH was significantly different (higher) from the MRS 1:3 control only in the cultures with post-digestion residue, while at the remaining sampling times, all pH values except that of the MRS 1:1 control were significantly higher than in the MRS1:3 control. If the fermentation matrix is considered, the pH was significantly different between cultures in H2O and MRS 1:3 only after 48 and 72 h ($p \leq 0.05$) for both substrates. Lactic and acetic acid concentrations are shown in Figure 5. In MRS 1:1 control lactic acid (Figure 5a) showed a huge increase, reaching a maximum of 4.0 g L−1, in the time interval from 48 to 72 h, whereas in MRS 1:3 control the highest increase was observed between 24 and 48 h, although the maximum was attained at the end of the trial (2.8 g L−1). A similar behaviour, at higher concentrations (maximum of 4.9 g L−1), was observed in the culture with T. lutea raw biomass in MRS 1:3, while in the presence of post-digestion residue in MRS 1:3 the curve was similar until 48 h, where the maximum was reached (3.3 g L−1). With alginate in MRS 1:3, lactic acid attained the highest value (1.47 g L−1) after 24 h. In the cultures with H₂O as the matrix, lactic acid was very low during the whole trial, reaching a maximum of 0.35 g L−1 after 48 h with T. lutea raw biomass, whereas with postdigestion residue never surpassed the detection limit of the method. Acetic acid production (Figure 5b) was, in general, lower than that of lactic acid. Concentrations generally increased from the start to the end of the trial. The highest values were attained with alginate (1.7 g L−1), T. lutea raw biomass in H₂O (1.2 g L−1), and the MRS 1:1 control (1.0 g L−1). The only exception was T. lutea post-digestion residue in H₂O, in which the maximum acetic acid concentration (0.3 g L−1) was reached after 48 h of fermentation. ## 3.3.1. Biochemical Composition In Table 2, the biochemical composition of T. lutea raw biomass fermented in both matrixes is compared with that of unfermented raw biomass. Considering proximate composition, only carbohydrate content resulted as significantly different (higher) in the unfermented compared to the two fermented biomasses. The post-digestion residue had a composition not significantly different from that of the raw biomass ($p \leq 0.05$). Among fermented residues, the only component showing a significant difference with the unfermented residue was carbohydrates when the fermentation matrix was MRS 1:3. Significant differences were instead present for functional molecules, such as total carotenoids and fucoxanthin, which were strongly affected by digestion ($p \leq 0.001$) and by fermentation of raw biomass (Table 2), whereas no decrease during fermentation of post-digestion residue was observed. The fraction of each raw biomass component remaining in the residue at the end of the in vitro digestion of T. lutea biomass is illustrated in Figure 6a (dark-coloured bar). The less digested component was protein followed by lipids (about 40 and $36\%$ of the content in the initial biomass was present in the residue). The components most strongly reduced during digestion were carbohydrates and TDF (about $28\%$ of the value in the initial biomass present in the residue). Functional components such as total carotenoids and, in particular, fucoxanthin were also highly reduced during digestion (only 19 and $7\%$, respectively, of the content in initial biomass present in the residue). Figure 6b reports the pigments normalized absorbance spectra of post-digestion T. lutea solid residue extracted with hexane, CHCl3MeOH 1:2, and EtOH $30\%$ and of the digestion supernatant (i.e., the digested fraction). With all three solvents used, the highest absorbances were registered from 400 to 450 nm (chlorophylls and carotenoids) and from 660 to 670 nm (chlorophylls), where the highest value was that of the digestion supernatant. ETOH $30\%$ extract showed high absorbances only in the blue region. ## 3.3.2. Digestibility of T. lutea F&M-M36 Biomass In Figure 7a, the digestibility (%) of T. lutea raw biomass is shown. The microalgal biomass showed a digestibility of about $65\%$, and no significant differences ($p \leq 0.05$) were found between the unfermented and the fermented biomasses (independently of the fermentation matrix) or between the two fermentation conditions. The survivability of L. plantarum in the fermented substrate after treatment with the digestive enzymes is shown in Figure 7b. In the substrate fermented in MRS 1:3, the survivability of L. plantarum was about $1\%$ after the treatment with pepsin and decreased to lower than $0.01\%$ after that with pancreatin. In the substrate fermented in water, the survivability of L. plantarum was around $6.6\%$ after pepsin and about $0.06\%$ after pancreatin. The comparison between the two matrixes showed no significant differences ($p \leq 0.05$). Furthermore, probably due to the high variability, no significant difference was found within the same matrix after treatment with the two enzymes. ## 3.3.3. Antioxidant Activity of T. lutea F&M-M36 Extracts In Figure 8, the antioxidant activity of sequential extracts obtained from T. lutea raw biomass and digested residues is shown. In the first extraction with hexane, a significant difference was detected between the unfermented post-digestion residue ($13\%$ RSA) and the post-digestion residue fermented in MRS 1:3 ($21\%$ RSA). No significant differences were detected among the other conditions where RSA showed values within the range of 13–$18\%$. The extracts obtained in CHCl3:MeOH 1:2 showed the highest RSA values. Raw biomass RSA was significantly higher when fermented in MRS 1:3 ($58\%$) than in H2O ($27\%$) and when not fermented ($30\%$). In addition, raw biomass fermented in MRS 1:3 had a significantly higher RSA with respect to the correspondent treatment with post-digestion residue ($52\%$). No other significant differences were detected among the samples. In the last extracts obtained in $30\%$ EtOH, RSA values ranged from 9 to $12\%$, and no significant differences were detected. Vitamin E equivalents of RSA values are also reported in Figure 8. The highest equivalents were in the order of 50 mg per gram of extract dry weight and were obtained for raw biomass fermented in MRS 1:3 extracted in CHCl3:MeOH 1:2, while the lowest values were in the order of 10 mg g−1 and were found for extracts in $30\%$ EtOH. ## 3.3.4. Pigment Content of T. lutea F&M-M36 Extracts Pigment content (Chl a, Chl c, and total carotenoids) was quantified (mg g−1 of dried extract) in the sequential extracts obtained with hexane, CHCl3:MeOH 1:2, and $30\%$ EtOH (Figure 9). Chl a was mostly extracted with the first two solvents. In hexane, its content varied little (25–30 mg g−1) among the three raw biomass extracts as well as among the three post-digestion residue extracts (30–36 mg g−1), although the content was rather higher in post-digestion residue than in raw biomass after fermentation in the organic matrix. In the CHCl3:MeOH 1:2 extracts, Chl a increased progressively in the raw biomass extracts from unfermented to fermented in water and finally to fermented in an organic matrix, going from 10 to 25 mg g−1. In the post-digestion residue, the trend was similar, but the differences were much lower, going from 30 to 37 mg g−1. In the extracts with EtOH $30\%$, Chl a content was very low, never exceeding 1.5 mg g−1. Chl c was detected only in CHCl3:MeOH 1:2 and $30\%$ EtOH extracts, although a very different behaviour was observed between raw biomass and post-digestion residue. In the former, almost all Chl c was extracted with CHCl3:MeOH 1:2 (7.8–9 mg g−1) independently of the treatment. On the contrary, Chl c in the post-digestion residues was detected in the two extraction solvents with differences among the treatments: An equal amount was extracted from the unfermented residue (about 1 mg g−1); a higher extraction in CHCl3:MeOH 1:2 (ca 3 mg g−1) was obtained for the residue fermented in MRS 1:3, while the residue fermented in H2O showed higher extraction values in $30\%$ EtOH (ca 3 mg g−1). Finally, for all the treatments, carotenoids were almost exclusively extracted with CHCl3:MeOH 1:2. An increasing content (from 12 to 21 mg g−1) was observed in raw biomass extracts from unfermented to water-fermented to organic-matrix-fermented biomass. In the post-digestion residue, carotenoid content was lower and similar among the three treatments (9–10 mg g−1). ## 3.3.5. Total Phenolic Content of T. lutea F&M-M36 Extracts Total phenolic content (mg GAE g−1) was analysed in CHCl3:MeOH 1:2 and $30\%$ EtOH extracts from T. lutea raw biomass and post-digestion residue, either unfermented, fermented in H₂O, or in MRS 1:3 (Figure 10). With CHCl3:MeOH 1:2, no significant differences were detected, neither between the different substrates (raw biomass or digested residue) nor among the experimental conditions. The total phenolic content in raw biomass was 20.8 mg GAE g−1 in the unfermented sample, 30.1 mg GAE g−1 in the sample fermented in H₂O, and about 35 mg GAE g−1 in the sample fermented in MRS 1:3, while in the post-digestion residue, total phenolic content was 30 mg GAE g−1 in the unfermented residue and 28 mg GAE g−1 with both fermentation matrixes (H₂O and MRS 1:3). In the $30\%$ EtOH extract, the total phenolic content of the unfermented raw biomass was significantly lower than in the unfermented post-digestion residue (13 and 18.5 mg GAE g−1, respectively). In the samples fermented in H₂O, a significant difference was detected between the two substrates (17 and 23 mg GAE g−1 in the raw biomass and post-digestion residue, respectively). No significant differences were detected between the samples fermented in MRS 1:3, where for both substrates, phenolic content was 17 mg GAE g−1. ## 4. Discussion In recent years, increasing attention has been addressed to healthy lifestyles, including nutrition [46,47,48]. Traditional foods, such as fermented products, have also been revisited to improve their healthiness and to better focus on their functionality [13]. Among the improvements, it is worth mentioning the characterization of probiotic properties in bacteria traditionally used to ferment food matrixes, the addition of probiotic strains to the traditional ones, and the investigation of new fermentation substrates [13,49,50]. Microalgae represent an interesting substrate, being endowed with many functional properties and an equilibrated nutritional profile [3,26]. In this framework, a preliminary screening to select the most suitable lactic acid bacterium to perform fermentation with T. lutea as the substrate was performed, and the nutritional and functional properties of fermented biomass were compared with those of raw algal biomass. ## 4.1. Probiotic Bacterial Strain Selection The three bacterial strains tested (L. plantarum ATCC 8014, L. bulgaricus LAB28A, and L. casei LAB28B) showed low survivability to the digestive process simulated in vitro. In particular, a larger reduction was observed after the step mimicking the transit through the stomach, where, besides the action of pepsin, the pH is extremely acidic (2.0). The results obtained in the present work (highest survivability of $3\%$ after stomach and $1.5\%$ after intestine passage simulation with L. plantarum) are consistent with the literature. A mixed culture of L. casei, L. plantarum, and L. rhamnosus showed a survivability of <$1\%$ (approximately from 14 to 11 logCFU mL−1) when treated with pepsin at pH 3.0 for 1 h at 38 °C [51]. Naissinger da Silva et al. [ 39] tested the survivability of commercial probiotic preparations, obtaining in most cases a survivability of 0.2–$7.9\%$ after stomach and 1.2–$6.3\%$ after stomach plus duodenum simulation. L. plantarum PL02 showed a decrease from 8.40 to 5.55 logCFU mL−1 ($0.14\%$ of survivability) when subjected to the action of acid alone at pH 2.0 for 3 h at 37 °C [52]. Lactobacilli are known to be exopolysaccharide producers [53]. Among the strains tested in this work, L. casei LAB28B produced high exopolysaccharide amounts, evidenced by culture medium viscosity. However, exopolysaccharides did not exert a protective effect on cell vital functions against low pH and digestive enzymes, as L. casei showed the lowest survivability. On the contrary, if looking at the ability of the enzymes to digest the bacterial cells, exopolysaccharides seem to reduce their accessibility to cell structures, as L. casei final digestibility was more than $50\%$ lower than that of the other two bacteria. Since L. plantarum ATCC 8014 showed the highest growth rate and resistance to in vitro digestion, it was chosen for the fermentation trial. In addition, L. plantarum is a versatile lactobacillus, being aerotolerant [54], and is certified as GRAS (generally recognized as safe) and QPS (qualified presumption of safety) [55]. ## 4.2. Fermentation of T. lutea F&M-M36 with L. plantarum ATCC 8014 Fermentation was carried out in two different matrixes, water and MRS diluted 1:3. The two matrixes led to similar final bacterial concentrations. Nevertheless, the growth curves showed different patterns: In MRS 1:3 culture, bacterial growth was biphasic, while in water it was monophasic. It is possible that in MRS 1:3, the bacteria already adapted to MRS, first used MRS components, and probably the soluble components of T. lutea raw biomass without the need to change their enzyme array; once these readily assimilable compounds were depleted, there was a growth halt during which the bacteria synthesized a proper set of enzymes to use those components of microalgal biomass more resistant to degradation (e.g., β-glucans, proteins, lipids). In water, the bacteria initially grew using the few readily assimilable compounds released from microalgal biomass while, at the same time, adapting to exploit the more difficult ones. Microalgal biomass is a complex substrate composed of large protein, lipid, and complex carbohydrate fractions. It is known that some strains of L. plantarum possess proteolytic activity and may also produce enzymes able to degrade complex carbohydrates and lipids [55,56,57]. Moreover, a partial contribution of autochthonous bacteria of T. lutea F&M-M36 biomass might be plausible. These bacteria, although present in lower amounts compared to L. plantarum (about three orders of magnitude lower, 5.2–5.4 log CFU mL−1, as seen in T. lutea fermentation without L. plantarum), might have contributed to the degradation of the more difficult macromolecules of microalgal biomass, thus providing nutrients useful to L. plantarum growth, as already hypothesized in the case of other fermentation substrates [58]. To fully understand these dynamics, further experiments to quantify the different compounds available in the fermentation broth along the process will be necessary. The main organic acid produced in the cultures in MRS 1:3 was lactic acid, while in water, acetic acid was produced in similar amounts as lactic acid. The reason for this different behaviour may reside in the complexity of microalgal biomass and its degradation compounds, which might have led also to the formation of different fermentation end-products [59], not characterized in this work, that, in a matrix such as water, might have emerged sooner than in diluted MRS. For example, Taniguchi et al. [ 60], at the end of a 7-day fermentation of $10\%$ A. flos-aquae biomass in water with L. plantarum AN7, found, besides a major production of lactic and acetic acid, small amounts of ethanol. L. plantarum ATCC 8014 was used to ferment A. platensis biomass as the only available substrate in water or in soybean milk providing additional organic compounds, showing similar growth but much different organic acid production patterns [16]. In this work, pH also highlighted the differences between the two matrixes, as it showed a significant negative correlation ($p \leq 0.01$) with lactic acid production in diluted MRS, while in water, a significant but weaker negative correlation was observed ($p \leq 0.05$). On the contrary, in neither matrix, the pH significantly ($p \leq 0.05$) correlated with acetic acid production, although the worse result was again obtained in water. This could be at least partially explained by better preservation of microalgal biomass buffering capacity, a trait observed in several microalgae [61,62], in water compared to MRS 1:3, which has a lower starting pH (about 5.5). The matrix in which fermentation is performed appears, in this work as well as in the literature, of great importance for fermentation outcomes and should be specifically addressed in future experiments. As the matrix impacts fermentative metabolism, it affects organic acid production, an aspect to be dealt with in future developments, considering the high technological relevance of organic acids in food preservation, as antibacterial components and as food taste modifiers [15]. L. plantarum is a well-known bacteriocin producer with large intraspecific differences [63] that could represent a criterium for strain selection to optimize fermentation. Another criterium for the choice of fermenting bacterium is the response to salinity since T. lutea is a marine microalga and its biomass is characterized by rather high salinity values (36 g L−1 for T. lutea F&M-M36 raw biomass suspended in water at the concentration used in the fermentation trials). The tolerance of L. plantarum to increasing salinity has been shown to be strain-dependent with complete growth inhibition at 8–$10\%$ salinity [64] and survival rates in excess of $80\%$ at $6\%$ salinity depending on the other culture parameters [65]. It is possible that L. plantarum ATCC 8014 did not represent the optimal choice to ferment T. lutea biomass, as no screening for salinity tolerance was performed. For future developments, an in-depth investigation to identify the most suitable bacterial strains to ferment marine microalgal biomass will be necessary to optimize the process in terms of growth and functional components. ## 4.3. Potential Prebiotic Effect of T. lutea F&M-M36 To verify whether the growth of L. plantarum ATCC 8014 on T. lutea F&M-M36 biomass could be due to a prebiotic effect, the residue left after in vitro-digestion of biomass was used as a fermentation substrate. Microalgae contain high amounts and varieties of polysaccharides with potential prebiotic effects, such as storage polysaccharides and cell wall components [66]. Several studies have been performed on microalgae prebiotic potential (enhancement of probiotic bacteria growth), mainly for Arthrospira and, to a lower extent, Chlorella [14,67], although compounds responsible for this type of activity have not been fully elucidated. A prebiotic effect higher than that of a fructooligosaccharide of the digested fraction (the opposite fraction with respect to that used in the present work) of Chlorella vulgaris (C. vulgaris), Spirulina platensis (S. platensis), Desmodesmus maximus, and Chlorococcum cf hypnosporum biomasses on human gut microbiota grown anaerobically in vitro was found, with the different microalgae stimulating different microbial groups in the microbiota [68]. The starting microalgal biomasses contained high amounts of fibres [68]; however, the amount actually present in the digested biomass was not determined. In the present work, L. plantarum grown with T. lutea post-digestion residue showed a lower growth compared to that with raw biomass; moreover, growth was higher in the diluted MRS matrix than in water. Lactobacilli are known to be nutritionally demanding in terms of amino acids, peptides, vitamins, fatty acids, and carbohydrates [58,69], therefore, it is possible that L. plantarum growth is unsustainable when the post-digestion residue is used as the only substrate due to a lack of nutritional compounds. However, lactic acid production was higher in L. plantarum + T. lutea post-digestion residue in MRS 1:3 than in the control culture of L. plantarum in MRS 1:3, suggesting that some components within the residue could actually contribute to the fermentative process. Sodium alginate in MRS 1:3 used as a control for prebiotic activity [70], being a component often found in algae, showed the lowest L. plantarum growth, producing a lower amount of organic acids compared to both raw biomass and control in MRS 1:3. This indicates a lack of prebiotic activity under the tested conditions. Interestingly, it was the largest producer of lactic acid in the first 24 h, suggesting that not enough nutrients were present for the following period of fermentation for its prebiotic effect to be exerted. The potential prebiotic effect of T. lutea partly observed in the fermentation performed in diluted MRS could be related to the fibres contained in the microalgal biomass. About $28\%$ of total dietary fibre remained after digestion, indicating that probably the majority of fibres were soluble and thus easily utilizable. Nevertheless, the fraction left in the digested residue could contribute to prebiotic effects. To further analyse the potential prebiotic effect, trials on a colon resident instead of a lactic acid bacterium could provide more indications. ## 4.4. Characterization of Nutritional and Functional Properties of Unfermented and Fermented T. lutea F&M-M36 The biochemical composition of T. lutea reported in the literature appears rather variable with different macromolecular constituents in turn reported as the most abundant [71,72,73,74,75,76]. In this respect, the data obtained in the present work lie within the range of this variability, with protein being the major constituent followed by lipids and carbohydrates. The fermentation process significantly reduced carbohydrate content, leaving the other main constituents substantially unchanged. To our knowledge, no data on T. lutea fermentation are available for comparison. It has to be considered that about one-third of the initial biomass was lost during fermentation, a value much lower than for substrates such as food waste (about $80\%$) [77], which probably reflects the presence in the microalgal biomass of components difficult to be degraded but also confirms the need for process optimization. This could also partly explain the lack of difference in digestibility of the fermented biomass. As to the digestion residue, its proximate composition is similar to that of raw biomass, i.e., the ratios among different components are almost constant (Table 2). However, if the fraction of each component remaining in the residue is considered, a different picture emerges, as the extent to which each component is conveyed into the fraction that could be considered as bioaccessible changes. In fact, bioaccessibility was $59\%$ for protein, $64\%$ for lipids, and $72\%$ for carbohydrates (Figure 6a). Bonfanti et al. [ 74] investigated the bioaccessibility of the lipid fraction in a taxonomically close microalga, Isochrysis galbana. Although a different in vitro digestion protocol was applied, a bioaccessibility of total fats of only about $12\%$ was found. Cavonius et al. [ 78] evaluated the degree of protein hydrolysis in Nannochloropsis oculata using an in vitro digestion model (different from that of the present work), finding that 32–$50\%$ of the peptide bonds were hydrolysed according to biomass pretreatment. Similar values were obtained by Hori et al. [ 79] who found a protein digestibility of $43\%$ for N. commune biomass. As to functional properties, the Italian Ministry of Health [80] states that to consider a product a probiotic, it must contain at least 109 living bacteria in a daily ration based on the assumption that this is the minimum number necessary to permit temporary colonization of the intestine. To exert a probiotic effect, thus, a product based on T. lutea F&M-M36 biomass fermented for 72 h should be ingested at a daily ration of 8.4 mL for MRS 1:3 matrix and 21.5 mL for water matrix (2.3 mL if the product is fermented only for 48 h). However, for the probiotic effect to be exerted, it is necessary that the probiotic bacterium survives the passage through the stomach and the proximal part of the intestine. In this respect, the survivability to the simulated stomach passage of L. plantarum ATCC 8014 at the end of the fermentation with T. lutea was not improved compared to the survivability of the bacterium alone, indicating that microalgal biomass does not have a protective effect, independently of the fermentation matrix. Moreover, a strongly decreased bacterial survivability was observed after the simulation of the intestine passage for both fermentation matrixes. Further investigations are needed to fully clarify these findings. No data are available to our knowledge on the effects on bacterial survivability to in vitro digestion after fermentation of microalgal substrates. The addition of unfermented fresh biomass from several microalgae (C. vulgaris, Scenedesmus quadricauda, Lagerheimia longiseta (L. longiseta), S. platensis) to living probiotic bacteria (L. acidophilus 05 or L. casei 01), then lyophilized and digested in vitro, resulted in not being effective in protecting the probiotic bacteria in the passage through the stomach, but C. vulgaris, L. longiseta, and S. platensis were able to improve survivability after the intestine phase compared to the control in a saline solution [81]. One of the most important functional properties of microalgae is antioxidant activity, primarily related to the high pigment content. In this work, radical scavenging activity determined by the DPPH assay was higher in the lipophilic extracts rich in chlorophyll and carotenoids. The activity in the hexane extract containing mainly chlorophyll a was low except with high extract concentrations, thus, this molecule is probably not the main antioxidant compound. The activity seems due overall to carotenoids, including fucoxanthin. Fermentation negatively affected total carotenoid content (−$36\%$) and particularly fucoxanthin (−$68\%$). However, fermentation seems to play a positive role by increasing access to pigments, and thus extraction yield, as can be inferred by the increased carotenoid content in both the fermented raw biomass and post-digestion residue. The higher carotenoid content is reflected in the increased radical scavenging activity of the extracts from biomasses fermented in diluted MRS. Increased pigment (carotenoid) extractability could not fully explain the increased antioxidant activity, which might be partly due to the presence of other compounds of microalgal or bacterial origin. Silva et al. [ 82], for an ethyl acetate extract from the biomass of a commercial T. lutea, obtained an IC50 of 0.8 g L−1, more than half that for lipophilic extracts in this work (about 1.7–1.8 g L−1). A water extract from Pavlova lutheri KMCC H-006 fermented with the yeast Hansenula polymorpha showed an IC50 value of 0.3 g L−1 [83], an activity much higher than that of the extract in $30\%$ EtOH in water obtained from fermented biomass in the present work, which did not reach values exceeding $50\%$, making it impossible to calculate IC50. It is noteworthy that this extract was the final step of solvent extractions in succession; the lipophilic extracts obtained in the first extraction steps reached IC50 values in the range of 0.8–1.0 g L−1. Besides functional properties, to develop products based on fermented T. lutea it will be necessary to consider organoleptic features to obtain a palatable product. T. lutea biomass is brown, which makes it look unappetizing. Currently, a strategy applied to surpass the colour limit for consumers is to choose shakes, juices, and fruit-based beverages as foods to which to add the microalga since their vivid colours are associated with healthy foods, and they might smooth down the visive impact with the colour of the microalga [12]. However, T. lutea has also a strong smell (and taste); a possible strategy would be to choose, as matrix, a sauce/condiment used to season fish, crustaceans, or other marine-derived food. It is remarkable that, as a first impression in the present work, the fermentation process has smoothed the smell of T. lutea biomass. This aspect deserves to be investigated with specific sensory analysis because if the first impression will be confirmed, fermentation could represent a good strategy to overcome this limit for T. lutea biomass application in foods. ## 5. Conclusions T. lutea F&M-M36 biomass shows a good nutritional profile and rather good digestibility. Bioaccessibility is higher for carbohydrates and, among functional components, for fucoxanthin. T. lutea F&M-M36 has a good potential as a substrate for fermentation with L. plantarum ATCC 8014, although thorough strain selection for the fermenting probiotic bacterium is recommended for future developments because of the salinity of this marine microalga. Fermentation reduces carbohydrates as well as fucoxanthin. In spite of this, the higher radical scavenging activity in the extracts from biomass fermented in a diluted organic medium suggests that fermentation could make pigments more easily accessible. The role of the fermentation matrix needs further investigation. The presence of the microalgal biomass does not increase the survivability of the probiotic bacterium to the digestive process in the fermented product. Moreover, fermentation does not improve microalgal biomass digestibility. Although some ability of the microalgal post-digestion residue to improve bacterial growth during fermentation was observed, further tests are needed using colon-dwelling bacteria to confirm prebiotic activity, while the growth of L. plantarum with T. lutea raw biomass seems related to bioaccessible nutritional elements. Finally, in-depth studies will be needed to improve the organoleptic characteristics of this biomass if aiming at wide use in the functional food industry. Approval of T. lutea as food by competent authorities would help increase research interest in its application in the food industry, including the development of functional fermented products. ## References 1. Matos, Ậ P.. The impact of microalgae in food science and technology. J. Am. Oil Chem. Soc. (2017) 94 1333-1350. DOI: 10.1007/s11746-017-3050-7 2. Kaga Y., Kuda T., Taniguchi M., Yamaguchi Y., Takenaka H., Takahashi H., Kimura B.. 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--- title: An Assessment of MT1A (rs11076161), MT2A (rs28366003) and MT1L (rs10636) Gene Polymorphisms and MT2 Concentration in Women with Endometrial Pathologies authors: - Kaja Michalczyk - Patrycja Kapczuk - Grzegorz Witczak - Piotr Tousty - Mateusz Bosiacki - Mateusz Kurzawski - Dariusz Chlubek - Aneta Cymbaluk-Płoska journal: Genes year: 2023 pmcid: PMC10048541 doi: 10.3390/genes14030773 license: CC BY 4.0 --- # An Assessment of MT1A (rs11076161), MT2A (rs28366003) and MT1L (rs10636) Gene Polymorphisms and MT2 Concentration in Women with Endometrial Pathologies ## Abstract Several studies have indicated a relationship between metallothionein (MT) polymorphisms and the development of different pathologies, including neoplastic diseases. However, no studies thus far have been conducted on the influence of MT polymorphisms and the development of endometrial lesions, including endometrial cancer. This study included 140 patients with normal endometrial tissue, endometrial polyps, uterine myomas and endometrial cancer. The tissue MT2 concentration was determined using the ELISA method. MT1A, MT2A and MT1L polymorphisms were analyzed using TaqMan real-time PCR genotyping assays. We found no statistical difference between the tissue MT2 concentration in patients with EC vs. benign endometrium ($$p \leq 0.579$$). However, tissue MT2 concentration was significantly different between uterine fibromas and normal endometrial tissue samples ($$p \leq 0.019$$). Menopause status did not influence the tissue MT2 concentration ($$p \leq 0.282$$). There were no significant associations between the prevalence of MT1A, MT2A and MT1L polymorphisms and MT2 concentration. The age, menopausal status, and diabetes status of patients were identified as EC risk factors. ## 1. Introduction Endometrial cancer (EC) is the most common gynecological malignancy, yet its incidence is still rising with little improvement in patient survival [1]. It is the sixth most common cancer in women worldwide. In 2020, in accordance with the World Cancer Research Fund International, the global incidence of EC was 417,367 patients, with an ASR (age-standardized rate) of 8.7 per 100,000. Poland was found to have the highest rate of endometrial cancer diagnosis at 26.2 ASR/100,000 [2]. The International Agency for Research on Cancer estimates that the incidence rate of endometrial cancer will rise by more than $50\%$ worldwide by 2040 [3]. It is a heterogeneous disease that varies in molecular characteristics and patient prognosis. Despite numerous studies investigating the pathophysiology and mechanisms responsible for the formation of endometrial cancer, the pathogenesis is still not fully understood. The formation of endometrial cancer involves multiple molecular pathways and epigenetic changes. Despite the red flag symptoms, including vaginal bleeding, that often allow for the early detection of an endometrial carcinoma, there are still no highly specific or sensitive biomarkers. The main risk factors for endometrial cancers include elevated estrogen levels and obesity, especially in postmenopausal women [4]. On the other hand, the use of combined estrogen–progestin hormonal therapy and physical activity were found to be protective of EC [5,6]. Metallothioneins (MTs) are a group of cysteine-rich proteins (MT-1 through MT-4) located on chromosome 16q13. They are major intracellular zinc-binding proteins responsible for zinc uptake, distribution and storage. They also have a regulatory role in the transportation, protection against oxidative stress and toxic effects of trace metals and other particles, including Copper, Cadmium, Lead and glutathione [7,8]. They also participate in processes that include the metabolism of free radicals and apoptosis [7]. In physiological conditions, they are usually expressed at low levels [9]. Their synthesis was found to increase during oxidative stress to protect cells against potential cytotoxicity, radiation or DNA damage [10,11,12,13]. They are involved in the pathophysiology of various diseases, including cancer [14,15], yet their role remains unknown. As metallothioneins were found to have antiapoptotic, antioxidant, proliferative and angiogenic effects, there is an increased focus on determining their role in oncogenesis, tumor progression, response to cancer therapy and patient prognosis [16]. Multiple studies have demonstrated an increased expression of both tissue and serum MT levels in lung, kidney, prostate, testes, urinary bladder, pancreatic, cervical and endometrial cancers. In some, the expression of MT was found to correlate with the tumor staging, grading, treatment resistance and prognosis [14,17,18]. However, the expression of MT seems to differ between the tumor types and may depend on the type of tumor differentiation status, its environment or associated gene mutations [14]. The prevalence of multiple gene polymorphisms was found to be associated with an increased cancer risk [19,20]. For our study, we selected gene polymorphisms characterized by a relatively high frequency to assess their influence on endometrial cancer risk in association with MT2 concentration. The polymorphisms of MT1 and MT2 were previously reported to be directly involved in malignant transformation, xenobiotic metabolism and/or oxidative stress processes. In our study, we decided to determine the presence of MT1A (rs11076161), MT2A (rs28366003) and MT1L (rs10636) polymorphisms among the studied patients as research on the influence of the selected polymorphisms on the risk of developing endometrial cancer has not yet been conducted. This study aimed to determine the potential of MTs as biomarkers for endometrial cancer diagnosis, with regard to other risk factors such as obesity and diabetes. The knowledge about MTs may provide a new insight into endometrial cancer diagnostics, especially in correlation with other clinical examinations. ## 2. Materials and Methods This study consisted of 140 patients treated at the Department of Gynecological Surgery and Gynecological Oncology of Adults and Adolescents, Pomeranian Medical University. It was a case—control study of patients with a confirmed diagnosis of endometrial cancer based on histopathological evaluation who were admitted for a radical surgery at the time of the study. The control group consisted of patients admitted for hysteroscopy or myomectomy. Patients suffering from chronic diseases, the recurrence of endometrial cancer, previous cancer treatment or other types of primary cancer were excluded from the study. Patients with incomplete or missing data were also removed from the study group. Finally, 110 patients were included in the final analysis. Informed consent to participate in the study was obtained from both patients and controls. Patient characteristics are demonstrated in Table 1. Of the included 21 patients diagnosed with endometrial cancer, 19 were diagnosed with endometrioid carcinoma, 1 with a serous EC and 1 with an undifferentiated carcinoma. For tumor characteristics, the patients’ FIGO (The International Federation of Gynecology and Obstetrics) staging, tumor grading and the presence of lymph node metastases were assessed. The research was conducted in accordance with the Helsinki Declaration and with the consent of the Ethics Committee of Pomeranian Medical University in Szczecin, under the number KB-$\frac{0012}{27}$/2020, on 9 March 2020. ## 2.1. Blood Sample and Tissue Collection Blood samples were taken from patients at the time of hospital admission. Two blood samples were collected. One was centrifuged, while the other was directly frozen. The tissue specimen was collected during the surgery, either during a hysteroscopy/laparoscopy or laparotomy. The specimens were stored at a temperature of −80 degrees Celsius. ## 2.2. Measurement of Tissue MT2 The tissue MT2 concentration was determined using the ELISA method. The collected fragments of carcinomas, fibroids, polyps and normal endometrial tissues underwent knife homogenization in the liquid nitrogen. To homogenize, a PBS–potassium phosphate (pH 7.4) lysis buffer was used containing NaCl 0.14M; KCl 0.0027M; Phosphate buffer, pH 7.4; 0.010M (Witko, Poland). Homogenates then underwent a 20 min sonification at 4 °C and were centrifuged at 15,000× g for 20 min at 4 °C. Supernatants were stored at −80 °C for later analysis. The concentration of Human Metallothionein-2 was calculated based on the protein quantity in the specimens. Protein concentrations were measured using a MicroBCAPierce ™ kit (Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer’s instructions, and with a plate reader (BiochromAsys UVM 340, Biochrom, Cambridge, UK) at 562 nm. The concentration of Human Metallothionein-2 was measured with the Human Metallothionein-2 (MT-2) ELISA kit (MyBioSource, San Diego, CA, USA, Cat # MBS703385), according to the manufacturer’s instructions. The extracted material was defrosted, and the reaction mixtures were transferred onto an ELISA microplate, following the manufacturer’s protocol. The optimum dilutions were selected by checkerboard titration. Sandwich ELISAs were performed using 96 well microtiter plates coated with MT2 biotin-conjugated monoclonal antibodies. Finally, horseradish peroxidase (HRP) and a 3,3’,5,5’-Tetramethylbenzidine (TMB) substrate were added to the wells. There was a visible color change. After stopping the reaction with the Stop Solution, the absorbance was read at 450 nm using Biochromasys UVM 340 technology. The signal intensity was directly proportional to the amount of MT2 in the sample. Concentrations were expressed in ng/mg of protein. ## 2.3. Single-Nucleotide Polymorphism Analysis The patients were genotyped for the following single-nucleotide polymorphisms (SNPs) within MT1A, MT2A and MT1L. The following variants were genotyped: MT1A rs11076161 A > G, MT2A rs28366003 A > G and MT1L rs10636 G > C. Genomic DNA was isolated from 0.2 mL of whole blood samples, using a commercial kit for genomic DNA isolation and the Genomic Mini AX Blood 1000 Spin (A&A Biotechnology, Gdańsk, Poland). TaqMan real-time PCR genotyping assays (Thermo Fisher Scientific) were used for the detection of the studied SNPs (Assay IDs: C_1402094_10, C_60284591_10, C_25996927_10). ## 2.4. Statistical Analysis The statistical analysis was performed using Statistica 10 software. The results were presented as the mean ± SD or absolute frequencies and percentage values, in accordance with the type of the variable. The unpaired, two-sided Student’s t-test, the chi-square test and the Mann-Whitney test were used to compare the sociodemographic, clinical and questioned data between the groups. Significance was defined at $p \leq 0.05.$ The Mann–Whitney U test was used to compare the studied group with the control group. Kruskal–Wallis and post hoc tests were used to measure the differences between multiple groups. The Spearman correlation was created to check for any correlations between the measured parameters. The occurrence of SNPs was estimated by an odds ratio (OR) analysis with a $95\%$ confidence interval, using univariable logistic regression models. ## 3.1. Tissue MT2 Concentrations In our study, there was no statistical difference between the tissue MT2 concentration in patients with malignant vs. benign endometria ($$p \leq 0.579$$). As a part of the study, we also evaluated for any differences between other groups, i.e., cancer vs. uterine fibroma, cancer vs. normal endometrial tissue and cancer vs. endometrial polyp. There was only a significantly important difference in tissue MT2 between the groups of patients diagnosed with uterine fibromas and presenting with normal endometrial tissue ($$p \leq 0.019$$). We found no difference in the tissue MT2 concentration between the patients before and after menopause ($$p \leq 0.282$$). Patients’ BMI was found to influence the tissue MT2 concentration, with higher MT2 concentrations found in patients with a greater body weight ($r = 0.01$, $p \leq 0.05$). ## 3.2. Associations between SNPs and Tissue MT2 Concentration We found no associations between prevalence of the selected polymorphisms and MT2 concentration. The most frequent genotype of MT1A among the studied population was GG, with only four patients with the AA genotype. Regarding MT2A, none of the patients were found to have the GG genotype. Specific results are listed in Table 2. ## 3.3. Regression Analysis We performed a univariate logistic regression analysis to assess whether any of the selected genotypes influenced the risk of endometrial cancer. The results of the study showed no influence of MT polymorphism on the endometrial cancer risk (Table 3). As part of the study, we attempted to assess the risk factors for endometrial cancer. The patients’ age (OR 7.26, $$p \leq 0.001$$), menopausal status (OR 14.77, $$p \leq 0.001$$), and diabetes status (OR 16.60, $p \leq 0.0001$) were found to increase the risk of endometrial cancer. However, we did not find any associations between MT2 concentration and EC risk. Specific results are listed in Table 4. ## 4. Discussion For many years, the division of endometrial cancer subtypes was based on the Bokhman classification, dividing the groups of patients into estrogen dependent (type I) and estrogen independent (type II). The Cancer Genome Atlas (TCGA) project has identified a new molecular classification of endometrial cancer, with four distinct prognostic endometrial cancer subtypes based on genomic abnormalities. The classification differentiates POLE mutated, mismatch repair-deficient, p53 abnormal and no specific molecular profile groups of patients. In accordance with the ESGO/ESTRO/ESP guidelines, patients should be classified based on their molecular profiles and FIGO staging to define prognostic risk groups and to decide on the need of additional/adjuvant treatment [21]. With the rising prevalence of endometrial cancer [22], we not only need better prognostic markers but also diagnostic factors that allow for early cancer detection. Some potential biomarkers for endometrial cancer have been identified, including the most common somatic mutations in already well-known tumor-suppressor genes and oncogenes, such as the PTEN, TP53, POLE and KRAS mutations. The utility of screening for endometrial cancer is limited and should only be considered for high-risk populations, e.g., patients with type 2 Lynch Syndrome [23]. Apart for the use of serum biomarkers, the use of a transvaginal ultrasound is possible as it is a reasonably sensitive and specific screening method [24]. As the sensitivity and specificity of the already-known EC diagnostic markers is limited, we decided to study the potential role of metallothioneins in endometrial cancer diagnosis. Metallothioneins were found to participate in carcinogenesis and to have important roles in tumor growth, progression, metastasis and drug resistance. Their effect can be caused by their role in the regulation of cell cycle, proliferation and apoptosis; their participation in multiple cell signaling pathways, zinc homeostasis, tumor cell microenvironment remodeling and cell adhesion and migration; their role in angiogenesis pathways, including VEGF and MMP-9, and their role in the mediation of p53 and NF-kB activity [14]. They are intracellular proteins that are able to form complexes with group IIB metals, thus being able to participate in heavy metal detoxification [25]. Their gene transcription is activated by stress stimuli including metals, glucocorticoids, catecholamines, ROS (reactive oxygen species) and proinflammatory cytokines [26,27]. In humans, four MT isoforms have been identified (MT1, MT2, MT3 and MT4), and they are all located on chromosome 16q13 with functional genes for their isoforms [26,28]. Previous studies have shown the overexpression of MT to be a prognostic marker for tumor progression and drug resistance in, i.a., ovarian [25,29], breast [30], lung [31], renal [32], bladder [33] and oral cancer [34] and melanoma [35]. Yet in some tumors, MT levels are downregulated (i.a., hepatocellular, gastric, colorectal and central nervous system). Due to the involvement of MT in cell proliferation, increased concentrations of MT in cancer patients were discovered to be associated with a poor patient prognosis [28]. Large discrepancies between MT expression have been found between different tumor types, and yet there is no reliable explanation for the associations between MT expression in tumor tissue, patient prognosis nor resistance to treatment [16]. Gumulec et al. conducted a meta-analysis to summarize the evidence of the studies on MT as molecular markers of various types of cancer [15]. The authors analyzed 77 studies that included 8015 tissue samples, revealing a positive correlation between MT expression and head and neck and ovarian tumors. A negative association was found for liver tumors, while no significant associations were found for breast, colorectal, prostate, thyroid, stomach, bladder, kidney, gallbladder and uterine cancer, nor for melanoma. A study on gynecological malignancies by Ioachim et al. [ 36] revealed a statistically significant difference in MT expression between endometrial carcinoma and simple hyperplasia tissues. In patients with carcinomas, the expression correlated positively with histopathological grading and inversely with progesterone receptors. In our study, we decided to evaluate the MT concentration in patients with different uterine diagnoses: patients with normal endometrial tissue, endometrial polyps, uterine myomas and endometrial cancer in patients presenting with abnormal uterine bleeding. Endometrial polyps are caused by the benign hyperplastic growth of endometrial tissue, while uterine fibroids are caused due to an imbalance between the cells’ proliferation rate and their apoptosis [37]. Both of these conditions can be related to hypoestrogenism [37,38]. Endometrial hyperplasia is a condition also associated with increased estrogen; however, it is an estrogen-driven precursor lesion to endometrial endometrioid adenocarcinoma characterized by an increased gland to stroma ratio of >3:1 glandular to stromal elements [39,40,41]. We found no statistically significant differences between MT2 expression in the tissue samples of patients with endometrial cancer and those with benign tissue ($$p \leq 0.579$$). We have also evaluated changes in the MT2 concentration between benign lesions. There was a significant difference in MT2 concentration between uterine fibromas and normal endometrial tissue ($$p \leq 0.019$$). A study by Klimek et al. [ 42] evaluated changes in MT levels during different menstrual cycle phases and revealed that MT expression changes respective to hormonal fluctuations, with the highest observed during the mid-secretory phase and its respective decrease occurring during the early, late secretory and mid-proliferative phases, suggesting a possible role of MT in the protection of endometrial cells against apoptosis. A similar study suggested higher MT expression during phases associated with low circulating progesterone levels [43]. As uterine fibroids are hormone-dependent and estrogen is considered the major mitogenic factor in the uterus [44], the expression of MT in fibroid tissue may be related to the rapid proliferation and presence of hormone receptors in the tissue. Additionally, tumor growth, whether benign or malignant, requires the formation of new blood vessels to obtain sufficient oxygen and nutrition for further growth and progression. Studies have demonstrated an important role of MTs in tumor angiogenesis. A study by Miyashita and Sato showed increased MT1 expression in vascular endothelial cells at the site of angiogenesis, and MT1 downregulation was found to cause the inhibition of cell proliferation and angiogenesis [45]. In our study, we did not ask the patients for any use of hormonal therapy, nor we did not evaluate the progesterone/estrogen receptor status of endometrial cancer tissues. Further studies assessing the use of exogenous sources of estrogen may provide additional insight on the correlation between estrogens and MT status. MTs were also discovered to regulate multiple proteins and transcription factors essential for intracellular signaling pathways, including CuZn-superoxide dismutase, Zn-finger proteins, and proapoptotic proteins (e.g., p53 [17,28,46]). P53 gene mutation resulting in p53 is found in numerous cancers, including endometrial cancer, with a p53-abnormal subgroup of patients with the poorest prognosis among all endometrial cancer patients [47]. Both in vitro and in vivo studies have demonstrated a strong positive correlation between p53 mutation and elevated MT I and MT II concentrations [48]. In our study, due to the limited population of endometrial cancer patients, we did not analyze the p53 mutation status in the endometrial cancer patients. However, further studies on endometrial cancer that assess the correlation between MT concentrations and p53 mutation status should be performed to determine any possible relationships. SNPs can alter gene expression and influence protein activity; thus, it was suggested that genetic variations of MT isoforms may influence cancer susceptibility [49,50,51,52,53,54]. In humans, MTs have four main isoforms (MT1, MT2, MT3 and MT4) and can be further subdivided into functional subisoforms. Although the expression of MTs is not universal in all tumor types, increasing evidence suggests that the differential expression of particular MT isoforms can be utilized for tumor diagnosis and therapy [14]. As a part of this research, we measured the most common isoforms, including MT1A, MT2A and MT1L. MT2A is a MT2 isoform which was found to be associated with an increased risk of prostate [53], laryngeal [51] and breast cancers [52]. MT2A was found to be the most expressed MT isoform in breast tissue, and its expression was found to be positively correlated with histological grading [17]. Its expression was also found to be associated with an increased recurrence rate and poorer survival in patients suffering from ductal breast carcinomas [16,18]. Moreover, studies on breast cancer revealed an inverse correlation between the expression of MTs and progesterone and estrogen receptors [55]. Similar to breast cancer, endometrial cancer is a hormone-dependent malignancy, and unopposed estrogen is one of the major risk factors [56,57]. We decided to check for similarities. From the pathophysiologic perspective of endometrial cancer, a type 1 endometrioid carcinoma is associated with a prolonged elevation of estrogen levels leading to the persistent stimulation and proliferation of the endometrial tissue. The causes of increased estrogen concentration may be correlated with different EC risk factors and include obesity and the use of estrogen-based hormonal therapy, especially when unopposed by progesterone, tamoxifen, ovarian cortical hyperplasia, polycystic ovarian syndrome and the presence of estrogen-producing tumors [58]. A study by Białkowska et al. [ 59] evaluated the gene polymorphisms of MT2A, MMP-2, MMP-7, and MMP-13. The authors did not find any significant association between gene polymorphisms, their relation to serum Zn level, and the occurrence of breast, lung or colon cancer. Other studies have shown an association between MT2A-5A/G SNP and an increased risk of breast cancer [50,52], prostate cancer [53,60] and gastric adenocarcinoma [61]. In prostate cancer, Krześlak et al. [ 60] found a significant association between MT2A SNP and Cd, Zn, Cu and Pb levels, suggesting that SNP polymorphisms may affect MT2A gene expression and be associated with metal accumulation. A study by Nakane et al. [ 62] evaluated the impact of MT gene polymorphisms on the risk of lung cancer. The authors found MT-1A C/A, MT-1B G/A and MT-1F C/T variants to significantly increase the risk of lung cancer. MT-1A polymorphism was also previously found to be associated with an increased risk of oral cancer [54]. In our study, we found no associations between the prevalence of the selected polymorphisms and MT2 concentration. The prevalence of specific genotypes was not associated with metalloestrogen or MT2 concentration. None of the assessed SNPs were associated with an increased EC risk. Our study is one of the first to evaluate the influence of metallothionein 2 and MT gene polymorphism on endometrial cancer risk. Nevertheless, there are some limitations to the study. The population study was limited to 110 consecutive patients treated at the clinic. Initially, 140 patients were admitted to the population sample. However, due to incomplete or missing data, 30 patients were excluded from the final study group. In our study, we decided to evaluate changes in trace metal levels, metallothioneins and their polymorphisms between not only endometrial cancer and normal endometrial tissue but also in different benign uterine pathologies. Due to the limited population of EC patients, we did not divide them based on histopathological or molecular characteristics. Different suppressor genes and oncogenes have been found among different histopathological subgroups of patients (i.e., the PTEN mutation associated with endometrioid endometrial carcinoma and the p53 mutation in serous endometrial carcinoma [63]). Further studies on greater populations are needed to confirm our findings and to determine if there are any differences in MT levels and their polymorphisms between the different subgroups of EC patients. It would be interesting to conduct further studies that would assess both serum and tissue levels of MTs simultaneously to check for any associations and eventual changes in their expression. ## 5. 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--- title: 'Influences of Spatial Accessibility and Service Capacity on the Utilization of Elderly-Care Facilities: A Case Study of the Main Urban Area of Chongqing' authors: - Jinhui Ma - Haijing Huang - Daibin Liu journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048546 doi: 10.3390/ijerph20064730 license: CC BY 4.0 --- # Influences of Spatial Accessibility and Service Capacity on the Utilization of Elderly-Care Facilities: A Case Study of the Main Urban Area of Chongqing ## Abstract With the unprecedented growth of the elderly population in China, elderly-care facilities (ECFs) are in a fast expansion process. However, limited attention has been paid to the imbalance at the actual utilization level of ECFs. This research aims to reveal the spatial inequity of ECFs and to quantitatively examine the effect of accessibility and institutional service capacity on utilization. Taking Chongqing, China, as the study area, we measured the spatial accessibility of different travel modes by the Gaussian Two-Step Floating Catchment Area (G2SFCA) method and investigated distribution differences in spatial accessibility, service capacity, and utilization of ECFs by the Dagum Gini Coefficient and its decomposition. Then, the impact of spatial accessibility and service capacity on the utilization of regional ECFs was quantified by multiscale geographically weighted regression (MGWR). The study findings can be summarized as follows. [ 1] Walking accessibility has the most significant impact on the utilization of ECFs and shows geographic heterogeneity. Developing a pedestrian-oriented network of pathways is essential to enhance the utilization of ECFs. [ 2] Accessibility by driving and bus-riding does not correlate with regional ECFs utilization, and relevant studies cannot rely on them alone for assessing the equity of ECFs. [ 3] In the utilization of ECFs, since the inter-regional difference is more significant than the intra-regional difference, efforts to reduce the overall imbalance should be oriented toward inter-regional variation. The study’s findings will assist national policymakers in developing EFCs to enhance health indicators and quality of life for older adults by prioritizing financing for shortage areas, coordinating ECFs services, and optimizing road systems. ## 1. Introduction Population aging is growing increasingly significant and has become a major social issue worldwide [1]. According to the United Nations report, there were 703 million older persons (aged 65 and over) globally in 2019, with the number expected to double in the following three decades to reach 1.5 billion by 2050 [2]. The growing elderly population poses a huge challenge to social development [3], especially in developing countries [4]. China has the most older adults globally and is also one of the countries with the fastest ageing population [5]. According to the latest 7th Census of China in 2020, the old population (aged 60 and over) reached 264 million, accounting for $18.7\%$ of the total population, a 5.44 percentage point rise from the sixth census [6]. This number is predicted to reach $30\%$ by 2050 [7]. As the population ages, China will continue to face the pressure of long-term balanced population development [8]. The physical condition of the elderly deteriorates with age [9], which increases their demand for elderly-care facilities (ECFs) [10]. Since 2012, China has experienced a tremendous expansion in the number of aged-care facilities, particularly in megacities where ageing is a significant issue [11]. Local governments, such as in Beijing, Shanghai and Sichuan, place a high value on the ageing population [12]. Researchers discovered that, despite increasing service facilities, supply still falls short of meeting demand [13]. By 2020, China’s ECFs were only able to service $3.1\%$ of the elderly population [14]. Due to limited urban space resources and rising demand for ECFs, the inequitable allocation of resources for urban services has increased. In China, the geographical scope of the studies has been uneven. Many studies examined the inequity of service of ECFs in plain cities such as Beijing, Tianjin and Shanghai. However, few examined Chongqing, a mountainous city with a unique road network. With the largest elderly population in China, Chongqing is experiencing severe ageing. The inequality of social service resources for disadvantaged groups has been a primary concern in sustainable urban development [15,16]. Researchers have grown more interested in EFC disparity in recent years [17]. Various methods have been used to assess public service equality, including spatial accessibility, Gini coefficient analysis, and spatial autocorrelation analysis [3]. Spatial accessibility is commonly used to evaluate people’s ease of access to public services at a specific location [18]. The main methods for evaluating the spatial accessibility of public service facilities are the Huff model [19], kernel density method [20], gravity model method [21] and two-step moving search method [22]. Compared with other methods, the two-step moving search method is more accurate and comprehensive in measuring the spatial accessibility of facilities and has been more widely developed and applied [23]. To improve the study’s rationale and application, several academics have expanded on the 2SFCA model by adding distance decay functions [24], variable catchment area [25], traffic mode categorization [26], and inter-service facility competitive effects [27]. The Gini coefficient is a global statistic that measures the income disparity of a country’s or region’s population [28]. It is commonly used to analyze the balance of socioeconomic indicators among various population groups [29], and is applied in various fields, including biology, economics, and transportation [30]. Furthermore, spatial autocorrelation analysis is a valuable method for studying the degree of geographical correlation and dependency of specific variables in analyzing the equity of municipal facility services [31]. Although some studies on the application of the equity method enhancement and assessment have been done, the existing studies still have some limitations. First, there is not only a quantitative imbalance in the supply of, and demand for, ECFs. More seriously, there is also an imbalance in space utilization [10], which has been ignored in prior studies. This imbalance in utilization is mainly reflected through the actual access to ECFs services by regional older adults, i.e., it shows the degree of acceptance of ECFs services. In recent years, there has been an unusual phenomenon in China: older adults in city centres have been waiting in line for ECFs for months or more (named “a bed is hard to acquire” in China), yet in remote suburbs, the opposite is true. This issue suggests that accessibility indicators only may not accurately reflect the equality of the spatial distribution of ECFs. Second, it is generally accepted that studies of spatial accessibility based on quantitative relationships between supply and demand can represent the equality of facility services to some extent. However, few studies have investigated the influence and relationship between spatial accessibility (supply and demand) and utilization (natural choice) in ECFs [32]. Finally, institutional service capability is an essential attribute of ECFs [33] and significantly influences the willingness to choose. It is worth exploring to what extent it affects the utilization of ECFs compared to spatial accessibility. Therefore, the main inquiry questions for this study are:What are the characteristics of the spatial distribution of ECF utilization, accessibility, and service capacity? In what areas do they exhibit significant imbalances?What are the main sources of the overall imbalance in the utilization of ECFs?How do accessibility and service capacity impact ECF utilization? What is the extent of the impact? The purpose of this research is to reveal the spatial inequity of ECFs in Chongqing and to quantitatively examine the effect of accessibility and institutional service capacity on utilization. Furthermore, it provides a solid foundation for national policymakers to rationalize resource allocation for ECF services and optimize the distribution of the ageing population. The research framework for this paper is shown in Figure 1. ## 2.1. Study Area Chongqing, located in the southwest of China’s heartland, is the economic centre of the upper Yangtze River area. With the largest elderly population in China, Chongqing has been entering a state of deep ageing society. According to the Chongqing Statistical Yearbook 2021 [34], Chongqing’s senior population has reached 5.7 million. Chongqing has a $17.8\%$ ageing rate (people over the age of 65), with a $4.1\%$ growth rate, which is $3.6\%$ higher than the national average. Notably, the main urban area is $7\%$ of the city’s area but hosts nearly one-third of the city’s ageing population. This makes the ageing problem in the main urban area of Chongqing even more prominent. Therefore, the scope of this paper focuses on the balanced distribution of elder care facilities resources in the main urban area of Chongqing. The study area includes Yuzhong District, Dadukou District, Jiangbei District, Shapingba District, Jiulongpo District, Nanan District, Beibei District, Yubei District and Banan District (Figure 2). ## 2.2.1. Elderly Population Data To obtain a more accurate spatial analysis, we used the sub-district (jiedao), a form of township-level administrative division in China, as the basic spatial unit to measure the inequity of elderly care institutions in the main urban area of Chongqing. The seventh national census collected data on the senior population in Chongqing’s 161 sub-districts [6]. Some scholars calculate accessibility using the geometric centre of the sub-district or its administrative centre [35]. The method does not consider the heterogeneity of the population distribution within a sub-district and may cause some errors. Therefore, the weighted centre of the elderly population has been used as the population centre of the sub-district in this paper. ## 2.2.2. Elderly-Care Facilities Data To ensure consistency between geographical and demographic data, we extracted information from the Chongqing Civil Affairs Bureau about elderly institutions in 2021 [36]. A total of 356 ECFs were identified in the main urban area of Chongqing during the study period. In the database, each record contains the following information: the name of the institution, the address, the number of elderly beds, the number of senior residents, and the number of people it serves. Table 1 shows the results of the partition statistics. According to the Chinese National Standard “Classification and Evaluation of Aged Care Institutions” [37], ECFs are divided into three categories: large (≥ 300 beds), medium (100–300 beds) and small (<100 beds). First, we determined the spatial latitude and longitude using the Map Coordinate Picker tool based on the addresses of the ECFs. Second, the coordinates were converted into point data using ArcGIS and overlaid with the Chongqing sub-districts layer. Finally, the spatial corrections were carried out by identifying the sub-districts in which the institutions were located. We quantified the utilization of ECFs by the ratio of actual number of elderly people in ECFs to the number of local older adults at the sub-district level. This more accurately reflects the equity of services at the regional level. Furthermore, to a large extent, the quality of services is determined by the availability of skilled healthcare staff [38]. To find out how many care staff provided by ECFs can be assigned to beds in each ECFs, service capacity is represented by the ratio of the number of nursing staffs by the number of beds. ## 2.2.3. Network Data The spatial data of the road network is derived from the Gaode Map and the Open Street Map, which mainly consists of the rail transit network, bus network, and urban roads (Figure 3). According to Yang et al. [ 39], the type of provider and the difference in transport costs can influence the size of the catchment area. Accordingly, based on a sample survey of Chinese older adults [40], the maximum travel time was set at 30, 60, and 90 min for small, medium, and large ECFs, respectively. Following the Road Traffic Safety Law of the People’s Republic of China [41], the standard speed for highways, trunk roads, secondary roads, and other roads is 120, 70, 40, and 30 km/h, respectively. In previous medical studies [42], walking speed was found to be 0.90 to 1.30 m/s for older people, so 3.5 km/h was determined as the average walking speed. Finally, according to the Chongqing Road Traffic Safety Regulations and the design code of the rail transit in Chongqing [43,44], the rail transit and the bus speed were set at 100 km/h and 50 km/h, respectively. ## 2.3. Accessibility Calculation The traditional two-step floating catchment area method (2SFCA) treats supply and demand point interactions by dichotomy [45]. However, this method has two problems: the unreasonable setting of the catchment area, and the homogeneity of weight in the search domain. Researchers have suggested enhancing the two-part moving search technique by using distance decay functions, including kernel density function [46], exponential function [47], power function [48] and Gaussian function [49]. The Gaussian two-step floating catchment area method (G2SFCA) replaces the 0 and 1 in the dichotomous technique with distance decay weights, making the computation more accurate. This study used G2SFCA to evaluate the accessibility of elderly care institutions in Chongqing based on the Gaussian decay function. Step 1: Calculating of the ratio of beds to elderly population in the catchment area of the ECF j:[1]Rjm=Sjm∑k∈dkj≤domDkGdkj,dom where m is the class type of the ECFs, Rjm is the supply-to-demand ratio of the ECF j within the search threshold, Sjm represents the number of beds provided by the ECF j, Dk represent the number of elderly people in the demand centre k, dom represents the the travel cost thresholds for m-type ECFs, dkj is the spatial distance between demand centre k and the ECF j, and Gdkj,dom is Gaussian-weighted distance decay function, which can be expressed as Equation [2]:[2]Gdkj,dom= e−12×dkjdom2−e−121−e−12 if dkj≤dom0 if dkj≤dom Step 2: calculating the spatial accessibility index Ai for demand centre i. The formulation is denoted as Equations [3] and [4]:[3]Aim=∑k∈dkj≤domRjmGdkj,dom [4]Ai=∑$m = 1$Aim where Aim is the spatial accessibility in the demand centre i to the m-type ECF and Ai is the spatial accessibility in the demand centre i to the ECFs, calculated by summing the accessibility of the three types of ECFs. ## 2.4. Inequity Calculation We use the Dagum Gini decomposition method to measure regional differences. This method overcomes the limitations of the traditional Gini coefficient and the Thiel index, and effectively analyzes the causes of regional differences [50]. Additionally, the method accurately decomposes the net interregional difference contribution to the overall regional difference by resolving the cross-over problem between subgroups. The overall Gini coefficient is calculated as follows:[5]G=∑$j = 1$k∑$h = 1$k∑$i = 1$nj∑$r = 1$nhyji−yhr2μn2 where G is the overall Gini coefficient, yjiyhr is the variable value of a sub-district in the district jh, μ is the mean variable value of all sub-districts in the mean urban area of Chongqing, n is the total number of sub-districts, k is the number of districts, and njnh is the number of sub-districts in the district jh. The overall Gini coefficient G can be decomposed into three parts. ( i) Intra-regional difference Gw, i.e., the spatial differences between variable value within districts, which refers to such differences between sub-districts within nine districts in this study. ( ii) Inter-regional difference Gnb, i.e., the differences of variable value between districts, which refers to such differences between nine districts in this study. iii) Intensity of transvariation Gt, i.e., the differences of variable value between districts, which refers to such differences between nine districts in this study, refers to the remainder of the overall Gini coefficient generated by the overlapping effects between regions. The relationship between them satisfies G = Gw + Gnb + Gt. The formula is as follows:[6]Gw=∑$j = 1$kGjjPjSj [7]Gjj=∑$i = 1$nj∑$r = 1$nhyji−yjr2Yj¯nj2 [8]Gnb=∑$j = 2$k∑$h = 2$j−1Gjh(pjsh+phsj)Djh [9]Gjh=∑$i = 1$nj∑$r = 1$nkyji−yjrnjnhYj¯+Yh¯ [10]Gt=∑$j = 2$k∑$h = 1$j−1Gjh(pjsh+phsj)(1−Djh) [11]Djh =djh−pjhdjh+pjh [12]djh=∫0∞dFjy∫0yy−xdFhx; [13]pjh=∫0∞dFhy∫0yy−xdFjx where Pj=nj/n, sj=njYj¯/nY¯. Djh represents the interaction between the variables in regions j and h, djh is the difference in the variables between regions j and h, pjh is the first moment of transvariation, and Fh and Fj are the cumulative density distribution functions of regions j and h respectively. ## 2.5. Regression Model The traditional OLS is a clear and simple linear regression. However, it ignores changes in spatial variables caused by fluctuating spatial locations. Researcher have offered geographically weighted regression (GWR) as an excellent solution to the problem of geographical non-smoothness using regional regression analysis with variable parameters [51]. Unlike the traditional GWR model, the multiscale geographically weighted regression model (MGWR) calculates the bandwidth of the explanatory variables, which improves the accuracy of the regression results [52]. Hence, we use MGWR to investigate the influence of institutional service capability and spatial accessibility on EFC utilization at various subdistrict levels. Its equation is as follows:[14]yi=βbw0ui,vi+∑$k = 1$mβbwkui,vixik+εi where yi is the utilization of EFCs (response variable), bwk is the bandwidth, βbw0ui,vi is the regression intercept of the bw0, βbwkui,vi is the regression coefficients for variable k at optimal bandwidth bwk, xik is the value of the variable xk at observation point i, and εi is the random error term. ## 3.1. Spatial Accessibility of Elderly-Care Facilities by Travel Mode The spatial accessibility of ECFs in different places shows whether older individuals have equal access to ECFs. To answer questions 1 and 2 mentioned in the introduction, we focused on two aspects: the spatial distribution characteristics of accessibility and the quantification of accessibility discrepancies. ## 3.1.1. Spatial Accessibility Distribution Characteristics The accessibility score is the number of ECF beds available to each older adult using a specific method of transportation, and is shown by its colour: redder indicates more accessibility, while yellower indicates lower accessibility. The accessibility distribution varies greatly between modes of transportation. Accessibility to ECFs by rail transit, car, and bus is presented in Figure 4a–c, respectively. High accessibility presents a spatial agglomeration centred on the Yuzhong District. The closer the sub-districts location to Yuzhong District, the higher the accessibility score. Yuzhong District is the centre of the main urban area of Chongqing, and its urban transportation system is significantly more developed than in other areas. Thus, these three modes of transportation to ECFs are poor for older adults in areas outside the city centre, such as Beibei, Banan, and Dadukou. Accessibility to ECFs by walking is similarly highly uneven, as shown in Figure 4d. However, unlike other modes of travel, sub-districts with relatively high accessibility by walking are mainly scattered and dotted. Meanwhile, these sub-districts are distributed far from the city centre. ## 3.1.2. Decomposition of Regional Accessibility Differences Table 2 illustrates the differences in accessibility to the main urban area of Chongqing at a global level based on the Dagum Gini coefficient. Among all modes of transportation, walking accessibility has the largest Gini coefficient, indicating that the spatial distribution of walking accessibility is the most unequal. In terms of intra-regional variation, by comparing Gw, we identified the districts with the most uneven accessibility distributions at the sub-district level. Figure 5a–d shows the Gini coefficients for the main urban areas for rail transit, driving, bus-riding, and walking, respectively. Firstly, in terms of accessibility by rail transit (Figure 5a), Dadukou, Jiangbei, and Shapingba have relatively higher Gini coefficients, indicating that the spatial distribution of accessibility in these areas is relatively more inequitable. Secondly, according to Figure 5b, Ba’nan’s Gini coefficient for accessibility by driving is nearly twice that of the other regions. Then, among the main urban areas, Jiulongpo, Jiangbei, and Beibei have the most inequitable accessibility by bus (Figure 5c). Finally, in Figure 5d, we can see that Dadukou and Jiulongpo have the most uneven spatial distribution of walking accessibility. From the perspective of inter-regional differences, we used Gnb to identify districts with the greatest differences in accessibility between groups. As shown in Figure 6, inter-regional differences in accessibility by rail transit and walking are significantly larger, whereas inter-regional differences in accessibility by driving and bus riding are generally lower. Beibei and Ba’nan differ significantly from other regions in Figure 6a. The most significant inter-regional differences in walking accessibility were found in Yuzhong, Yubei, and Jiulongpo (Figure 6d). Compared to the other areas, these three have a greater density of older residents and are less accessible by walking. ## 3.2.1. Spatial Distribution Characteristics Figure 7a shows the spatial distribution of the service capacity of ECFs in the main urban area of Chongqing. Some sub-districts do not have ECFs, so they are named after Nodata and expressed using slashes. Service capacity (the number of people served per bed) ranges from 0.03 to 0.73, with an unequal spatial distribution. The sub-districts with the highest service capacity resources have 20 times more resources than those with the lowest service capacity resources. Sub-districts closer to the city centre have a higher service capacity in Jiangbei, Jiulongpo and Yubei. However, there are still a few sub-districts remote from the city centre with high service capacity, such as those in northwestern Beibei, central Shapingba, and southern Dadukou. Thus, ECFs away from urban centres should continue to improve their service capacity. Figure 7b shows the spatial distribution of ECFs utilization in the main urban area of Chongqing. Shapingba has the highest space utilization rate of $2.24\%$ among the nine districts, while Jiangbei has the lowest rate of $0.52\%$. Furthermore, $36.7\%$ of the sub-districts with a space utilization rate of less than $1\%$ are distributed mainly around the city centre. Sub-districts with a space utilization rate of $1\%$ to $2\%$ account for $41.3\%$ of the total and are primarily concentrated in Ba’nan. This disparity may be caused by older adults selecting ECFs using a cross-regional approach. Spatial distance is generally considered the most critical factor in selecting an ECF for older adults. However, studies have shown that service capacity is also an influential factor in the choice of ECFs for older people. Figure 7a,b shows that sub-districts with higher space utilization are relatively better served. ## 3.2.2. Decomposition of Regional Differences Table 3 illustrates that overall service capacity and spatial utilization are significantly imbalanced. From the comparison of contribution rates, this imbalance is primarily caused by inter-group differences Gnb and intensity of transvariation Gt. Therefore, identifying and reducing interregional differences is the first step toward ensuring equity in service capacity and spatial use in Chongqing. In addition, identifying and adjusting high imbalances within the region is also essential. Based on the intra-regional differences Gw shown in Figure 8, we can see that the region’s overall service capacity and spatial utilization of sub-districts show significant inequity. In Figure 8a, Ba’nan, Dadukou, and Jiulongpo all have Gw values greater than 0.5. Regarding spatial utilization, the utilization rate of ECFs varies significantly between sub-districts in the Yuzhong, Nan’an and Dadukou regions. The Gw value is exceptionally high in Yuzhong District, which reaches 0.8 (Figure 8b). As is shown in Figure 9, the overall inter-regional differences in service capacity and space utilization are significant, with Gnb ranging from 0.37 to 0.66 and 0.33 to 0.76, respectively. In terms of service capacity, Banan, Yubei, and Jiulongpo have higher Gnb values than the other districts (Figure 9a), so these three districts should be the main targets to decrease inter-regional variability. Space utilization in Yuzhong should receive attention (Figure 9b), as it reflects the most significant variability. ## 3.3. Impact of Accessibility and Service Capacity on the Utilization The MGWR model was used to analyze the overall and local effects of the factors on utilization. Table 4 provides descriptive statistics for the MGWR model regressions, including mean, standard deviation, median, maximum, bandwidth, and p-values. Comparing the t-values with the adjusted t-values ($95\%$) indicates that accessibility by walking and rail transit, as well as service capacity, have a direct impact on the utilization of ECFs. To explore the spatial heterogeneity of the impact of the three factors on utilization rates, we conducted a spatial mapping analysis of the correlation coefficients and p-values for each element in Figure 10. Figure 10a–c shows the correlation coefficients for rail transit, walking, and service capacity, respectively. Redder colors indicate a stronger correlation, while yellower colors indicate a weaker correlation. Figure 10d,e displays the p-values for rail transit, walking, and service capacity, with darker shades indicating statistically significant results. As shown in Figure 10a, accessibility by rail transit significantly affects the utilization of ECFs. Correlation coefficients ranged from 0.34 to 0.42. This suggests that rail transit development may provide older people access to ECFs. In the city centre and southwest area, accessibility by rail transit affects utilization most significantly, with p-values ranging from 0.40 to 0.42 (Figure 10d). Rail transit accessibility in the northeast has a less significant impact on utilization and is less significant. Because rail transit development has been concentrated in the centre and southwest, the northeast has limited access to this resource. Compared with accessibility by rail transit, walking accessibility has a more significant spatial effect on the utilization of ECFs, with coefficients ranging from 0.22 to 1.13 (Figure 10b), decreasing stratified from west to east. In the western region (Yuzhong and Shapingba), walking accessibility positively affects utilization, with coefficients ranging from 0.74 to 1.13. In the central region (Dadukou, Jiangbei, Beibei and Yubei), walking accessibility has a relatively low impact on utilization but is still significant (Figure 10e). In the eastern region (Banan District), walking accessibility has the lowest impact on utilization and is insignificant, with coefficients ranging from 0.22 to 0.39. Additionally, there is geographic heterogeneity in the impact of service capacity on spatial utilization, but the degree of impact is relatively low overall, with coefficients ranging from 0.24 to 0.36 (Figure 10c). In the southwest region (Dadukou, Shapingba, and Jiulongpo districts), service capacity greatly influences spatial utilization. The correlation coefficient and significance level decrease from the southwest to the northeast (Figure 10f). ## 4.1. Principal Findings and Comparison with Other Studies Given the increasing inequality of ECFs, existing studies fail to provide evidence regarding the impact of accessibility and service capacity on the utilization of ECFs. Contrary to previous studies based on POI [45], the data in this study were sourced from the Chongqing government’s latest statistical data on ECFs in 2020, ensuring complete and scientific findings. The findings from this study suggest that accessibility by rail transit and walking, and service capacity, can affect the utilization of ECFs. Globally, walking accessibility has the most significant impact on space utilization, while at the local scale, these impacts are variable. The contribution of inter-regional difference to the utilization of ECFs was more significant than intra-regional difference and was the main source of overall difference. First, walking accessibility has the most significant impact on space utilization. Our findings accord with recent studies that travel by walking and bicycling was associated with more frequent primary care visits than car travel [53]. Since Chongqing is a mountainous city resulting in inconvenient cycling for the elderly, bicycling was not considered in this study. In contrast to primary care, our research focuses on ECFs, which provide more complete services for the elderly. In addition, we used the MGWR method to generate correlation results not only at the global level but also at the local level through spatial mapping. At the local level, we found that the higher the population density of the region, the lower the walking accessibility. This is consistent with the results of Zhang et al. [ 22]. Although the road system in the city centre is more developed than in other areas, accessibility by walking is relatively low. The reason for this is the extremely high density of elderly people in the city center (Figure 3). It is estimated that the population density in Yuzhong District is 30 times higher than that in the other regions (southeast of Banan District). Despite the huge number of ECFs clustered in city centers, per-person service resources remain limited. These areas have better public transport networks, leading to the possibility that other modes of travel (such as bus-riding and rail transit) may be more popular than walking. In these areas, walkability significantly impacts space utilization, with p-values less than 0.01. This indicates that walkability has a more significant impact on space utilization in densely populated areas. Previous studies have suggested that walking is the most popular mode of transportation for older adults, and benefits their overall health and well-being [17,54,55]. Furthermore, a survey of older adults in Chongqing showed that $67.3\%$ walked to travel, and $78.9\%$ travelled within a two-kilometre radius [56]. Walking is the primary mode of transportation for older people in other regions as well [22]. Because of their physiological condition, older people have a low level of mobility and a limited range of trips. According to research, older adults’ satisfaction with care services decreases as walking distance increases [57]. Thus, as a safe and affordable method of transportation, walking is especially suitable for older adults [58]. In summary, choosing an ECF closer to home can enhance older adults’ sense of belonging and well-being. Older adults prefer ECFs that are located within walking distance. Second, accessibility by rail transit and service capacity can also affect the utilization of ECFs, although less significantly than walking accessibility. Spatial distance is generally considered the most critical factor in selecting an ECF for older adults. However, studies have shown that service capacity is also an influential factor in the choice of ECFs for older people [59]. Through Figure 7a,b, it is apparent that sub-districts with higher space utilization are relatively better served. Our findings accord with recent studies indicating that distance to home, care services, and rail accessibility are all significant factors influencing older adults’ choices of ECFs [10]. According to Habib et al. [ 60], older adults are more likely to travel by public transportation if the system is reliable, convenient, and comfortable. In Chongqing, a mountain city with high heat and humidity, the relatively poor bus infrastructure (congested roads, slow speeds, and lack of seats and covers at bus stops) could discourage older adults from travelling to ECFs [61]. In recent years, the rail transit system has developed rapidly, offering increased safety, convenience and comfort over bus-riding. This gives older adults a new travel option when considering ECFs. Since service capacity is an effective guarantee of the quality of life for older adults in ECFs [62], improved service facilities can significantly enhance the use of these facilities. Third, the inter-regional differences between regions of the explanatory and explained variables are significantly more significant than the intra-regional differences. In previous studies, the Gini coefficient has been used to measure the equity of public transportation [30] and various types of nursing homes [3]. However, this only explains the overall level of inequity. Using the Gagum Gini coefficient, we identify areas of imbalance and the main sources of overall differences. Across the nine districts, we discovered various degrees of intra-regional and inter-regional differences, and this variation was intuitively reflected in the coefficient values. For districts with more pronounced intra-regional differences, we can reduce the imbalance directly at the sub-district level, such as the walking accessibility of Jiulongpo District (Figure 6d). For districts with more apparent inter-regional differences, resources can be rationally allocated across districts, such as the service capacity between Yubei-Banan districts (Figure 9a). This method provides a measure of equity identification at different levels. Somewhat surprisingly, accessibility by driving and bus-riding was not significantly associated with the utilization of ECFs at the global level. While previous research has focused on improving accessibility methods [63] and fairness assessments [45] for ECFs in driving mode, our findings demonstrate that these conclusions may not be fully applicable in practice since this mode does not contribute to utilization. As we know, only older adults with cars can use this mode of transportation [64]. Unlike some developed countries, older adults rely on cars for transportation [65]. In developing countries, older adults have fewer transportation options, especially with low car ownership, which increases their transportation barriers [66]. In these countries, public transport networks (such as bus-riding and rail transit) are at the core of urban public facilities. This may overlook the fact that older people travel predominantly by walking, thus undermining their access to public health services. As a recent study from a medium to low-income country has shown, the accessibility to non-communicable disease services decreased as age increased [67]. Furthermore, transportation academics regard older adults as a “transportation disadvantaged group” [68], with cognitive and functional decline eliminating driving alternatives for most elderly adults [69]. Accordingly, older adults in middle-income countries do not travel by car as their primary mode of transportation [53]. Travel by bus is relatively crowded and slow, making it difficult for elders to travel safely, particularly to distant ECFs. Thus, most older adults may not choose these two modes of transportation to ECFs. These theories are logically consistent with our findings that accessibility by driving and bus-riding had no significant impact on the utilization of ECFs. ## 4.2. Limitations We investigated the spatial differentiation characteristics of the equity of ECFs in the main urban area of Chongqing, identified the main sources of inequity, and explored the effects of transportation accessibility and service capacity on utilization. However, there are limitations to our study. [ 1] Our study’s explanatory variables included many factors (transportation network, type of institution, spatial location, travel time, population, beds, and attendants). Nevertheless, income, air quality and other uncertain information, such as subjective preferences of older adults in different age groups, were not considered. In the future, these data can be supplemented and improved with government guidance, and their impact on space utilization can be further investigated by overlaying income data and air quality. [ 2] In addition to the number of people served to the number of beds ratio, service capacity also includes the quality of meals and the quantity of other equipment, such as televisions and air conditioners. Due to the large sample size, these data are not currently available. Future research could develop more quantitative statistics on these elements and use more comprehensive service metrics to evaluate the quality of ECF services. This will facilitate a more accurate analysis of its contribution to utilization. ## 4.3. Implications At the local level, sub-districts that need to be improved can be divided into two categories: without ECFs and with low ECFs utilization (Figure 7 shows that $25\%$ of the sub-districts in the main urban area of Chongqing do not have ECFs). For sub-districts with low utilization of ECFs, the cost of enhancing rail transit construction is high. Therefore, for these older ECFs, improving the pedestrian system should be the first priority, followed by increasing service capacity. For sub-districts without ECFs, new projects should be located near sites with convenient accessibility by walking and rail transit. For example, in Chongqing, ECFs should be located within 2000 m of residential communities to ensure efficient operation. At the global level, the government should focus on reducing regional differences and provide policy and economic support to areas with low spatial utilization of ECFs. This will ensure overall equity. In addition, separate strategies should be developed for different districts, with guided improvement measures to reduce the waste of urban public resources. For example, in areas with limited transportation options, the government could plan senior-specific bus routes and urban bus rapid transit for ECFs. Future studies on the utilization of ECFs should integrate multiple transportation modes while giving sufficient weight to walking and rail travel. Furthermore, in this study, accessibility by bus and driving had no effect on the utilization of ECFs. Therefore, a single mode of travel by bus or car should be carefully considered in studies assessing the equity of ECFs in similar cities, especially methodological improvement studies. Finally, due to geographical variances, the effects of the same factor on fairness may differ between locations. Accordingly, future research on the utilization of ECFs for more geographical regions should be done. ## 5. Conclusions Our findings are as follows. [ 1] Overall, walking accessibility has the most significant impact on the utilization of ECFs, and there is spatial heterogeneity in this significance. Building a pedestrian-friendly pathway system is critical for improving ECF utilization. [ 2] Regarding the utilization of ECFs, the inter-regional difference is significantly higher than the intra-regional difference and is the primary source of overall difference. Eliminating overall differences must consider the sensible deployment of resources across districts. [ 3] Accessibility by driving and bus-riding does not correlate with the utilization of ECFs. Therefore, relying on them alone to evaluate the service equity of ECFs is inadequate, and this should be avoided in future studies. We emphasize the actual utilization of ECFs as a more equitable criterion than single accessibility. As the elderly population continues to grow, it is increasingly urgent to eliminate inequalities in services for ECFs. Therefore, it is imperative to develop separate strategies for unbalanced areas, with policy and economic support. 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--- title: 'Effects of Microfiltered Seawater Intake and Variable Resistance Training on Strength, Bone Health, Body Composition, and Quality of Life in Older Women: A 32-Week Randomized, Double-Blinded, Placebo-Controlled Trial' authors: - Alvaro Juesas - Pedro Gargallo - Javier Gene-Morales - Carlos Babiloni-López - Angel Saez-Berlanga - Pablo Jiménez-Martínez - Jose Casaña - Josep C. Benitez-Martinez - Rodrigo Ramirez-Campillo - Ivan Chulvi-Medrano - Juan C. Colado journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048547 doi: 10.3390/ijerph20064700 license: CC BY 4.0 --- # Effects of Microfiltered Seawater Intake and Variable Resistance Training on Strength, Bone Health, Body Composition, and Quality of Life in Older Women: A 32-Week Randomized, Double-Blinded, Placebo-Controlled Trial ## Abstract The aim was to explore the effects of a 32-week resistance training (RT) intervention with elastic bands with or without microfiltered seawater (SW) supplementation on isokinetic strength, bone mineral density (BMD), body composition, and subjective quality of life in postmenopausal women. Ninety-three untrained women (age: 70.00 ± 6.26 years; body mass index: 22.05 ± 3.20 kg/m2; body fat: 37.77 ± $6.38\%$; 6.66 ± 1.01 s up-and-go test) voluntarily participated in this randomized, double-blinded, controlled trial. Participants were allocated into four groups (RT+SW, RT+PLA, CON+SW, and CON+PLA). The RT intervention (twice weekly) consisted of different exercises for the whole body performed at submaximal intensities with elastic bands. Both control groups were not involved in any exercise program. A two-way mixed analysis of variance of repeated measures revealed significant improvements in almost all the variables in both intervention groups ($p \leq 0.05$). However, significant differences with controls were encountered in isokinetic strength, body fat percentage, and bodily pain. Although the group with SW supplementation obtained greater effect sizes, non-significant differences between both RT groups were observed. In conclusion, the determinant factor of the adaptations seems to be RT rather than SW. ## 1. Introduction The management of healthy aging represents an important therapeutic concern for public health and governments [1]. Aging is associated with osteopenia, sarcopenia, overweight, and dynapenia, which increase the risk of functional dependence and reduce the quality of life [2,3]. In this regard, bone health has been proposed as a critical factor in the senescence process due to the reported higher risk of falls, fractures, and mortality in older adults [4]. Previous research has elucidated different procedures for assessing bone quality and health, such as magnetic resonance imaging (MRI), dual-energy, X-ray absorptiometry (DXA), or bone resorption and ossification biomarkers [5,6]. In this sense, although bone mineral density (BMD) assessment by DXA is claimed as the gold standard, this evaluation only provides partial data about bone strength and properties [5]. Thus, biomarkers such as procollagen type I N propeptide (P1NP) and cross-linked C-telopeptides of type I collagen/1000 (BCTX/1000) have been documented as representative measures of bone’s architecture and remodeling rates [7]. Dietary supplements are legal, free-sale nutritional complements that in conjunction with a healthy diet can improve well-being and/or sports performance [8,9]. In this concern, resistance training (RT) and specific nutritional supplementation (e.g., calcium, vitamin D, creatine, and magnesium) have elicited positive results as non-pharmacological strategies to prevent and treat the abovementioned long-term conditions [10,11,12,13,14]. Previous studies have documented the direct benefits of liquid mineral-enriched supplementation, such as seawater (SW) on human health (e.g., immunological and gastrointestinal) [15,16,17] and performance [18,19]. SW has been mostly studied in aerobic-based sports, as it is depicted in a recent systematic review [20]. SW supplementation provokes an ergogenic effect on performance outcomes such as endurance muscle ability [21], incremental running testing [22], and high-intensity intermittent running [19]. Consistently, it has been hypothesized that SW may lower lactate concentrations [22,23] and increase the recovery status after exhaustive endurance tasks [21]. However, despite the promising results of acute endurance exercise, little is known about the chronic effects of SW administration while following a RT program (e.g., weight machines or elastic bands [EB]). In addition, the current knowledge of SW effects in non-athletic populations (e.g., older women) and bone health is scarce. RT with EB has shown positive acute and chronic adaptations in different population groups, including older women [24,25,26,27,28]. One of the primary concerns during RT bouts is appropriate hydration [29]. In this regard, exhaustive efforts may induce a hypohydration state due to reductions in total body water volume and the increase of extracellular fluid osmolality [30]. As previously mentioned, the intake of water and mineral-enriched supplementation has been reported to restore normal osmolality [31], especially in endurance sports [21]. However, no previous study has investigated the potential beneficial effects that mineral-enriched supplementation, such as SW, before or during RT bouts, may have in long-term adaptations (e.g., body composition, bone health, strength, perceived quality of life). Therefore, this study aimed to analyze the effects of a mineral-enriched supplement (i.e., microfiltered SW) and 32 weeks of variable RT (i.e., EB) on isokinetic muscle strength (hip adduction, knee flexion, and elbow flexion at 60 and 180°/s), bone health biomarkers (global, hip, and spine bone mineral density, P1NP, BCTX/1000), body composition (fat and muscle mass), and quality of life (SF-36) in older women (>65 years). It was hypothesized that a 32-week variable (i.e., EB) RT program would increase muscle strength, bone markers, and body composition, with better results when participants were supplemented with SW. Moreover, considering that the participants supplemented with SW would improve the aforementioned parameters, we expected to find an improved subjective quality of life in those participants. ## 2.1. Study Design This study pertains to a larger research project aimed at exploring the effects of different RT intensities on blood biomarkers and muscular strength. We used a 32-week prospective, randomized, double-blinded, controlled trial design following the Consolidated Standards of Reporting Trials (CONSORT) (Supplementary Materials Table S1). Four study groups were formed (RT+SW, RT+PLA, CON+SW, and CON+PLA). All the participants provided informed consent and were free to withdraw from the study at any time. We applied all procedures following the tenets of the Declaration of Helsinki. The experimental protocols were authorized by the Ethics Committee of the University of Valencia (Valencia, Spain) (H1414072784009). We conducted the procedures in different Municipal Activity Centers for Older People in Valencia (Spain) and measurements in the Sports Performance Laboratory of the Faculty of Physical Activity and Sports Sciences of the University of Valencia (Valencia, Spain) and University Hospital Dr. Peset (Valencia, Spain). ## 2.2. Participants We recruited participants with an advertisement that was publicly posted at several Municipal Activity Centers for Older People in Valencia (Spain). The inclusion criteria were as follows: (i) women aged ≥ 65 years; (ii) able to climb 10 stairs without pause and walk 100 m without a walker; (iii) score in the mini-mental state examination (MMSE) > 23 points [32]; (iv) less than one hour of physical activity or physical exercise per week throughout the six months before the start of the study. Participants who had suffered any musculoskeletal, cardiovascular, hepatic, renal, pulmonary, neurological, or neuromuscular injury or disorder and/or were taking any type of drug/supplement that may alter the results of the study (e.g., vitamin C, vitamin E, estrogens, beta-blockers, calcitonin, steroid hormones) were excluded. A total of 160 Caucasian women attended the recruitment calls, of which 51 were discarded. Of these 51 excluded women, 19 refused to participate upon receiving a detailed description of the commitments of the study, and 32 did not meet the inclusion criteria (Parkinson’s disease, $$n = 3$$; multiple sclerosis, $$n = 2$$; ongoing treatment with specific medications (diuretic, $$n = 4$$; hormone replacement therapy, $$n = 5$$; corticosteroids, $$n = 6$$); age below 60 years, $$n = 2$$; score in the mini-mental state examination below 23 points, $$n = 1$$; engagement in regular strength training, $$n = 2$$; plans to leave the area during the intervention for a long period, $$n = 1$$; inability to commit due to scheduling conflicts and time constraints, $$n = 6$$). Therefore, an independent staff member not involved in the trial or any screening, testing, training procedures, or contact with the participants randomized the 109 women into the four groups, using a computer-generated random permutation procedure. ## 2.3.1. Intervention Protocol Both control groups (CON+SW, CON+PLA) did not participate in any exercise program. Both intervention groups (RT+SW, RT+PLA) participated in two weekly sessions of 55–60 min on non-consecutive days (separated by 48–72 h) for 32 weeks. Each session was performed in a group, and the individuals always performed the exercises in the same order, alternating between the lower and upper limbs to reduce fatigue [33]. A metronome indicated the speed of execution (2 s each of concentric and eccentric contraction) during the whole session. Likewise, the loads were modified (adapting the color and width of the grip) each week to maintain the appropriate training intensities. Two different intensities were used: (i) high intensity (six submaximal repetitions equivalent to $85\%$ of 1RM); (ii) moderate intensity (15 submaximal repetitions equivalent to 65–$70\%$ of 1RM). The level of perceived exertion at the end of each set for both intensities on the OMNI-RES EB scale [34] progressed from 6–7 (“somewhat hard”) in the first four weeks to 8–9 (“hard”) during the last 28 weeks. The participants performed 3 sets per exercise throughout the first 8 weeks, which were increased to 4 for the remaining 24 weeks [35]. Between sets, an active rest (coordination and cognitive tasks) [36] of 120 s was allowed throughout the whole intervention. Between exercises, a 90 s passive rest was allowed throughout the first 16 weeks and the last 8 weeks. From week 17 to week 24, the passive rest time was reduced to 60 s. The participants performed lower and upper extremity exercises during the first 24 weeks. For the last 8 weeks, the exercises were combined in supersets. No pause was allowed between both exercises of the superset. During the first 24 weeks, the participants performed the exercises in the following order: elbow flexion, squat, upright row, lunge, incline row, and standing hip abduction. The order of the supersets for the last 8 weeks was: standing hip abduction + squats, pushups + incline row, and lunges + upright row. ## 2.3.2. Initial Assessment and Familiarization The participants completed two familiarization sessions to learn exercise techniques [37] and select the width of the EB grip for each exercise according to prior studies [38]. For such purpose, volunteers performed sets of 6 and 15 repetitions with an EB (Theraband, Hygenic Corporation, Akron, OH, USA; five colors in ascending order of resistance/ thickness: green, blue, black, silver, and gold) at different grip widths. These efforts showed the participants what were low and maximal values (1 to 9) in the OMNI-Resistance exercise scale of perceived exertion with the EB [34]. The bands presented a mark every 3 cm to measure and record the increase or reduction in intensity. We measured height and body mass with a portable stadiometer (Seca T214, Hamburg, Germany; precision 0.01 cm), and a digital scale (Tanita® BF-350, Tanita Corp., Tokyo, Japan; precision 0.01 kg) following Calatayud et al. [ 39] protocol. We used DXA (QDR® Hologic Discovery Wi, Hologic Inc., Waltham, MA, USA) equipped with APEX software (version 12.4, APEX Corp., Waltham, MA, USA) to examine body composition (muscle and fat mass), anteroposterior lumbar spine (segments L1–L4), non-dominant proximal femur (total hip), and global bone mineral density. We instructed the participants to control hydration and diet before the DXA measurements to avoid potential influences on the outcomes. The protocol was followed according to Carnevale et al. [ 40]. The same certified researcher carried out all the measurements. ## 2.3.3. Supplementation Protocol The microfiltered SW and placebo supplements used were supplied by Quinton (Laboratories Quinton International, S.L., Alicante, Spain). Participants drank a 20 mL sample just before each session. Composition of this nutritional supplement was as follows: (i) sodium: 11.87 g L−1; (ii) chloride: 20.36 g L−1; (iii) magnesium: 1.36 g L−1; (iv) calcium: 433 mg L−1; (v) potassium: 441 mg L−1; (vi) bicarbonate: 148 mg L−1; (vii) zinc: 11.8 μg L−1; (viii): manganese: 116.9 μg L−1; (ix) cupper: 6.6 μg L−1. Furthermore, the nutritional supplement included other chemical elements: proteins, lipids, water-soluble vitamins D-biotin, thiamine, riboflavin, nicotinamide, cyanocobalamin, pyridoxine, and fat-soluble vitamins retinal, vitamin D3, α-tocopherol and vitamin K1, naturally present in seawater in trace quantities. Placebo composition included only water. This product has neither contraindications nor incompatibilities and does not cause side reactions. A blinded researcher distributed the placebo samples with the same appearance. ## 2.4. Strength Assessment We used a multi-joint isokinetic dynamometer (Biodex Medical TM, Shirley, NY, USA), with the software Advantage (version 3.2, Biodex System Advantage, Shirley, NY, USA) to measure isokinetic strength [41]. We retrieved maximal strength in hip adduction and knee and elbow flexion at angular speeds of 180 and 60°/s since they are the ideal speeds to verify power/function and maximum force, respectively [42]. The participants performed all three exercises in random order and rested for two minutes between exercises. Two trials (one at each angular velocity) consisting of five maximal voluntary contractions on the dominant side were conducted for each exercise. Each exercise was always evaluated first at an angular velocity of 180°/s, followed by the same exercise at 60°/s. A rest of one minute was allowed between the trial at each angular velocity. We used the best maximum concentric isokinetic torque from the five repetitions for analyses. The knee extension range of movement was from 5 to 90°, the elbow flexion was from 15 to 75°, and the hip adduction was from 5 to 45° [43]. ## 2.5. Physiological Parameters We used serum sample separation to analyze the set of physiological parameters (i.e., P1NP and BCTX/1000). After participants fasted for 12 h, a qualified nurse drew 10 mL whole blood samples from an antecubital vein of the participants in a seated position. Blood samples were extracted into dry 10 mL tubes with a silicone gel separator and coagulation activator between 8:00 and 10:00 a.m. (to minimize circadian effects). These samples were kept in a refrigerator at 2–4°C until they were processed, which always occurred within 4 h of extraction. After clot retraction (15–30 min at room temperature), samples were centrifuged with Histopaque (Sigma H-1077) at 3500 rpm for five minutes at 4°C in a Rotina 380R Hettich centrifuge (Tuttlinger, Germany). The professional in charge pipetted and aliquoted the resulting serum supernatant. The aliquots were frozen at −80°C until use. An automated Roche ECLIA system (Cobas 6000, Roche Diagnostics, Mannheim, Germany) measured serum P1NP and BCTX/1000. The person in charge ran the samples in duplicate as per the manufacturer’s instructions to ensure the reliability of the measurements. If the results differed by more than $15\%$, the analysis was repeated. We used the average of both readings for data analysis. ## 2.6. Quality of Life Assessment With the Short Form Health Survey (SF-36) we evaluated physical, psychological, and social well-being. This tool consists of 36 items arranged in eight dimensions that assess positive and negative states of health (general health, physical functioning, physical role, bodily pain, emotional role, social function, vitality, and mental health). For each dimension, the items are coded, aggregated, and transformed into a scale ranging from zero (worst state of health) to 100 (best state of health). A score is achieved for each dimension, as the SF-36 has not been shaped to generate an overall score [44]. Previous research has demonstrated its usefulness and reliability in older adults [45]. ## 2.7. Statistical Analyses We determined the sample size with an a priori analysis conducted with G* Power 3.1 software [46] to reduce the probability of type II error [47]. The calculation based on the study design (F-tests, ANOVA: repeated measures, within–between interaction) indicated a sample size of 72 volunteers to meet a statistical power of 0.80, α = 0.05, a correlation coefficient of 0.5, a non-sphericity correction of 1, and an effect size (ES) of 0.35. We selected the ES according to the average outcomes of all the dependent variables as obtained in the pilot studies. We used commercial software IBM SPSS (version 26.0; IBM Corp., Armonk, NY, USA) to perform the rest of the analyses based on the principle of the intention to treat. Results are reported as mean and standard deviation (SD). We uniformly set the level of statistical significance at $p \leq 0.05.$ We checked the normality of data distribution using the Kolmogorov–Smirnov test. We transformed the non-normal variables, first, into a percentile rank and, second, into a normally distributed variable through the inverse normal [48]. Therefore, we carried out a two-way mixed analysis of variance (ANOVA) of repeated measures to determine the influence of each group (RT+SW, RT+PLA, CON+SW, CON+PLA) and time (pre- and post-test) on isokinetic strength, BMD, blood markers, body composition, and quality of life. The eta partial squared (ηp²) served to evaluate the ES, with 0.01 < ηp² < 0.06 constituting a small effect, 0.06 ≤ ηp² ≤ 0.14 a medium effect, and ηp² > 0.14 a large effect. Planned pairwise comparisons were conducted using the Bonferroni post hoc correction to test for differences. We used Cohen’s d to calculate the ES of the post hoc comparisons, which was interpreted as a trivial (<0.20), small (0.20–0.49), moderate (0.50–0.79), or large effect (≥0.80) [49]. ## 3.1. Participants Details of the participant flow through the study are displayed in Figure 1. Ninety-three untrained older women were randomly assigned into four groups: (i) resistance training with deep seawater supplementation (RT+SW; $$n = 35$$); (ii) resistance training with placebo supplementation (RT+PLA; $$n = 35$$); (iii) control group (no exercise) with deep seawater supplementation (CON+SW; $$n = 11$$); (iv) control group (no exercise) with placebo supplementation (CON+PLA, $$n = 12$$). The baseline characteristics of the subjects are presented in Table 1. At baseline, the age, anthropometric characteristics, and TUG performance did not differ between the intervention groups ($p \leq 0.05$, ηp2 < 0.06). Of the 109 women definitively randomized into the four groups, 93 started the intervention and 77 completed the 32-week intervention (dropout rate of $17.2\%$). At the end of the training program (Week 32), the attendance rate was approximately $75\%$. ## 3.2. Strength The ANOVA testing (see Table 2) showed a significant effect of time on knee and elbow flexion at both speeds. Additionally, the interaction group x time showed a significant effect on all the isokinetic strength variables. Table 3 presents descriptive and inferential analyses performed on the isokinetic strength variables. While both resistance training groups significantly improved all the variables (greater ES for RT+SW), both control groups presented non-significant variations. The post hoc between-group comparison (Supplementary Materials Table S2) revealed non-significant differences between RT+SW and RT+PLA. On the other hand, both resistance training groups presented significantly greater levels of post-intervention isokinetic strength levels compared to both control groups. ## 3.3. Bone Health Table 4 shows the results of the ANOVA performed on the bone health parameters assessed. A significant effect of time was observed on hip BMD and P1NP. Furthermore, the interaction group x time showed significant effects on all the bone health parameters. The post hoc between-group comparisons (Supplementary Materials Table S3) showed non-significant differences between groups. As can be seen in Table 5, while RT+SW significantly improved all the variables, RT+PLA only improved hip BMD, P1NP, and BCTX/1000, and both control groups presented non-significant variations in all the bone markers. ## 3.4. Body Composition Table 6 depicts the effects of the factor time and the interaction group x time on body composition. It is worth mentioning that only muscle mass and fat percentage were influenced by both factors. Table 7 presents descriptive and inferential analyses performed on body composition parameters. While RT+SW significantly improved all the variables, RT+PLA only improved muscle mass and body fat percentage, and both control groups presented non-significant variations in body composition. The post hoc between-group analysis (Supplementary Materials Table S4) revealed non-significant differences between the study groups apart from those presented in the table. ## 3.5. Quality of Life The ANOVA performed on SF-36 (Table 8) showed a significant effect of time on general health, bodily pain, emotional role, vitality, and mental health. On the other hand, the interaction group x time only showed significant effects on vitality. Table 9 exhibits the eight dimensions covered by the SF-36 questionnaire. While both resistance training groups significantly improved almost all the variables (greater ES for RT+SW), both control groups presented non-significant variations. The post hoc between-group comparison (Supplementary Materials Table S5) showed non-significant between-group differences apart from those presented in the table. ## 4. Discussion This study aimed to explore the effects of a mineral-enriched supplement and 32 weeks of a RT intervention with EB on isokinetic muscle strength (hip adduction, knee flexion, and elbow flexion at 60 and 180°/s), bone markers (global BMD, hip BMD, spine BMD, P1NP, and BCTX/1000), body composition (fat and muscle mass), and quality of life (SF-36). The main finding of the present study was that a RT program with EB and SW supplementation over 32 weeks improved all the analyzed parameters of strength, bone health, body composition, and almost all the quality-of-life parameters. While non-significant between-group differences existed in the baseline measurements, significant differences in the post-intervention measurements were observed between both RT groups and control groups in isokinetic strength, body fat percentage, and bodily pain. Non-significant differences existed in post-test measures between both intervention groups (RT+SW vs. RT+PLA), although the RT+SW group presented greater ES. Furthermore, the RT+PLA group did not improve hip adduction strength at 180°/s, global and spine BMD, fat mass, subjective physical functioning, and physical role. Considering the non-significant between-group differences and that the RT+SW group improved all the aforementioned parameters, we could not confirm the study hypothesis. ## 4.1. Strength Adaptations Long-term RT programs increase lower and upper limb strength in older adults [50,51]. However, the loss of essential minerals (Na, Ca, K, and Mg) caused by fluid depletion during physical exercise may hinder RT performance [52,53]. The intake of SW before RT showed greater ES in almost all the variables of isokinetic strength compared to not drinking SW before RT, although non-significant between-group differences existed. Thus, the ingestion of SW may be able to counteract exercise-induced muscle damage and reinforce the antioxidant ability against oxidative stress [53,54]. The non-significant variations of the CON+SW group are in accordance with previous long-term studies that used mineral supplementation (i.e., magnesium) without a RT intervention and did not show significant effects on isokinetic muscle strength [55,56]. Additionally, calcium supplementation added to RT did not elicit a better improvement in isokinetic leg flexion and extension [57]. Therefore, our study reinforces the hypothesis that RT may be plausibly the determining factor in the development of lower and upper limb strength [58,59]. In this sense, according to previous research, EB training is a safe, portable, effective, progressive overload methodology that can be used everywhere and at any time for increasing muscle mass and strength [27,60]. ## 4.2. Bone Health Regarding bone health, postmenopausal women express an upper bone turnover and a higher rate of trabecular bone loss, mainly in the vertebrae, caused by estrogen deficiency after menopause [61]. Indeed, bone resorption in this population increases by up to $90\%$, while bone formation increases only up to $45\%$, as analyzed previously by markers of resorption (BCTX/1000) and bone formation (P1NP) [61]. In our study, non-significant differences appeared between the study groups. However, we found a significant increase in bone formation (P1NP) and a significant decrease in bone resorption (BCTX/1000) in both intervention groups (RT+SW and RT+PLA). Regarding BMD, the intake of SW in the RT+SW group elicited a significant improvement in global BMD, hip BMD, and spine BMD. The intervention group that did not take the supplement (RT+PLA) only reached significant improvements in hip BMD. Within this context, previous research has detailed the role of magnesium in the prevention and treatment of osteoporosis. Rude et al. [ 62] reported an increment of $140\%$ of interleukin-1 (IL-1) in comparison to a placebo condition after three days of magnesium depletion in rats’ osteoclasts. Moreover, SW administration in rats led to a rise in osteogenesis rates due to the upregulation in osteoblast differentiation [63]. Orchard et al. [ 64] reported a relationship between a low magnesium intake and the reduction of hip and total body BMD in postmenopausal older women. In this sense, participants with intakes below 206.5 mg/day showed a $3\%$ reduction in magnesium levels compared to participants with intakes higher than 422.5 mg/day. Aligned with previous literature [65,66], we found a moderate positive effect of the RT intervention on bone resorption, although significant between-group differences did not exist. In this regard, the impact of RT on BMD has been categorized according to different intensities of the 1RM in postmenopausal women [67]. In this sense, moderate and high-intensity zones (i.e., >$70\%$ RM) exhibited greater benefits on bone health and BMD. For this reason, the results found in this study for global, hip, and spine BMD may be explained by the selected intensity in both intervention groups. ## 4.3. Body Composition Abdominal obesity and low muscular strength levels are linked to a higher risk of hospitalization and dependence [68]. In this sense, RT is crucial to prevent and revert frailty in community-dwelling older people [69]. While the intake of mineral-enriched supplements alone has not shown a significant impact on body composition [55,70], RT is a crucial variable for that purpose [71]. In our study, non-significant differences appeared between the study groups. However, both RT groups presented significant improvements in body composition, except the RT+PLA group, which did not improve fat mass (kg). A plausible explanation for the small ES encountered could be the lower concentrations of magnesium and calcium in SW (magnesium: 26 mg; calcium: 8 mg) compared to other studies (magnesium: 250 mg; calcium: 1200 mg) [55,70]. ## 4.4. Quality of Life Improving the quality of life is the cornerstone in most older adult interventions [72]. Almost all SF-36 questionnaire parameters improved in both RT groups with greater ES for the RT+SW group. However, significant between-group differences only appeared in bodily pain. Concerning the relationship between quality of life and supplement intake, the current literature is ambiguous and depends on the active principle analyzed [73,74,75,76]. In this regard, RT is a major predictor for improving the quality of life in older adults [77]. However, although the relationship between biopsychosocial factors that influence pain is widely studied [78], further studies are needed to elucidate the exact influence of SW on chronic pain in the dimensions of the SF-36 questionnaire retrieved. ## 4.5. Limitations Despite the novel findings presented, the methodology carried out in this study does not show measurements of the acute effects that SW could have on specific parameters of RT. Whether variables such as levels of acute fatigue and one-repetition maximum vary when participants drink SW just before or during RT is still unknown. Therefore, further investigation of this mineral-enriched supplement is needed. ## 5. Conclusions An EB RT program with SW supplementation significantly improves isokinetic strength compared to controls. Similar results were obtained in the RT+PLA group. 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--- title: Transdermal Delivery of Phloretin by Gallic Acid Microparticles authors: - Roberta Cassano - Federica Curcio - Roberta Sole - Sonia Trombino journal: Gels year: 2023 pmcid: PMC10048548 doi: 10.3390/gels9030226 license: CC BY 4.0 --- # Transdermal Delivery of Phloretin by Gallic Acid Microparticles ## Abstract Exposure to ultraviolet (UV) radiation causes harmful effects on the skin, such as inflammatory states and photoaging, which depend strictly on the form, amount, and intensity of UV radiation and the type of individual exposed. Fortunately, the skin is endowed with a number of endogenous antioxidants and enzymes crucial in its response to UV radiation damage. However, the aging process and environmental stress can deprive the epidermis of its endogenous antioxidants. Therefore, natural exogenous antioxidants may be able to reduce the severity of UV-induced skin damage and aging. Several plant foods constitute a natural source of various antioxidants. These include gallic acid and phloretin, used in this work. Specifically, polymeric microspheres, useful for the delivery of phloretin, were made from gallic acid, a molecule that has a singular chemical structure with two different functional groups, carboxylic and hydroxyl, capable of providing polymerizable derivatives after esterification. Phloretin is a dihydrochalcone that possesses many biological and pharmacological properties, such as potent antioxidant activity in free radical removal, inhibition of lipid peroxidation, and antiproliferative effects. The obtained particles were characterized by Fourier transform infrared spectroscopy. Antioxidant activity, swelling behavior, phloretin loading efficiency, and transdermal release were also evaluated. The results obtained indicate that the micrometer-sized particles effectively swell, and release the phloretin encapsulated in them within 24 h, and possess antioxidant efficacy comparable to that of free phloretin solution. Therefore, such microspheres could be a viable strategy for the transdermal release of phloretin and subsequent protection from UV-induced skin damage. ## 1. Introduction Ultraviolet (UV) radiation is responsible for damaging effects on the skin that occur either acutely or on a delayed basis. An example of an acute effect is inflammation caused by cytokines that results in erythema or “sunburn,” which is characterized by the reddening of the skin caused by the sun. In addition, such radiation increases cellular levels of reactive oxygen species and is responsible for phenomena such as oxidative stress [1], a pathological condition that occurs when an abnormally high amount of free radicals is produced in a living organism, exerting a damaging action on DNA, cells, and tissues, particularly the skin, in which there is an increase in fibroblasts, mast cells, macrophages, and T lymphocytes, and, thus, the development of an inflammatory process. All this leads not only to the establishment of photoaging processes, but also to a wide variety of chronic-degenerative diseases [2,3,4,5]. To perform its protective function, the skin has developed endogenous antioxidant and cytoprotective defense systems and produces specific detoxifying enzymes against reactive oxygen species [6]. Unfortunately, however, the aging process and environmental pollution can reduce the production of these endogenous defense systems. Therefore, natural antioxidants can be an important resource against UV-induced skin damage [7]. In fact, humans are unable to synthesize these compounds de novo, and several plant foods constitute the natural source of various antioxidants [8], such as carotenoids, polyphenols, etc. [ 9]. These include gallic acid and phloretin used here by us. Gallic acid (GA), or 3,4,5-trihydroxybenzoic acid, is a carboxylic acid of the phenolic type found in many types of plants and particularly in grapes, tea, hops, and oak bark. It has anti-inflammatory, antioxidant [10,11], antifungal, antibacterial, antiallergic [12], and anticarcinogenic [13,14] properties. Phloretin is a member of the flavonoid class dihydrochalcones and is distributed mainly in green apples and strawberries (Figure 1) [15]. The compound has antioxidant, anti-inflammatory [16,17], antidiabetic [18,19], anticancer, and antimicrobial properties [20]. However, phloretin’s poor water solubility reduces its absorption and, consequently, bioavailability, limiting its application in the treatment of various diseases and administration via conventional drug delivery systems [21]. To facilitate the topical administration of phloretin, it is possible to use formulations able to improve the dissolution of the drugs, increasing their stability and bioavailability [22,23,24,25,26,27]. For this purpose, polymeric microspheres based on gallic acid have been made here for the transdermal administration of phloretin (Figure 2). The decision to use gallic acid for particles formation is due to its interesting chemical structure. In fact, it has two types of functional groups, carboxyl and hydroxyl, which are amenable to derivatization. Moreover, gallic acid, which is present in the microsphere structure, can both act synergistically with the phloretin trapped inside and preserve it from oxidation processes. In fact, phloretin, in addition to being poorly soluble in water, is an unstable molecule that can degrade especially upon prolonged exposure to aqueous solutions. The microspheres, based on gallic acid, were obtained by reverse-phase emulsion radical polymerization reaction and characterized by transformed infrared spectroscopy. Their antioxidant efficacy was tested on rat liver microsomal membranes. Swelling behavior, phloretin loading efficiency, and transdermal delivery were also evaluated. All the results obtained indicated the possibility of using these microparticles for the transdermal delivery of phloretin and subsequent protection from UV-induced skin damage. ## 2.1. Esterification of the Gallic Acid with Methyl Alcohol This reaction, which represents the first step to obtaining the esterified gallic acid, required the use of DCC and DMAP. Methanol was used as both a solvent and a reagent. ( Scheme 1). The reaction was carried out in anhydrous conditions to avoid the degradation of DCC by water. The obtained mixture was deprived of solvent under reduced pressure, and raw residue was recrystallized from methanol. This allowed the methyl gallate to be obtained, which was characterized using FT-IR, GC/MS, and 1H-NMR. FT-IR (KBr) ν (cm−1): 3445 (–OH), 3329 (–OH), 3034 (–CH), 2945 (–CH), 2928 (–CH), 1760 (–C=O), 1627 (–C=C), 1244 (–CO). MZ: 56 ($100\%$), 184 ($2\%$). 1H-NMR (CD3OD) δ (ppm): 7.250 (2H, d), 3.829 (3H, s). The yield was $98\%$. ## 2.2. Transesterification of Methyl Gallate with Allyl Alcohol The methyl gallate [1] was subjected to trans-esterification with allyl alcohol. The potassium tert-butoxide removes a proton from the hydroxyl group of allyl alcohol, leading to the formation of an alcoholate, an extremely reactive nucleophile, that by attacking the carbonyl group of 1, promotes the release of the methoxy group and leads to the formation of the trans-esterified gallic acid (Scheme 2). The obtained product was hydrolyzed with acidic water and extracted with chloroform. The combined organic phases, dried under reduced pressure, gave a product [2] that was thoroughly characterized by FT-IR spectrophotometry, GC-MS, and 1H-NMR. FT-IR (KBr) ν (cm−1): 3446 (–OH), 3333 (–OH), 3034 (–CH), 1729 (–C=O), 1626 (–C=C), 1261 (–CO), 990 (–CH), 892 (–CH). m/z: 56 ($100\%$), 168 ($7\%$). 1H-NMR (CD3OD) δ (ppm): 6.853 (2H, d), 5.41 (1H, ddt), 5.05 (1H, dd), 5.01 (1H, dd), 4.66 (2H, d). The yield was $79\%$. ## 2.3. Preparation of the Microspheres Based on Allyl Gallate The microspheres were obtained by reversed-phase emulsion radical polymerization reaction. In particular, the aqueous solution of a monomer, which represents the dispersed phase, was added to an excess of organic solvents, immiscible with water, as the dispersing phase. Thus, under the action, small droplets of the dispersed phase formed and assumed a spherical shape. The reaction was carried out in a cylindrical reactor in which was added trans-esterified gallic acid previously solubilized in distilled water (dispersed phase). The dispersing phase, based on chloroform and n-hexane, had previously been introduced into the reactor and kept under stirring conditions. The formation of the microspheres was initiated by the addition of ammonium persulfate, radical initiator, and the comonomer N, N′-methylene-bis-acrylamide. More importantly, Span85 and Tween85 were also involved in this reaction. In fact, these surfactants contribute to the formation of the spherical structure of the particles. The TMEDA (N,N,N′,N′-tetramethyl-ethylenediamine) allows the process of the decomposition of the radical initiator to be accelerated. The reaction was carried out under constant mechanical stirring at 40 °C for three hours. The obtained microspheres were washed with three solvents in succession (isopropanol, ethanol, and acetone) and then were dried. ## 2.4. Microspheres Characterization The microspheres were characterized by means of light scattering and FT-IR, which confirmed the disappearance of the typical bands of the allyl group at 990 cm−1 and 892 cm−1 and the appearance of a new band of carbonyl stretching at 1634 cm−1. The light scattering analysis showed that the obtained microspheres had a good polydispersity (0.095 ± 0.001) and that the average particle diameter was equal to 1.5 ± 0.13 µm. Morphological analysis, by using scanning electron microscopy (SEM), showed that the particles, with micrometric dimensions, appeared to be grouped in part in large clusters (Figure 3). ## 2.5. Swelling Degree Evaluation The microspheres swelling degree (α%) was evaluated at appropriate time intervals (1 h, 2 h, 3 h, 4 h, 6 h, 12 h, 24 h), using Franz cells and a solution of water/ethanol $\frac{8}{2.}$ The swelling degree was calculated using the following equation:α%=Ws−WdWd×100 Ws and Wd represent the weights of swollen and dried microspheres, respectively [28]. Each experiment was carried out in triplicate. The results, reported in Figure 4, show the achievement of a good swelling degree after 2 h. This value remained almost constant up to 24 h, validating the possible use of microspheres for phloretin transdermal delivery. ## 2.6. Phloretin Loading Efficiency The microspheres were impregnated with a solution of water/ethanol $\frac{8}{2}$ of phloretin and left under stirring at 37 °C for 72 h. Subsequently, the solution was filtered and analyzed by UV-VIS (λ = 288 nm, ε = 3201.4 mol−1∙dm3∙cm−1). This allowed the loading efficiency (LE%) to be calculated through the following equation: LE%=Ci−CfCf×100 Ci represents the initial drug concentration in solution, while Cf is the drug concentration in solution after the loading study. LE% was $67\%$. ## 2.7. In Vitro Skin Permeation Studies The ability of the microspheres to release phloretin in contact with cellulose acetate membranes or the rabbit skin was evaluated after 1 h, 2 h, 3 h, 4 h, 6 h, 12 h, and 24 h using Franz cells. The obtained data showed that the microspheres released the phloretin efficiently in contact with the skin, and the cumulative percentage of active substance released in 24 h was comparable with the release of phloretin solution (Figure 5). In particular, about $90\%$ of the loaded phloretin was released within 24 h. The GC/MS analysis demonstrated that the microspheres protected phloretin from the degradation process. In fact, phloretin was almost integrally released after approximately 30 min from the acid gallic-based microspheres (Figure 6A). On the contrary, phloretin in solution, released under the same conditions, suffered degradation (Figure 6B). ## 2.8. Antioxidant Activity Evaluation The ability of the microspheres, loaded and not loaded with phloretin, to inhibit lipid peroxidation, induced by a source of free radicals, such as tert-butyl hydroperoxide (tert-BOOH), which endogenously produces alkoxyl radicals by Fenton reactions, was examined in rat liver microsomal membranes of Wistar rats (250–300 g) (Charles River Laboratories, Lecco, Italy) over 120 min of incubation [28]. The antioxidant activity of the prepared materials was time-dependent and, as can be seen from Figure 7, was preserved in time. The obtained data indicated that the microspheres containing phloretin possessed a higher antioxidant efficacy with respect to the unloaded ones. ## 3. Conclusions In this work, phloretin, a natural antioxidant substance with many therapeutic properties, was incorporated into gallic acid microspheres. Tests were performed that showed that the swelling of the particles increases with time, facilitating the release of phloretin from the matrix. Preliminary studies designed to evaluate the release behavior of phloretin have shown that the particles effectively release phloretin upon skin contact, with performance comparable to that of free drug solution. In addition, the microspheres were able to protect phloretin from degradation, as confirmed by mass spectrometry analysis. The antioxidant activity of loaded and unloaded gallic acid microspheres, tested in rat liver microsomes, was found to be time-dependent and higher for microspheres containing phloretin than for those that did not contain it. All the results obtained indicate that the microspheres, based on gallic acid, could be applied as a transdermal carrier of phloretin for the prevention and treatment of UV radiation-induced skin damage. ## 4.1. Reagents Acetone, hydrochloric acid, chloroform, diethyl ether, ethanol, isopropanol, methanol, n-hexane, tetrahydrofuran (THF), allyl alcohol, and sodium sulfate were purchased from Carlo Erba Reagents (Milan, Italy). Gallic acid (MW = 170.12), phloretin (MW = 274.27), dicyclohexylcarbodiimide (DCC), N,N-dimethylaminopyridine (DMAP), potassium tert-butoxide, N,N-dimethylacrylamide (DMAA), ammonium persulfate (NH4)2S2O8, N,N′-methylene-bis-acrylamide, sorbitan trioleate (Span85), polyoxymethylene sorbitan trioleate (Tween85), N,N,N′,N′-tetramethyl-ethylenediamine (TMEDA), tert-butylhydroperoxide (t-BOOH), trichloroacetic acid (TCA), 2-thiobarbituric acid (TBA), and butylated hydroxytoluene (BHT) were Purchased from Sigma-Aldrich (Sigma Chemical Co, St. Louis, MO, USA). ## 4.2. Instruments The IR spectra were performed using a spectrometer FT-IR Perkin Elmer 1720. 1H-NMR spectra were obtained using a spectrometer Bruker VM30; the chemical shifts were expressed as δ and referring to the solvent. The structures of the obtained compounds were confirmed by mass spectrometry using a Hewlett Packard instrument GM-MS Hewlett Packard 5972 (Analytical Instrument Management, Littleton, CO 80127, USA). The UV-VIS spectra were realized by means of UV-530 spectrophotometer JASCO. Dimensional analysis of the microspheres prepared were carried out by means of light scattering using a Brookhaven 90 Plus Particle Size Analyzer. Scanning electron microscopy (SEM) photographs of microspheres were obtained with a JEOL JSMT 300 A. ## 4.3. Animals The animal study protocol was approved by the Italian Ministry of Health (Rome, Italy) (protocol code 700A2N.6TI, date of approval: March 2018). ## 4.4. Esterification of Gallic Acid with Methyl Alcohol (1) In a three-neck flask fitted with a reflux condenser and magnetic stirring, fully flamed, and maintained under nitrogen atmosphere, gallic acid (2 g, 1.17 × 10−2 mol) was dissolved in dry methanol (13 mL) [29]. The reaction was left under reflux and magnetic stirring at 35 °C for 30 min. After that, DCC (2.41 g, 1.17 × 10−2 mol) and DMAP (1.43 g, 1.17 × 10−2 mol) were added, and the system was stirred for 30 min. The reaction, kept under reflux for 12 h at 50 °C and magnetic stirring, was monitored using thin layer chromatography (TLC/silica gel, eluent phase chloroform-methanol mixture 7:3). At the end, the reaction mixture was hydrolyzed and extracted with chloroform (3 × 10 mL). The orange organic phase was dried under vacuum. Next, the obtained solid was purified by recrystallization from methanol. The white precipitate [1] was recovered by filtration and characterized through FT-IR spectrophotometry, GC-MS, and 1H-NMR. ## 4.5. Transesterification of Methyl Gallate with Allyl Alcohol (2) The second step involved the trans-esterification of the compound [1] with allyl alcohol. The reaction was carried out according to a procedure reported in the literature [30]. In a three-neck flask, fitted with a reflux condenser and magnetic stirring, fully flamed, and maintained under nitrogen atmosphere, allyl alcohol (0.33 mL, 8.06 × 10−3 mol) was dissolved in dry THF (30 mL). The solution was heated to 80 °C and stirred. The temperature was then lowered to 70 °C and potassium tert-butoxide (0.98 g, 8.06 × 10−3 moles) was added after 30 min. The methyl gallate (1.49 g, 8.06 × 10−3 moles) was added. The reaction was monitored using thin layer chromatography (TLC/silica gel, eluent chloroform-methanol mixture in the ratio 8:2). Then the reaction mixture was hydrolyzed with acid water and extracted with chloroform. The orange organic phase was dried under vacuum. The white product [2] was characterized through FT-IR spectrophotometry, GC-MS, and 1H-NMR. ## 4.6. Preparation of Microspheres Based on Allyl Gallate Microspheres, based on allyl gallate, were obtained by radical polymerization reaction in reverse phase emulsion according to the procedure reported in literature [30,31]. Briefly, a cylindrical glass reactor of 100–150 mL, equipped with mechanical stirrer, dripping funnel, and screw cap with puncture-proof rubber septum, was flamed in a nitrogen flow; after cooling, it was immersed in a bath thermostatically controlled at 40 °C. Next, n-hexane (20 mL) and chloroform (18 mL) were introduced into the reactor. After 30 min of N2 bubbling, this mixture was treated with distilled water containing allyl gallate (0.1 g, 4.7 × 10−4 mol), the co-monomer methylene bisacrylamide (MBA, 0.037 g, 2.38 × 10−4 mol), and ammonium persulfate 800 mg (3.5 × 10−3 moles) as radical initiator. The mixture, under stirring at 1000 rpm, was treated with 150 μL of sorbitan trioleate (Span85), then after 10 min, with 150 μL of polyoxymethylene sorbitan trioleate (Tween85), and after a further 10 min, with 150 μL of N,N,N′,N′-tetramethyl-ethylenediamine (TMEDA). This mixture was left under stirring conditions for another 3 hours [26]. The obtained microspheres were filtered, washed several times with 100 mL of isopropanol, 100 mL of ethanol, and 100 mL of acetone to remove all traces of free acrylic moieties, co-monomer, and initiator, and dried under vacuum at 40° C overnight. Their characterization was carried out by light scattering, FT-IR spectrometry, and SEM. ## 4.7. Phloretin Loading Efficiency Microspheres (0.49g) were soaked for 3 days at room temperature, under magnetic stirring, in a phloretin solution. The phloretin (0.01 g, 3.5 × 10−5 moles) was solubilized in 10 mL of distilled water/ethanol $\frac{8}{2}$ solution. The amount of solubilized drug was chosen to have a drug loading of $20\%$ (w/w). After 3 days, the microspheres were filtered and dried, at reduced pressure, in the presence of P2O5 at constant weight. ## 4.8. Size Distribution Analysis The size of the particles was determined by dynamic light scattering (DLS) using a 90 Plus Particle Size Analyzer (Brookhaven Instruments Corporation, New York, NY, USA) at 25 °C by measuring the autocorrelation function at 90° scattering angle. Cells were filled with 100 µL of sample solution and diluted to 4 mL with filtered (0.22 µm) water. The polydispersity index (PI), indicating the measure of the distribution of particle population [28], was also determined. Six separate measurements were made to derive the average. Data were fitted by the method of inverse “Laplace transformation” and Contin. ## 4.9. Swelling Degree Evaluation The swelling behavior of the microspheres was investigated to check their hydrophilic affinity. The study was realized through Franz cells using a solution water/ethanol $\frac{8}{2.}$ Predetermined aliquots of dried microspheres (0.018 g) were placed in the Franz cells with a solution of water/ethanol. At predetermined time intervals (1, 2, 3, 4, 6, 12, 24 h), microspheres were deprived of excess water, weighed, and finally their swelling degree was calculated. Each experiment was carried out in triplicate ($$n = 3$$). ## 4.10. In Vitro Skin Permeation Studies Skin permeation tests were performed using Franz Diffusion cells ($$n = 3$$) both with cellulose acetate membranes and rabbit ear skin (New Zealand rabbits 2.9–3.1 kg; provided by local butcher) for 24 h with the aim to compare eventual components of skin interferences. The apparatus was maintained at 37.0 °C to mimic physiological conditions. Receptor chambers (6.0 mL) were filled with NaCl $0.9\%$ solution and kept under stirring conditions. Free phloretin was used as control. At specific time intervals (1, 2, 3, 4, 6, 12, and 24 h) aliquots (7 mL) of each sample were withdrawn from receptor chambers and replaced with fresh release medium. Samples were analyzed through UV-Vis spectrophotometry (288 nm) and GC-MS. In particular, after release, all the solutions withdrawn intended to be characterized by GC/MS were dewatered and then subjected to analysis after solubilization in ethanol. ## 4.11. Antioxidant Activity Evaluation First, 1 mL of microsomal suspension, reacted with microspheres, was mixed with 3 mL $0.5\%$ TCA and 0.5 mL of TBA solution (two parts $0.4\%$ TBA in 0.2 M HCl and one part distilled water), and 0.07 mL of $0.2\%$ BHT in $95\%$ ethanol. Samples were then incubated in a thermostatic bath at 90 °C for 45 min. After incubation, the TBA–MDA complex was extracted with 3 mL of isobutyl alcohol. The absorbances of the extracts were measured by UV spectrophotometry at λ = 535 nm, and the results were expressed as mmol per mg of protein [32]. ## 4.12. Statistical Analysis All quantitative data were expressed as means ± standard deviations. Differences between means were analyzed for statistical significance using the Student’s t-test. In this study, p-values less than 0.05 were considered statistically significant. ## Figures and Schemes Figure 1: Phloretin: structure and source. Figure 2: Gallic acid-based microsphere containing phloretin. Scheme 1: Reaction of esterification of the gallic acid with methyl alcohol. Scheme 2: Reaction of the transesterification of methyl gallate with allyl alcohol. Figure 3: Scanning electron microscope (SEM) photomicrography of microspheres. Figure 4: Swelling degree of microspheres. The results represent the mean ± SD of three separate experiments. Figure 5: Graph of the cumulative release of phloretin. The results represent the mean ± SD of three separate experiments. Significance was evaluated by the unpaired t-test. Figure 6: Evaluation, through GC/MS analysis, of phloretin released, after approximately 30 min, from acid gallic-based microspheres (A) and from a solution (B) under the same conditions. 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--- title: Anti-Obesity and Anti-Dyslipidemic Effects of Salicornia arabica Decocted Extract in Tunisian Psammomys obesus Fed a High-Calorie Diet authors: - Souhaieb Chrigui - Sameh Hadj Taieb - Hedya Jemai - Sihem Mbarek - Maha Benlarbi - Monssef Feki - Zohra Haouas - Ayachi Zemmel - Rafika Ben Chaouacha-Chekir - Nourhène Boudhrioua journal: Foods year: 2023 pmcid: PMC10048570 doi: 10.3390/foods12061185 license: CC BY 4.0 --- # Anti-Obesity and Anti-Dyslipidemic Effects of Salicornia arabica Decocted Extract in Tunisian Psammomys obesus Fed a High-Calorie Diet ## Abstract Salicornia is a halophyte plant that has been used in traditional medicine for the treatment of scurvy, goiter, and hypertension. It is commercialized in Europe and Asia as fresh salads, pickled vegetables, green salt, or tea powder. This work is the first to assess the potential anti-obesity and anti-dyslipidemic effects of Salicornia arabica decocted extract (SADE). SADE was characterized by its significant in vitro radical scavenging activity (using DPPH and ABTS assays). The effect of SADE on food intake, weight loss, serum biochemical parameters, liver and kidney weights, adiposity index and on liver histology was investigated in the Tunisian gerbil Psammomys obesus (P. obesus), which is recognized as a relevant animal model of human obesity and diabetes. P. obesus animals were firstly randomly divided into two groups: the first received a natural low-calorie chow diet (LCD), and the second group received a high-calorie diet (HCD) over 12 weeks. On day 90, animals were divided into four groups receiving or not receiving SADE (LCD, LCD + SADE, HCD, and HCD + SADE). If compared to the HCD group, SADE oral administration (300 mg/kg per day during 4 weeks) in HCD + SADE group showed on day 120 a significant decrease in body weight (−$34\%$), blood glucose (−$47.85\%$), serum levels of total cholesterol (−$54.92\%$), LDL cholesterol (−$60\%$), triglycerides (−$48.03\%$), and of the levels of hepatic enzymes: ASAT (−$66.28\%$) and ALAT (−$31.87\%$). Oral administration of SADE restored the relative liver weight and adiposity index and significantly limited HCD-induced hepatic injury in P. obesus. SADE seems to have promising in vivo anti-obesity and anti-dyslipidemic effects. ## 1. Introduction Obesity and overweight result from an imbalance between consumed calories, basal metabolism, and energy expenditure [1]. They are characterized by excessive accumulation of fat in adipose tissue, liver, pancreatic islets, muscles and other metabolism-involved organs, which can be harmful to health [2]. The modern lifestyle involving a high-calorie diet (HCD) and less physical activity contributes to the concomitant development of obesity and dyslipidemia. Dyslipidemia results from lipid metabolic changes and is characterized by elevated concentrations of total cholesterol (TC), low-density lipoprotein cholesterol (LDL), and triglycerides (TG), and low levels of high-density lipoprotein cholesterol (HDL) [3]. Obesity, dyslipidemia and their complications such as diabetes mellitus, hypertension and cardiovascular diseases are one of the major causes of comorbidity and the excess of mortality [3,4,5,6,7]. The treatment of obesity has proven hugely resistant to therapy, with anti-obesity medications which often showed insufficient efficacy, dubious safety and gastrointestinal side effects (nausea, diarrhea, vomiting, and constipation) [8]. Natural anti-obesity products (biomolecules, plant crude extracts, mixture of fruit or plant crude extracts) were reported to be an excellent alternative strategy for developing effective and safe anti-obesity and anti-dyslipidemic agents [1,9,10,11,12,13,14,15]. For example, it was shown that long-term Huangshan Maofeng green extract supplementation remarkably reduced excessive fat accumulation, increased gut microbiota diversity, restored the relative abundance of the microbiota responsible for producing short-chain fatty acids and reduced hyperlipidemia and hepatic steatosis in rats [16]. Antioxidants and particularly phenolic compounds (simple phenolic acids, curcumins, flavonoids, anthocyanin, and catechins) were reported to have potent anti-obesity and dyslipidemic effects. Anti-obesity and anti-dyslipidemic effects of phenols were essentially related to the enhancement of antioxidant defense, the inhibition of cholesterol absorption and lipogenic and adipogenic activities [17]. Halophytes, native plants of saline ecosystems, present economic and ecological interests because of their high salt tolerance and are an important source of bioactive compounds, particularly phenols [18]. They are evaluated as a promoting candidate for culinary and pharmaceutical applications [19]. The genus Salicornia is a halophyte belonging to the Chenopodiaceae family, including about thirty species of succulent annual hygro-halophyte plants. Salicornia genus has recently been commercialized in Europe and Asia as a staple food, and it is used in green salads for its saltiness [20] or as an ingredient in various recipes as tea powder, seasoned vegetable, makgeolli, pickled vegetables, vinegar and fermented food [21]. Various promoting therapeutic applications have been reported for Salicornia species: Salicornia herbacea and Salicornia bigelovii are in particular used against oxidative stress, inflammation gastroenteritis, cancer, diabetes, asthma and hepatitis [21] and in the treatment of various diseases such as obesity, hyperglycemia and hyperlipidemia [22,23]. Salicornia herbacea supplementation reduces fat accumulation in the liver and regulates hepatic triglycerides [24], decreases lead-induced oxidative stress, and exerts cytoprotective action [25]. Salicornia arabica (S. arabica) lipid extract was reported to induce a protective effect against cadmium-induced erythrocyte damage. In vitro, the antioxidant properties of S. arabica polysaccharides and lipid extracts were also examined [26,27], whereas the potential in vivo protective effect of antioxidants in the decocted extract of S. arabica is not yet explored. Psammomys obesus (P. obesus), Muridae, Gerbillidae is a desert gerbil of particular interest because, in its native habitat, P. obesus feeds on low-caloric vegetation remains healthy when in captivity and is subjected to nutritional stress induced by a high-calorie laboratory diet, resulting in the development of obesity, dyslipidemia, diabetes [28], and diabetic retinopathy [29]. P. obesus is known as a reference animal model of nutritionally-induced obesity and its complications [28]. The aim of this work is to examine the anti-obesity and anti-dyslipidemic effects of the antioxidant decocted extract of S. arabica in P. obesus fed a high-fat calorie diet. To the best of our knowledge, this is the first work reporting in vivo anti-obesity and anti-dyslipidemic effects of supplemented S. arabica decocted extract (SADE) in HCD induced -obesity and dyslipidemia in rats. The effects of SADE on body weight, food intake, energy intake, serum biochemical parameters, liver and kidney relative weights, adiposity index and on the histology of the liver tissue were assessed. ## 2.1. Plant Material Chenopodiaceae plant material (S. arabica) was collected and identified at the Biology Agronomy and Plant Biotechnologies Department at the National Agronomic Institute, University of Carthage, Tunisia. ## 2.2. High-Calorie and Low-Calorie Diets A high-calorie standard laboratory chow (EL BADR, Bizerte, Tunisia) supplemented with $30\%$ sugar, $30\%$fat (corn oil) and saline water (NaC1 $0.9\%$) was used as a high-calorie diet (HCD). The aerial part of Chenopodiaceae plant material (S. Arabica) was used as a natural low-calorie diet (LCD). ## 2.3. Proximate Chemical Composition of Low and High-Calorie Diets The Association of Official Analytical Chemists (AOAC) method [30] was used to determine the nutritional compositions and energetic values of LCD and HCD. Samples were weighed and dried at 105 °C for 24 h for moisture content determination. Ash content was measured by using a muffle furnace (Nabertherm GmbH, Lilienthal, Bremen, Germany) at 550 °C for 5 h. Total protein content was determined by the Kjeldahl method and calculated by multiplying the nitrogen content by 6.25. Fat content was determined by using the Soxhlet method, with hexane as a solvent. Carbohydrate content was estimated by the difference of mean experimental values, i.e., 100 − (sum of percentages of moisture, ash, protein and fat) [31]. The sample weight was measured by an analytical balance (Ohaus Corporation, Parsippany, NJ, USA) having a precision of ±10−4 g. Energetic values (EV) of LCD and HCD were estimated on the basis of protein, fat and carbohydrates content as follows [32]:[1]EV (kcalg)=4×Protein content+4×Carbohydrate content+9×Fat content ## 2.4. Preparation of Decocted Salicornia arabica Extract The aerial part of S. arabica was dried in the dark in the oven at 40 °C. After drying, the plant material was crushed in a mixer mill (Isolab, Laborgeraete GmbH, Germany), then 100 g of S. arabica powder was added to 1000 mL of distilled water and boiled for 15 min. It was then left to cool at room temperature for 20 min. The extract was centrifuged for 10 min at 5000 rpm, and the supernatant was filtered through Whatman filter paper. The resulting decoction was concentrated using a rotary evaporator, frozen and then freeze-dried (Biobase, BK-FD12P, Jinan, China). The obtained freeze-dried decocted extract (SADE) was vacuum packed and stored at −30 °C until use. ## 2.5. In Vitro Antioxidant Properties of Decocted Salicornia arabica Extract The total phenols content (TPC), total flavonoids content (TFC), and the radical scavenging activities of SADE were determined. ## 2.5.1. Determination of Total Phenols Content The determination of total phenols content (TPC) was carried out according to the Folin–Ciocalteu method modified according to M’hiri et al. [ 33]. The sample was added to Folin–Ciocalteu reagent and Na2CO3 solution and placed in a water bath at 40 °C for 30 min before spectrophotometric analysis (PEAKII UV, C7200S, USA). Total phenolic content was determined colorimetrically at 765 nm and expressed as mg of Gallic Acid Equivalent (GAE) per g of freeze-dried SADE. ## 2.5.2. Determination of Total Flavonoids Content The determination of total flavonoids content (TFC) was carried out by spectrophotometric method as described by M’hiri et al. [ 33] with aluminum trichloride. First, 0.5 mL of S. arabica decocted extract (SADE) was placed in a 5 mL plastic tube, then 2.5 mL of distilled water, followed by 0.15 mL of NaNO2 ($5\%$), was added. After 5 min, 0.15 mL of AlCl3 ($10\%$) was added, and finally, 1 mL of NaOH (1M) was added another 5 min afterward. The volume was made up to 5 mL with distilled water. The solution was mixed, and the absorbance was measured at 510 nm using a spectrophotometer (PEAKII UV, C7200S, Houston, TX, USA). Total flavonoid content was expressed in mg of Quercetin Equivalent per g of freeze-dried SADE. ## 2.5.3. Determination of In Vitro Antioxidant Radical Scavenging Activities The free radical scavenging activity of SADE extract was determined using a 1.1-diphenyl-2-picrylhydrazyl (DPPH) assay [34] and a 2 2′-azino bis 3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assay with minor modifications as reported by M’hiri et al. [ 33]. For the DPPH assay, the DPPH radical scavenging activity of the extract was assessed by measuring the absorbance at 515 nm using a spectrophotometer (PEAKII UV, C7200S, Houston, TX, USA). For the ABTS assay, ABTS radical scavenging activity was determined by measuring the absorbance at 734 nm. DPPH and ABTS radicals scavenging activities were expressed as mg Trolox Equivalent, TE eq. per g of freeze-dried SADE. ## 2.6. Animals The Tunisian P. obesus animals used in the trials were captured in Southern Tunisia (Bouhedma region, located at 34°25′0″ N, 9°30′0″ E). The animals were then taken into captivity at the laboratory animal facility and left for a week for acclimation in a large aquarium and furnished space with sand, nearly similar to their natural environment. After acclimation, adult male Psammomys obesus (P. obesus) aged eight weeks and having an average body weight of 101 ± 8 g. Animals placed in individual cages (40 cm × 25 cm × 17 cm) and maintained during the experiment of 120 days under standard and controlled environmental conditions (temperature 22–25 °C, hygrometry 60–$70\%$) with a light on a photoperiod $\frac{12}{12}$ [35]. ## 2.7. Experimental Design During the first week corresponding to the animals’ adaptation to life in captivity, animals were exclusively fed on their natural Chenopodiaceae halophilic plant, LCD. After the week of adaptation, the 40 P. obesus were first randomly divided into two groups:-Control group: received the natural vegetable diet of P. obesus, which is considered in this work as a natural low–calorie diet (LCD)-HCD group: received the high-calorie diet, rich in carbohydrates and fat-On the 90th day, each group of animals (LCD and HCD) was divided into two groups each, as follows:-LCD: used as a negative control: received the natural low-calorie vegetable diet-LCD + SADE: used as positive control: received Chenopodiaceae with oral administration of a dose of 300 mg SADE/kg per day-HCD: fed with HCD without administration of SADE-HCD + SADE: fed with HCD with oral administration of a dose of 300 mg SADE/kg per day. The four animal groups received food and water ad-libitum during the 120-day period. The choice of SADE dose of 300 mg SADE/kg per day was made on the basis of published data using other halophyte plant extracts for in vivo experimentations [36,37]. The percentage of the P. obesus initial body weight, % Pi, was determined according to Equation [2] and was presented bi-monthly. The body mass index (BMI) was determined monthly. It was assessed by dividing the body weight (g) by the square of the nose-anus length (cm) of P. obesus [38]. [ 2]% Pi=Pt×100Pi [3]BMI (g/cm2)=Body weight (g)Body lengthh2 (cm) where Pt is P. obesus weight at time t and Pi is its initial weight. Animals were considered obese when the average weight gain of the animal at a given time was equal to or superior to $150\%$ [35]. Preliminary investigations showed that this criterion is in agreement with the measurement of P. obesus’ BMI, which was higher than 0.68 g/cm2 for obese animals [38]. The nutritional status was determined by calculating food consumption (FC), energy intake (EI), energy intake, EI and feed efficiency (FE) [39,40,41]. Daily food consumption, or FC (g/day), was calculated as follows:[4]FC (g/day)= quantity of food supplied− quantity of food remaining after 24 Average values of daily food intake (FI) and energy intake (EI) were determined for each animal group. Food intake, FI (%/day), was calculated as follows:[5]FI (%/day)=FC (g/day)Animal weight (g)×100 Daily energy intake, EI (kcal), was calculated as follows:[6]EI (kcal/day)=FC (g/day)×dietary metabolizable energy(kcalg) where food consumption (FC) was weighed, and dietary metabolized energy was calculated according to corresponding energetic values. Feed efficiency (FE) was expressed in % and was defined as the ability of animals to convert feed energy consumed in body weight and was measured by dividing body weight gain (g) by the total energy intake (kcal) and multiplying by 100 [39,40,41]:[7]FE (%)=Mean body weight gain (g)Total energy intake (kcal)×100 ## 2.8. Blood Sampling and Serum Biochemical Parameters Analyses Blood glucose level was measured bi-monthly. Blood was collected by retro-orbital sinus puncture with a capillary hematocrit and was estimated using an Accu-Check Blood Glucose Meter (Roche, Manheim, Germany). The serum is obtained by centrifugation of the blood at 5000 rpm for 15 min, 4 °C, aliquot, and stored at −30 °C until used for analysis. Serum concentrations of total cholesterol (TC), total triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), aspartate aminotransferase (ASAT) and alanine aminotransferase (ALAT)were assessed by the enzymatic colorimetric method by an Architect C8000 analyzer (Abbott Laboratories, Abbott Park, IL, USA) using the respective reagent kits [42]. Furthermore, the atherogenic index (AtI) was calculated for different animal groups. It is defined as the ratio of LDL (TC-HDL) and HDL according to the Friedewald equation. [ 8]AtI=(TC−HDL)HDLP. obesus animals were considered dyslipidemic when TC ≥ 2.00 g/L and/or LDL ≥ 1.60 g/L and/or TG ≥ 1.50 g/L and/or HDL < 0.40 g/L. ## 2.9. Animal Sacrifice and Organs Sampling After four months of experimentation, the animals were weighted and sacrificed by decapitation. Immediately after sacrifice, the liver, the kidneys and the adipose tissue were excised and washed with $0.9\%$ NaCl, and the relative weights were determined. Adiposity index (Ia) was calculated as follows [39]:[9]Ia=AT (g)Animal weight (g)×100 where AT (g) is the weight of adipose tissue. ## 2.10. Animal Welfare and Ethics Statement The present experimental protocol was approved by the National Ethical Committee on Medical and Animal Research of the National Veterinary Medicine School, E.N.M.V of Tunisia (Approval Number: CEEA-ENMV $\frac{23}{20}$). The study was performed in accordance with the “Guide for the Care and Use of Laboratory Animals” published by the US National Institutes of Health (NIH publication No. 85–23, revised 1996). All efforts were made to minimize animal suffering and reduce the number of animals used. ## 2.11. Histological Observation of the Liver Liver samples were fixed in a $10\%$ buffered neutral formalin solution and were transferred into an automatic processor (Leica TP 1020, Buffalo Grove, IL, USA) where they were dehydrated in a graded ethanol series, cleared in xylene, and embedded in paraffin wax. Samples were sectioned at 5 μm thickness by using a rotary microtome (Medite M380, Burgdorf, Germany). The sections were stained with hematoxylin and eosin (H&E) and then were examined using a Leica light microscope (Leica DM750, Wetzlar, Germany), and provided with a camera (Leica ICC50, Wetzlar, Germany). For each liver specimen, tissue changes were examined in 10 randomly selected areas. The microscopic appearance of the liver tissues was examined for fatty vacuolation, hepatocyte necrosis, hepatocyte ballooning, massive micro and macrovesicular, intracellular lipid droplets in hepatocytes and inflammatory cell infiltration. The number of apoptotic cells, necrotic cells and lipid droplets were measured using the particle sizing function provided by the ImageJ software version 1.53 (Rasband, ImageJ, National Institutes of Health, Bethesda, MD, USA). Counting cells was assessed in triplicate using the following equation:[10]Counting cells (%)=(count target cells)(Total number of hepatic cells)×100 ## 2.12. Statistical Analysis Results were expressed as mean values ± standard deviation (SD). Multiple-group analysis using ANOVA, with a post-hoc test, was used to analyze the statistical significance between parameters measured for different animal groups. Differences were considered significant when $p \leq 0.05.$ Principal components analysis (PCA) was performed on measured average body weight gain (% Pi) and biochemical parameters (TC, TG, HDL, LDL, ALAT, ASAT, and AtI) to elucidate significant differences between animal groups (LCD, LCD + SADE, HCD, and HCD + SADE) on day 0, day 90 (the beginning of SADE administration) and day 120 (the end of SADE administration and of the experiment). The number of dimensions considered for the PCA was chosen to equal two in order to allow meaningful interpretations of the results. All statistical analyses were performed using SPSS statistical software version 22.0 (SPSS Inc., Chicago, IL, USA). ## 3.1. Proximate Chemical Composition and Energetic Values of Low and High-Calorie Diets Table 1 shows the proximate composition of LCD and HCD. The HCD is characterized by EV of 4.50 kcal/g with the following composition: $9.11\%$ moisture, $58.33\%$ carbohydrates, $2.83\%$ ash, $10.82\%$ proteins, and $18.91\%$ fat. The LCD corresponding to the areal part of S. arabica is characterized by high ash content (8.42 ± 0.15 g/100 g wb) and moisture content (81.63 ± 0.69 g/100 g wb) and a low EV (0.42 ± 0.03 kcal/g wb). A similar composition was reported for the Chenopodiaceae plant, Salsola foetida, with an EV of 0.4 kcal/g [38], S. bigelovii, with an EV of 0.3 kcal/g [31], and S. herbaceae with an EV of 0.44 kcal/g [43]. The determined EV of HCD is also similar to that used in similar experiments varying from ~3.70 kcal/g [38,44] to ~4.00 kcal/g [41,45]. ## 3.2. Phenols, Flavonoid Contents, and In Vitro Radical Scavenging Activities of SADE Total phenolic, total flavonoid contents (TPC, TFC), DPPH, and ABTS radical scavenging activities of S. arabica decocted extract (SADE) are presented in Table 2. SADE is characterized by a TPC of 20.5 ± 0.3 mg GAE/g and an antioxidant activity of 3.2 ± 0.1 mg Trolox/g of extract (DPPH assay) and 17.30 ± 0.65 mg TE/g (ABTS assay). Spectrophotometric determination methods of total phenols, flavonoids content, and antioxidant activities in plant extracts may be subject to artifacts due to the interaction with other compounds, such as non-phenolic pigments. Nevertheless, these methods allow the screening of plant extracts according to their antioxidant potential in comparison with other plant extracts assessed by the same methods [46]. TPC and TFC in S. arabica decocted extract were high compared to some known halophyte plants assessed with spectrophotometric methods. Chikhi et al. [ 47] reported that the aqueous extract of Atriplex halimus has a phenolic content of 12.4 mg GAE/g extract, and Kim et al. [ 48] reported that the aqueous extract of Salicornia europaea contains 11.6 mg QE/g extract. ABTS scavenging capacity of SADE is similar to that of S. europea originating from Italy (ABTS = 15.1 mg TE/g) reported by Costa et al. [ 49]. Halophyte plant extracts are characterized by promising in vitro antioxidant activities, as reported by many authors [19,22,50]. Phenolic acids such as ferulic acid, cinnamic acid, chlorogenic acid and coumaric acid were reported as the main phenolic compounds in many halophytes [18,37,51,52]. ## 3.3.1. Effects of SADE on Food, Energy Intakes and Energy Efficiency The average daily food intakes (FI) of the LCD group and LCD + SADE group were 42.77 ± $0.62\%$/day and 40.47 ± $0.90\%$/day, respectively (Figure 1a). The values of FI of HCD and HCD + SADE-fed animals were significantly ($p \leq 0.05$) lower (7.11 ± $1.46\%$/day for HCD and 8.41 ± $0.85\%$/day for HCD + SADE) than those of the LCD and LCD + SADE groups. Due to the high-calorie value of HCD (4.50 kcal/day) compared to a natural low-calorie diet (0.42 kcal/g), the daily EI of both HCD (58.11 ± 2.58 kcal/day) and HCD + SADE-fed animals (51.94 ± 2.11 kcal/day) are higher than EI of LCD group (20.72 ± 1.19 kcal/day) and LCD + SADE-fed animals (17.75 ± 0.48 kcal/day) (Figure 1a). According to the feed efficiency percentage, FE (%) (Figure 1b), the LCD +SADE group underwent a significant decrease as compared to the LCD group ($p \leq 0.05$). The HCD group demonstrated a significantly higher FE percentage in comparison to the HCD + SADE ($p \leq 0.05$) and LCD group ($p \leq 0.05$). The HCD + SADE exhibited a significant decrease in FE ratio as compared to the LCD group ($p \leq 0.05$) (Figure 1b). SADE administration seems to decrease food and energy intake in P. obesus. In addition, FE was higher in the HCD group (+$73.46\%$) than in the LCD group. This difference is two folds higher than those reported by Ferron et al. [ 39] and Rocha et al. [ 41] for Wistar rats fed HCD. FE value assessed in the HCD group (1.70 ± $0.06\%$) is lower than that obtained by Novelli et al. [ 40] for Wistar rats (5.4 ± $0.8\%$). This difference can be due to different body weight gain in the different used animal models. ## 3.3.2. Effects of SADE on Body Weight Change Figure 2 shows the body weight change in percentage (% Pi) for the different groups of P. obesus during the experimental period. As expected, animals in HCD fed group showed significantly higher body weight change than those fed with a normal low-calorie diet (LCD). The % Pi of the LCD and LCD +SADE animals remained almost constant during the 120 days of the experiment. However, after 30 days, the % Pi of the HCD and HCD + SADE rats were significantly higher than those of the LCD and LCD + SADE rats ($p \leq 0.05$). The % Pi of the HCD animals remained significantly higher (187.58 ± $12.47\%$; $p \leq 0.05$) than that of the LCD and LCD +SADE animals during 120 days of the experiment. The P. obesus fed HCD and administered with SADE on day 90 showed a marked decrease of %Pi (123.81 ± $7.77\%$) by $34\%$ after 120 days if compared to HCD group ($p \leq 0.05$) and was not significantly different if compared to the LCD and LCD + SADE groups (respectively, 111.27 ± $11.72\%$ and 98.85 ± $4.52\%$) (Figure 2). These observations confirm that obesity was successfully induced after the first month of the HCD diet application. HCD + SADE-fed P. obesus showed a decrease in animal weight, indicating that SADE was effective in averting weight gain. This is in agreement with the result above, indicating the decrease of EI of the HCD + SADE group compared to the EI of the HCD group (Figure 1a). FE value was significantly lower in the HCD + SADE group (Figure 1b), indicating that SADE prevents animal body weight increase. Used HCD (4.50 kcal/g) allows a fast establishment of obesity in P. obesus. Indeed, generally, more than one month is necessary to establish obesity in such animal models. Several studies showed that the sand rat develops obesity after two to three months of HCD (3.25 kcal/g and 3.85 kcal/g, respectively) [44,53]. HCD administration for up to three months caused significant metabolic changes in Meriones shawi rats and resulted in the development of obesity [54]. In addition, some studies using Wistar rats [55] reported that the HCD (3.65 kcal/g) induces an increase in body weight and establishment of obesity from two to six months of HCD. Besides, the results of the present study are in agreement with those of Rahman et al. [ 36], who reported that the oral administration of 250 and 500 mg/kg of S. europaea for 12 weeks resulted in a significant reduction in the body weight of the Sprague–Dawley rats fed on HCD. ## 3.3.3. Effects of SADE on Body Mass Index BMI in rats is an easy and reproducible anthropometric assessment of obesity [56]. When BMI is above 0.68 g/cm2, P. obesus are considered obese, and there is an increased risk of developing metabolic syndrome such as dyslipidemia by an increase in lipid biomarkers [28]. As shown in Figure 3, there were no significant BMI differences between the different animal groups at the beginning of the experiment. However, HCD and HCD + SADE groups led to a strong increase in BMI from days 30 to 90 ($p \leq 0.05$), whereas LCD and LCD + SADE groups were similar and stayed roughly constant until the end of the experiment. After oral administration of SADE, from day 90 to day 120, BMI slightly decreased on day 120 in LCD + SADE group ranging from 0.39 ± 0.02 g/cm2 (day 90) to 0.32 ± 0.02 g/cm2 (day 120) compared to the LCD group and the difference was statistically significant ($p \leq 0.05$). A high significant decrease ($p \leq 0.05$) in BMI on day 120 was noticed in HCD + SADE animals ranging from 0.72±0.07 g/cm2 (day 90) to 0.50 ± 0.02 g/cm2 (day 120) compared to the HCD group (0.75 ± 0.02 g/cm2). Figure 3 shows progressive changes in BMI in all groups of P. obesus during the experimental period. None of the animals in the HCD group was obese before the first thirty days, which indicates that BMI stayed normal and has not attained 0.68 g/cm2. However, HCD led to a rapid body weight change and accelerated the development of obesity after the first month. A similar value of BMI was reported for Wistar rats and P. obesus by Mashmoul et al. [ 56] (0.76 ± 0.05 g/cm2) and Gouaref et al. [ 38] (0.67 ± 0.03 g/cm2). ## 3.4. Effects of SADE on the Relative Weight of Liver and Kidney and Adiposity Index Changes Figure 4 shows the effect of SADE on the relative liver (Figure 4a), kidney (Figure 4b) weights and adiposity index change (Figure 4c) in normal and obese dyslipidemic rats. On day 120, the relative liver weight and adiposity index decreased slightly from 3.22 ± 0.19 to 2.92 ± $0.10\%$ (LCD group) and from 0.14 ± 0.01 to 0.13 ± $0.01\%$ (LCD + SADE group), and the difference was not statistically significant ($p \leq 0.05$). However, SADE administration in HCD animals significantly restored the relative liver weight ($p \leq 0.05$; Figure 4a) and adiposity index ($p \leq 0.05$; Figure 4c) in comparison to the HCD group. Though, the relative weight of the kidney decreased in HCD and HCD + SADE groups compared to LCD and LCD + SADE groups and showed a significant difference between HCD + SADE and LCD groups ($p \leq 0.05$; Figure 4b). Saidi et al. [ 44] reported that HCD resulted in a significant increase in relative liver weight and adiposity index due to the accumulation of energy as triglycerides stored in tissues as well as its deposition in these organs. The increase in body weight of rats consuming high-energy diets is a sign of the increase in the number and/or size of adipocytes [57]. Adipose tissue is an active endocrine tissue secreting adipocytokines that affect full-body energy homeostasis through the sensing metabolic signals [57]. Many authors reported that the consumption of an HCD increases body weight and induces the accumulation of fat in adipose tissue. The accumulation of fat is the result of direct excess intake of a high-fat diet and/or the synthesis of fatty acids, mainly from carbohydrates [50]. The oral administration of SADE induced a decrease in body weight and adiposity index. A similar effect was shown by Chinchu et al. [ 5], who reported that 3.23 g/kg of Varanadi kashayam decocted extract for 6 weeks resulted in a significant decrease in organ weight compared to that of the high-fat diet group. ## 3.5.1. Effects of SADE on Blood Glucose Level Table 3 shows the average blood glucose level measured during 120 days in animal groups receiving LCD, LCD + SADE, HCD and HCD + SADE. At the baseline, the glycaemic level was similar between the different groups. From day 45 until the end of experimentation, glycemia values were significantly increased in the HCD group (140 ± 5 mg/dL) compared to the LCD (81 ± 4 mg/dL), LCD + SADE (65 ± 5 mg/dL) and HCD + SADE groups (73 ± 7 mg/dL) ($p \leq 0.05$) but animals remain not diabetic. SADE administration seems to modulate glycemia levels. Indeed, phenolic compounds possess redox potential and may act as antioxidants inducing hypoglycemic effects by enhancing glucose uptake [58]. ## 3.5.2. Effects of SADE on Serum Lipid Profile The results of serum lipid contents are shown in Figure 5. The serum lipid profile of the LCD and LCD + SADE animals remained almost stable during the 120 days of the experiment. The P. obesus subjected to an HCD showed a highly significant increase after 120 days of treatment in TC ($p \leq 0.05$) (Figure 5a), TG ($p \leq 0.05$) (Figure 5b), LDL ($p \leq 0.05$) (Figure 5d), atherogenic index (AtI) ($p \leq 0.05$) (Figure 5e) and a decrease in the levels of HDL ($p \leq 0.05$) (Figure 5c) compared to LCD, LCD + SADE and HCD + SADE groups. A high level of HDL was observed in HCD and HCD + SADE groups during the first three months. This is consistent with several previous studies which have shown that HCD (3.25–3.70 kcal/g) administration in P. obesus induced the development of obesity after two to three months and metabolic syndrome such as dyslipidemia after 16 weeks. Indeed, the metabolic syndrome and dyslipidemia seem to induce insulin resistance in peripheral tissues leading to an enhanced hepatic flux of fatty acids and forming adipose tissue resistant to the anti-lipolytic effects of insulin. High levels of serum TG observed in HCD groups are generally associated with increased VLDL secretion by which lipolysis could produce HDL [11]. This may explain the high levels of HDL observed in HCD rats during the 90 days before the SADE administration. The authors also noticed a decrease in HDL levels ranging from 15–$30\%$ following the end of long-term exposure to the HCD [28,53]. The prominent decrease of HDL in the HCD group may be attributed to the disturbances in lipid and associated lipoprotein metabolism and the advanced dyslipidemia stage reached on day 90. Many authors reported a strong correlation between overweight or generalized obesity assessed using BMI and the decrease in HDL [59,60,61,62]. It is well known that the increase in blood biochemical parameters such as TC, LDL, and TG and the decrease of HDL after 90 days are dangerous indicators that develop the risk of cardiovascular complications such as dyslipidemia, atherosclerosis, coronary heart disease, and myocardial infarction [62,63]. The administration of SADE to HCD rats (HCD + SADE group) during 30 days (from day 90 to day 120) induced a significant decrease ($p \leq 0.05$) in serum lipid biochemical parameters (Figure 5) and in AtI value (Figure 5e) compared to those of obese and dyslipidemic rats (HCD group). AtI is a useful indicator of the risk of cardiovascular complications [5]. In this study, HCD + SADE significantly reduced the AtI compared to HCD ($p \leq 0.05$) (Figure 5e), and this indicates its cardio protective potential. At the end of treatment (on the 120th day), no significant difference was observed between the main biochemical lipid parameters of HCD + SADE group compared to the LCD and LCD + SADE groups. Several approaches are proposed to reduce or suppress obesity and dyslipidemia, among them the use of natural herbal products with antioxidant activity [57]. S. arabica is not well investigated for its therapeutic use. Several species of Salicornia, such as S. herbacea, S. bigelovii, and Sarcocornia perennis, have been reported as presenting beneficial effects in vitro and in vivo, including antioxidant activity [25,43], hypolipidemic [23,64], anti-obesity [24,36], and immunomodulatory effects [65]. SADE resulted in rapid restoration of TC (Figure 5a), TG (Figure 5b), and LDL levels (Figure 5d). Indeed, a significant decrease from day 90 to day 120 in the level of TC, TG, and LDL was observed under the effect of SADE (HCD + SADE group). An increase in HDL (Figure 5c) was also observed in HCD + SADE group on day 120; it is well-known that a high serum level of HDL is a protective factor against vascular diseases. This result suggested that SADE exerts its anti-dyslipidemic effect (hypocholesterolemic and hypotriglyceridemic) on the HCD group. Similar studies reported the anti-hyperlipidemic effect of the S. bigelovii seed polysaccharide extract at 200 mg/kg body weight/day in hyper-cholesterol-fed rats [23]. Thus, in this study, it seems that SADE could have the capability to regulate lipid metabolism and the potential to reduce cardiovascular complications. Pichiah and Cha [24] reported a similar effect of S. herbacea supplementation in HCD rats. It has also been reported that administration of dried ethanolic extract of S. herbacea led to reducing weight gain and to a significant decrease of serum lipids in mice that exhibit type 2 diabetes and hyperlipidemia when prescribed for 10 weeks, with the suppression of genes linked to lipogenesis [64]. In addition, the flavonoids of this plant exert adipogenic inhibition in 3T3-L1 adipocytes [66]. Compared to other plants, oral administration of Varanadi kashayam decocted extract for a period of six weeks along with HCD to rats decreased the serum lipid profile [5]. Similar doses of Ephedra alata areal part (100 to 300 mg/kg/day) were reported to have positive effects on the reduction in blood lipid levels [67]. According to the literature, the hypocholesterolemic of SADE may be attributed to the catabolism of LDL and modulation of expression levels of genes related to lipid metabolism, as reported for other phenolic extracts [68]. Further molecular investigations will be completed to better understand the potent SADE role in the mechanism of regulation of lipids and its anti-obesity and anti-dyslipidemic effects. Recently, it was proved that phenolic compounds and phenolic extracts might contribute to reducing obesity and dyslipidemia by exerting different mechanisms. The main reported pathways of anti-obesity and dyslipidemic effects involving phenols are (i) enhancement of the in vivo antioxidant defense allowing protection of lipoprotein against oxidation and minimizing hepatic injury [13,14], (ii) inhibition of the key enzymes involved in carbohydrate (such as α-amylase) and fat metabolism (such as pancreatic lipase) which hamper the digestion and absorption of carbohydrates and fats in the small intestine [10,11], and (iii) decreasing of lipogenic adipogenic activities in liver and adipose tissues [1,9,44,69]. It was reported that gingerol [11] and betula utilis bark extract [10] induce the reduction of the absorption of fat and cholesterol by inhibiting the activity of pancreatic lipase. Similar effects were reported for gallic acid supplementation in rats fed with HFD. The improvement of antioxidant status contributes to reducing obesity. Similar effects were reported for the mixture extracts of Morus alba and Aronia melanocarpa against high-fat diet-induced obesity in C57BL/6J mice [9]. The authors showed that extract mixture exerts a synergistic effect against diet-induced obesity by decreasing expression levels of genes involved in lipid anabolism (SREBP-1c, PPAR-, CEBP, FAS, and CD36), increasing the expression levels of lipolysis-related genes in liver and adipose tissue and upregulated AMPK signaling. Feng et al. [ 1] showed, using transcriptome analysis and real-time quantitative RT-PCR, that heptamethoxyflavone supplementation in rats fed a high-fat diet markedly downregulated hepatic genes related to adipogenesis transcription and inflammatory responses and significantly upregulated genes related to fatty acid oxidation and energy expenditure. Similar hypolipidemic effects were reported for Coriandrum sativum L. in Meriones shawi rats fed high-fat diets [12]. ## 3.6. Effects of SADE on Liver Enzyme Markers and Liver Histology The serum levels of ASAT and ALAT corresponding to the four animal groups (LCD, LCD + SADE, HCD, HCD + SADE) are presented in Figure 6a,b. The histological changes of P. obesus liver tissues of animal groups assessed at the end of the experiment (four months) are shown in Figure 7. Compared to the LCD group, the activities of hepatic marker enzymes of ASAT (Figure 6a) and ALAT (Figure 6b) were significantly increased in the HCD group ($p \leq 0.05$). However, oral administration of SADE to obese and dyslipidemic P. obesus (HCD + SADE) induced a significant reduction in serum ALAT and ASAT on day 120 and showed no significant difference comparable to the LCD + SADE (Figure 6a) and LCD groups (Figure 6b). The HCD-induced body weight increase (Figure 2) causes changes in lipid balance (Figure 5a–e) and promotes transaminase enzyme activities of ASAT (Figure 6a) and ALAT (Figure 6b), inducing impaired liver function compared to control rats. Several reports demonstrated that the increase in ASAT and ALAT is a principal indicator of liver dysfunction and disturbances in the biosynthesis of these enzymes, with an alteration in the permeability of the hepatic membrane [70]. Indeed, Spolding et al. [ 71] showed that the P. obesus fed a cholesterol-supplemented standard rodent diet for four weeks, causing a significant increase in ASAT and ALAT levels. Antioxidants of SADE seem to reduce serum ASAT and ALAT, which represents a clear indication of the improvement of the functional status of the liver. Therefore, treatment with 300 mg SADE/kg per day for a month moderated the deleterious effects of the HCD. Indeed, SADE induced a significant decrease in the activities of ASAT (Figure 6a), and ALAT (Figure 6b) compared to the HCD group. These results are in agreement with those of Gargouri et al. [ 65]. The authors proved that treated rats with a dried extract of Sarcocornia perennis regulates the enzyme levels (ASAT and ALAT) and reduces the cell oxidative damage induced by lead. It is also reported that an aqueous extract of Salicornia shows an hepato-protective effect at a dose of 500 mg/kg in mice stressed by acetaminophen [72]. The increase of ASAT and ALAT activity levels and their decrease after SADE supplementation indicate the restoring effect of SADE supplementation, and this is in agreement with the analysis of the histology of the liver tissue (Figure 7). Indeed, the liver tissues of LCD and LCD + SADE groups presented normal architecture, with radiating organization of hepatocytes from the central vein and showed a normal portal triad (Figure 7a–b’’). Hepatic injury was observed in the liver tissue of the animals of HCD groups (Figure 7a–b’’). It was marked by strong ballooning hepatocytes characterized by enlarged size, pale color and the presence of numerous micro and macro-vesicular and intracellular lipid droplets. In addition, the liver sections of HCD groups (Figure 7b–b’’) showed very severe hepatotoxicity with hepatocyte necrosis and leukocyte inflammatory infiltration, mainly in the lobular and portal levels. The percentage of apoptotic cells, necrotic cells and lipid droplets (Figure 7c–e) was significantly increased in the liver tissue of the HCD group. The administration of SADE (HCD + SADE group) seems to be able to partially restore the hepatic morphology back to a normal state (regular size of hepatocytes, attenuation of inflammation and steatosis) and significantly decrease the percentage of apoptotic cells, necrotic cells and lipid droplets. Besides, oral administration of SADE didn’t induce any hepatotoxicity signs and limited HCD-induced hepatic steatosis. Similar observations were reported in HCD-induced obese P. obesus [44] after supplementation of spirulina. Similarly, Hsu et al. [ 13] reported the attenuation of inflammation and steatosis after antioxidant supplementation and this positive effect was attributed to the amelioration of oxidative status in the liver tissue. ## 3.7. Principal Components Analysis of Biochemical Parameters, Body Weight Gain Figure 8 showed the PCA biplot performed on body weight gain (% Pi), biochemical parameters (TG, TC, HDL, LDL, AtI) and ASAT and ALAT activities assessed for different animal groups on day 0 (the beginning of the experiment), day 90 (the beginning of SADE administration) and day 120 (the end of SADE administration and of the experiment). The biplot revealed that PCA described ~$94\%$ of the whole data variation through the first two components; respectively, PC1 explained $82\%$ of the variance, and PC2 accounted for an additional $12\%$ of the variance (Figure 8a,b). The first dimension was represented positively by % Pi (0.976), AtI [0925], LDL (0.972), TG (0.958), TC (0.988), ASAT (0.950) and ALAT (0.907). The biplot was divided into four quadrants (A, B, C, and D), where three clusters comprising animal groups with similar biochemical parameters, % Pi and the atherogenic index, and exhibiting similar ranges of ASAT and ALAT activities are distinguished. The first cluster (quadrant A) comprises LCD, LCD + SADE and HCD + SADE animal groups on day 120. The second group (quadrant B) contains the four animal groups (LCD, LCD + SADE, HCD, HCD + SADE) on day 0 (the beginning of the experiment) and two LCD groups on day 90 (LCD and LCD + SADE). The third group (quadrants C and D) is represented by three animal groups (HCD and HCD + SADE on day 90) and HCD on day 120. The latter cluster is positively correlated to % Pi, ASAT and ALAT activities and lipid parameters. Table 4 shows the correlation matrix between all measured parameters. The average body weight gain, % Pi, is positively correlated to AtI, all lipid biochemical parameters and ASAT and ALAT (R2 ≥ 0.836). At the same time, a weak correlation was recorded with HDL content (0.383 ≤ R2 ≤ 0.476). Strong positive correlations (R2 ≥ 0.809) were also observed between AtI, all lipid parameters (except HDL) and ALAT and ASAT. These results are in agreement with biochemical analyses shown above and confirm the strong positive correlation between TC, TG, HDL, LDL, % Pi and levels of hepatic enzymes markers, whereas HDL seems to be weakly correlated to all measured parameters. ## 4. Conclusions One-month oral administration of 300 mg of the S. arabica decocted extract (SADE)/kg per day to obese and dyslipidemic P. obesus fed a high-calorie diet induced a significant decrease in body weight, body mass index, food intake and energy intake, liver relative weight and adiposity index. SADE supplementation in the P. obesus diet induces, in the high-calorie diet animals group, significant hypocholesterolemic and hypotriglyceridemic effects with a significant decrease in atherogenic index. 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--- title: From Foxtail Millet Husk (Waste) to Bioactive Phenolic Extracts Using Deep Eutectic Solvent Extraction and Evaluation of Antioxidant, Acetylcholinesterase, and α-Glucosidase Inhibitory Activities authors: - Chunqing Wang - Zhenzhen Li - Jinle Xiang - Joel B. Johnson - Bailiang Zheng - Lei Luo - Trust Beta journal: Foods year: 2023 pmcid: PMC10048580 doi: 10.3390/foods12061144 license: CC BY 4.0 --- # From Foxtail Millet Husk (Waste) to Bioactive Phenolic Extracts Using Deep Eutectic Solvent Extraction and Evaluation of Antioxidant, Acetylcholinesterase, and α-Glucosidase Inhibitory Activities ## Abstract Foxtail millet husk (FMH) is generally removed and discarded during the first step of millet processing. This study aimed to optimize a method using deep eutectic solvents (DESs) combined with ultrasonic-assisted extraction (UAE) to extract phenols from FMH and to identify the phenolic compositions and evaluate the biological activities. The optimized DES comprised L-lactic acid and glycol with a 1:2 molar ratio by taking the total flavonoid content (TFC) and total phenolic content (TPC) as targets. The extraction parameters were optimized to maximize TFC and TPC, using the following settings: liquid-to-solid ratio of 25 mL/g, DES with water content of $15\%$, extraction time of 41 min and temperature of 51 °C, and ultrasonic power at 304 W. The optimized UAE-DES, which produced significantly higher TPC, TFC, antioxidant activity, α-glucosidase, and acetylcholinesterase inhibitory activities compared to conventional solvent extraction. Through UPLC–MS, 12 phenolic compounds were identified, with 1-O-p-coumaroylglycerol, apigenin-C-pentosyl-C-hexoside, and 1-O-feruloyl-3-O-p-coumaroylglycerol being the main phenolic components. 1-O-feruloyl-3-O-p-coumaroylglycerol and 3,7-dimethylquercetin were identified first in foxtail millet. Our results indicated that FMH could be exploited by UAE-DES extraction as a useful source of naturally derived antioxidants, along with acetylcholinesterase and α-glucosidase inhibitory activities. ## 1. Introduction Minimizing waste production in the agricultural and food sectors has been a significant topic of interest in recent years [1]. The current processing methods for plant foodstuffs, including grains, fruits, and vegetables, tend to generate large amounts of waste materials, including husks, seeds, peels, and other by-products [2,3]. However, these agricultural waste materials may contain high levels of valuable bioactive compounds, making them prospective feedstocks for the extraction and recovery of key compounds, including polyphenols, flavonoids, pectin, and dietary fiber [4]. Foxtail millet has been cultivated for over 8000 years, and China is considered to be the place of origin [5]. Foxtail millet is widely planted and is one of the major grain crops in Northern China [6]. Foxtail millet husk (FMH) is the outer layer of the seed and is generally removed and discarded to be a by-product during the first step of the millet processing. However, FMH is one of the prospective sources of polyphenols and other active ingredients, as previous research has demonstrated that the husk and aleurone layers of most cereal grains showed the highest concentration of phytochemicals [7]. Phenolics in foxtail millet present anti-proliferation effects on HT-29 human colon adenocarcinoma cells, breast cancer cells, and HepG2 liver cancer cells [8,9]. The extract from millet husk has also been used to synthesize silver nanoparticles, and the silver nanoparticles had antibacterial activity [10]. In our previous research, we reported that the husk contained a significantly higher content of phenolics compared with the dehulled millet used for cooking [11]. Therefore, it is necessary to develop optimized procedures to extract these valuable phenolics, allowing for value-added utilization of FMH. In the two decades since it was first used by Abbott [12], the use of deep eutectic solvents (DESs) had gained more and more traction as a novel extraction procedure that was more green and sustainable. This technique has also been widely used for extracting polyphenols from plants or plant waste products, such as tea leaves, saffron processing wastes, Moringa oleifera leaves, and chestnut-shell waste [13,14]. The main advantages of DESs over conventional organic solvents include their low cost, widespread availability, biodegradability, efficiency, and environmental friendliness [15]. However, future studies need to be performed on the toxicity of the extract before it can be used for food application [16]. Ultrasonic-assisted extraction (UAE) is a very good auxiliary extraction method, and it has the advantages of a shorter extraction time, improved extraction efficiency, and better environmental protection. Furthermore, the highly efficient combination of UAE and DES has also gained a lot of attention because of the efficiency in extraction of high yields of bioactive compounds from plant matrices [17]. However, investigations on the green extraction method and utilization of phenolics from FMH, including its phenolic components and their bioactivities, have not been reported. Consequently, the main objectives of this study are to [1] optimize a green and efficient UAE-DES method to extract the phenolics from FMH; and [2] determine the phenolic compositions, antioxidant activity, and acetylcholinesterase and α-glucosidase inhibitory activities of FHM phenolics extracted, using DESs. ## 2.1. Raw Material and Reagents Foxtail millet husk (dry) was acquired from Jinsu Agricultural Technology Corporation (Henan Province, China). The sample was smashed (RS-FS1401 mill, Royalstar Co., Ltd., Hefei, China) sieved (40-mesh), and then stored at −20 °C. Betaine and L-lactic acid were obtained from Shanghai Macklin Biochemical Co., Ltd., (Shanghai, China). Acetylcholinesterase, α-glucosidase (from Saccharomyces cerevisiae), and standards (vanillic acid, p-hydroxybenzoic acid, p-hydroxybenzaldehyde, caffeic acid, p-coumaric acid, ferulic acid, syringic acid, rutin, and kaempferol, the purity above $98\%$) were sourced from Shanghai Yuanye Biotechnology Co., Ltd., (Shanghai, China), while ABTS(2,2-azinobis-(3-ethylbenzothiazoline-6-sulfonate)), DPPH(2,2′-diphenyl-1-picrylhydrazyl), Folin–Ciocâlteu reagent, Trolox (6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid), and TPTZ(2,4,6-tris(2-pyridyl)-s-triazine) were acquired from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA). Chromatographic methanol and formic acid were purchased from Thermo Fisher Scientific Reagent Co., Ltd., (Waltham, MA, USA). All other reagents, including glycerol, sodium carbonate (Na2CO3), sodium acetate, sodium hydroxide (NaOH), sodium nitrite (NaNO2), glycol, aluminum trichloride (AlCl3), ferric chloride (FeCl3), and methanol, were purchased from Tianjin Deen Chemical Reagent Co., Ltd. (Tianjing, China). ## 2.2. Preparation of DES The preparation of DESs followed the method reported by Zheng et al. [ 18]. A range of different hydrogen bond donors (HBDs) and hydrogen bond acceptors (HBAs) were investigated in different molar ratios, as detailed in Table 1. To prepare each DES, the combined HBA and HBD was stirred continuously at 80 °C, until the solution developed a transparent, uniform appearance. ## 2.3. Extractions of Phenolic Compounds A total of 0.2 g of milled FMH was combined with 2 mL of the DES (with $20\%$ water content) in a 10 mL centrifuge tube. Extraction was conducted by sonicating the centrifuge tubes for 30 min (50 °C temperature; 300 W ultrasonic power). The supernatants, which included the extracted polyphenolic compounds, were obtained by centrifuging the extracts at 8000 rpm for 10 min. For comparison, the ultrasonic-assisted conventional solvent extraction used the same process of UAE-DES but with $80\%$ methanol solution as the solvent. ## 2.4.1. Single-Factor Experiments The single factors were water content of DES, extraction time, liquid-to-solid ratio, ultrasonic power, and extraction temperature, and all were explored by single-factor experiments. The ranges and gradients of each factor were selected from preliminary experimental work. The water content of the DESs ranged from 10 to $35\%$, the liquid-to-solid ratio ranged between 10 and 35 mL/g, the water-bath temperature varied from 30 to 80 °C, the extraction time was trialed between 10 and 60 min, and the ultrasonic power was set at 200 to 450 W. ## 2.4.2. Response Surface Methodology (RSM) To explore the interactions of these independent variables on TPC and TFC, an RSM experiment was conducted. This used a 4-factor, 3-level Box–Behnken design. The four independent variables were the DES water content (X1), extraction temperature (X2), extraction time (X3), and ultrasonic power (X4). Responses Y1 and Y2 were the TPC and TFC of the extracts, respectively. Regression equations were calculated using the software of Design Expert 8.0.6 (11.0.1.0 64-bit, State-East Corporation). Regression coefficients were calculated from the experimental results, using the second-order polynomial model (Equation [1]). The performance of the constructed models was evaluated through a range of parameters, including their F-value, p-value, R2 value (coefficient of determination), and R2adj and R2pred values (adjusted and predicted coefficient of determination, respectively) [19]. A desirability function approach was used to calculate the optimal input variable values in order to maximize the yield of TPC and TFC. Finally, the theoretical results were verified by conducting triplicate extractions, using the optimal conditions, and comparing the experimental and predicted TPC and TFC. [ 1]γ=β0+∑$j = 1$kβjxj+∑$j = 1$kβjjxj2+∑i∑<$j = 2$kβiixixj+ei ## 2.5. Determination of TPC and TFC The TPC of the extracts was quantified by the Folin–Ciocâlteu colorimetric method, according the method of Zhang et al. [ 11]. The results were expressed in milligrams of ferulic acid equivalents (FAE) per gram of the FMH sample (mg FAE/g). The TFC was measured by the aluminum chloride colorimetric method, as outlined by Xiang et al. [ 20]. The TFC was calculated as mg of rutin equivalents (RE) per gram of sample (mg RE/g). ## 2.6.1. DPPH Radical Scavenging Activity The measure of the radical scavenging DPPH activity of the FMH extracts was conducted using our existing methods [21]. The results were expressed in micromoles of Trolox equivalents (TE) per gram of sample (μmol TE/g). ## 2.6.2. ABTS+ Scavenging Activity About ABTS+ free-radical scavenging activity, the extract was measured by a method previously described [21]. Again, results were expressed as μmol TE/g of sample. ## 2.6.3. Ferric Reducing Antioxidant Power (FRAP) To gain a full picture of a sample’s antioxidant activity, radical scavenging activity alone may not be sufficient. Consequently, the extracts’ reducing capacity was also quantified by the FRAP assay described by Zhang et al. [ 11], with results quantified as μmol TE/g of sample. ## 2.7. Inhibition of Acetylcholinesterase (AChE) Activity The ability of the extracts to inhibit AChE activity was evaluated by the reported method [18]. Briefly, various concentrations of polyphenolic extracts were combined with 200 mmol/L phosphate buffer at pH 7.7 (200 μL), DTNB (80 μL), and 2 U/mL AChE enzyme (10 μL). After 5 min (at 25 °C), 15 μL of acetylthiocholine iodide substrate was added, the samples were left to sit for another 5 min, and then the absorbance measured at 405 nm. AChE inhibitory activities were expressed as IC50 values, using the calculated inhibition percentage, I (%) (Equation [2]). [ 2]I=Abackground−Ablank control−Asample−Asample controlAbackground−Ablank control where Abackground, Ablank control, Asample, and Asample control are the absorbances measured for $100\%$ enzyme activity (enzyme and solvent), the blank group (enzyme only), the test sample group (enzyme and sample), and the sample control group (sample only), respectively. ## 2.8. Inhibition of α-Glucosidase Activity The α-Glucosidase inhibitory activity was determined with minor modifications to the method described by Li et al. [ 22]. In brief, various concentrations of polyphenol fractions (20 μL) were combined in a 96-well microplate with α-glucosidase enzyme (5 U/mL; 20 μL) and sodium phosphate buffer (0.2 M at pH 6.9; 120 μL). After 15 min of incubation (37 °C), the pNPG substrate (2.5 mM; 20 μL) was added before further incubation (10 min). Finally, 0.2 M sodium carbonate solution (80 μL) was added to terminate the reaction, before the resultant absorbance was measured at 405 nm. The results were calculated and expressed as IC50 values. ## 2.9. Identification and Quantification of Phenols by UPLC–MS/MS To identify and quantify the phenols of the FMH extracts, exploratory profiling was conducted using the Waters H-Class UPLC system in series with a QqQ-MS (Waters Xevo TQ-S/micro). For the sample preparation before analysis, D101 macroporous resin was applied to separate the extract, according to method optimized by Zheng et al. [ 23]. The method used an Accucore C18 column (2.6 μm, 100 mm × 3 mm; Thermo Fisher Scientific, Waltham, MA, USA), injection volume with 10 μL, column temperature at 25 °C, and flow rate of 0.4 mL/min. The mobile phase comprised phase A (water) and phase B (chromatographic methanol), respectively, containing $0.1\%$ formic acid, and used a 25-min gradient elution by the method of Yuan et al. [ 24]. The MS/MS information was collected in negative mode across the mass range of 100–1000 in a resolution of 5000. Phenolic components were identified with authentic standards where available, or otherwise tentatively identified through comparison of the UV spectra and MS/MS information in the literature. To quantify the polyphenolic compounds, their peak areas were used at specific wavelengths: 280 nm for p-hydroxybenzaldehyde, 320 nm for p-coumaric acid and ferulic acid, and 350 nm for flavonoids. If a standard was not available for a particular compound, it was quantified as the equivalent concentration of its corresponding aglycone or closest analogue. All phenolic concentrations were expressed as mg/g of sample. ## 2.10. Surface Morphology Analysis The surface morphology of the FMH cell structure was analyzed by scanning electron microscope (SEM), as described in Zheng et al. [ 18]. Following the extractions using the optimized UAE-DES and the UAE conditions, the remaining residues were air-dried naturally before being mounted on aluminum stubs. After coating these with gold-palladium, the Hitachi TM3030Plus SEM (HITACHI High-Technologies Corporation, Tokyo, Japan) was used to visualize the surface morphology. ## 2.11. Statistical Analysis All of the experiments were repeated three times. Consequently, the results are expressed as mean ± standard deviation. The data of results were analyzed in IBM SPSS Statistics (Version 25.0, IBM Corp., Armonk, NY, USA) by the analysis of variance (ANOVA), followed by Duncan’s multiple range test and t-tests to determine statistically different results. A significance level of $p \leq 0.05$ was taken as statistically significant. ## 3.1. Selection of DES It has previously been found that, for phenolic acids and flavonoid compounds, sugar-based DESs have a lower extraction efficiency compared to amide, acid, and alcohol-based DESs [12,15,25]. According to the pre-experiments, the same DES showed different effects on the extraction of phenolic acids and flavonoids, so the TFC and TPC of the extracts were used to evaluate the extraction efficiency. The TPC and TFC obtained after extracting FMH polyphenolics using eight different types of DESs are shown in Figure 1. As anticipated, both the TPC and TFC of FMH extracts varied widely depending on the extraction solvents used. Consequently, it can be stated that the DESs of different chemical natures differed significantly in their phenolic acid and flavonoid extraction capacities [13]. It could be seen that the DESs 1, 2, 4, 5, 6, and 8 gave excellent results on TPC. For TFC, DES-4 and DES-8 showed better results. There was no significant difference between DES-4 and DES-8 on TPC and TFC. DESs based on organic acids have higher polarity than DESs based on polyols and sugars [26]. Solvent polarity can influence the extraction efficiency since more polar compounds, such as polyphenols, obtained better extraction performance with eutectic solvents containing organic acids, so DES-4 and DES-8 showed a good effect in TPC extraction. Compared with DES-8, which is made of sodium acetate and L-lactic acid, the viscosity of DES-4, comprising L-lactic acid and glycol with a 1:2 molar ratio, was lower, and the flowability was better. Therefore, DES-4 was designated as the optimal solvent for extracting FMH phenolics. ## 3.2.1. Water Content of DES One of the easiest to alter—but most influential—factors influencing the properties of a DES is its water content [27]. Figure 2A displays the change in the extraction efficiency of phenolics for DESs with a different water content. The results of TPC and TFC showed a similar trend, beginning with an increase in yield between 10 to $15\%$ water content, followed by a moderate decrease after $15\%$. The significant initial increase ($p \leq 0.05$) of extraction yield of phenolics might be attributable to the weakening of the hydrogen bonds. The increased amount of water in the DES could decrease the solvent viscosity and increase extraction yield [28]. Conversely, the significant decreases ($p \leq 0.05$) in extraction yield between 15 to $35\%$ water content were likely a consequence of reduced solubility of moderately polar phenolics in the DES matrix with increasing polarity [29]. ## 3.2.2. Liquid-to-Solid Ratio In contrast to the results observed for water content, the influence of liquid-to-solid ratio on TPC and TFC showed an asymptotical relationship (Figure 2B). With the increase of liquid-to-solid ratio, the TPC and TFC increased significantly up to 25 mL/g. However, there was no significant increase between 25 and 35 mL/g ($p \leq 0.05$), potentially indicating that maximum extraction efficiency had been reached. A similar trend was also observed by previous researchers extracting flavones from soy [30]. With the increase of the liquid–solid ratio, the dissolution amount of solute in solution will increase. Therefore, the phenolic concentration of the DES extract increased with the increase of the liquid–solid ratio. When the liquid–solid ratio increased to a certain extent, the dissolution number of phenolic compounds reached the maximum. Using an excess of extraction solvents produces unnecessary solvent waste and may actually hinder the recovery of phenolics; hence, a 25 mL/g liquid-to-solid ratio was selected as optimum and used in the following experiments. ## 3.2.3. Extraction Time The third variable investigated was extraction time. Initially, the increase of the extraction time provided a continuous and almost-linear increase in TPC and TFC (Figure 2C). This indicates that the ultrasonic treatment provided effective disruption of the FMH cell walls and internal vacuoles, allowing the polyphenols to diffuse out of the cells and into the solvent solution. Furthermore, during these shorter extraction times, there would be minimal diffusion resistance acting to prevent the polyphenols from exiting the intracellular environment. However, as the extraction time was past 40 min, the TPC and TFC started to decrease, possibly due to decomposition of some of the unstable polyphenol components when subjected to longer extracting times and relatively higher temperature [18]. ## 3.2.4. Extraction Temperature The extraction temperature also affected the TPC and TFC yield significantly, as presented in Figure 2D. As the temperature increased, the TPC and TFC yield initially rose before both reached a maximum at 50 °C and fell off after this peak. Another study using acidified water to extract anthocyanins from blue honeysuckle berries reported similar results, with the highest yield at a temperature of 40 °C [28]. Higher temperatures are likely to increase polyphenol solubility in the DES and also increase the diffusion process of polyphenols from the intracellular environment into the DES. However, after a certain point, then the heat-sensitive polyphenols will begin to decompose due to the high temperature. ## 3.2.5. Ultrasonic Power Figure 2E shows an increasing trend in TPC and TFC as the ultrasonic power was raised from 200 to 300 W, reaching the highest TPC and TFC at 300 W power. Ultrasonic-assisted extraction uses ultrasound waves to induce localized pressure and cavitation regions in the matrix, which helps break up cell structures and release the phenolic compounds into solution. Consequently, it would be anticipated that increased ultrasonic power would give rise to an increase in extraction efficiency. However, as with extraction temperature, increasing the ultrasonic power beyond 300 W led to a reduction in TPC and TFC, again most likely due to the degradation of the polyphenols through a combination of direct ultrasonic energy and the higher water bath temperatures associated with ultrasonic activity [28]. It is worth noting that the TFC did not show as sharp a decline as TPC with increasing ultrasonic power, potentially indicating the greater stability of this compound class. ## 3.3. Fitting the Model Table 2 shows the results for the 29 runs of TPC (Y1) and TFC (Y2) under different extraction conditions. To check the fit of the regression equations and quadratic polynomial model of yield, t-tests and an ANOVA were used, respectively. The p-value represented the significance of the variable; the smaller the p-value expressed, the more significant the impact of the variable on the results. The F-values were used to assess the relative contribution of each factor to the TFC and TPC yield [31], as shown in Table 3. The 3D plots and corresponding contour plots created based on the model are given in Figure 3 for directly displaying the effects of significant interaction terms on the responses of TPC and TFC. In the 3D diagram, the inclination of the surface is related to the influence of two interactive factors on the response value. The higher the inclination, the more significant the interaction between the two. The values on each curve in the response surface contour plots are the same. The color of the graph changes from blue to red, indicating that the value changes from small to large. ## 3.3.1. Total Polyphenol Content There was a significant positive correlation between the TPC response values and those calculated by the regression model detailed in Equation [3] ($p \leq 0.001$). As can be seen in Table 3, three factors had significant linear impacts on TPC ($p \leq 0.05$), namely the extraction time (X3), extraction temperature (X2), and ultrasonic power (X4) (in order of significance). Furthermore, four investigated factors all showed significant quadratic effects on the resultant TPC ($p \leq 0.001$). The only significant ($p \leq 0.05$) interactive effects on TPC were found for the X2X3 and X2X4 interaction terms. The non-significant factors were removed, and the predicted values of TPC were calculated using Equation [3]. [ 3]Y1=7.23+0.53X2+0.70X3−0.13X2X3−0.40X2X4−0.26X12−0.24X22−0.25X32−0.24X42 The variance analysis of the response surface displayed very high correction coefficients of R2 = 0.970 and R2Adj = 0.939; the R2 were in reasonable accord with the R2Adj (the difference is less than $20\%$), without significant lack of fit in the Equation [3] ($p \leq 0.05$). These data indicated that the model results were accurate; thus, it could be applied to predict TPC results when using DES-based UAE on foxtail millet husk [19]. In the polynomial Equation [3], the interaction term of X2X3 had a negative impact on TPC values ($p \leq 0.05$), indicating that, for short extraction times, higher temperatures gave a higher TPC, whereas higher temperatures lowered the TPC at longer extraction times. The interaction of these two factors (X2X3) can be seen in the 3D and contour plots in Figure 3A and Figure 3B, respectively. Similarly, the TPC was significantly negatively related to the X2X4 interaction ($p \leq 0.05$), suggesting that, with increasing ultrasonic power, the TPC gradually increased (within a certain temperature range). However, exceeding this range, the TPC gradually decreased with increasing ultrasonic power. The 3D plot and corresponding contour plot showing the interaction of temperature and ultrasonic power (X2X4) are presented in Figure 3C and Figure 3D, respectively. ## 3.3.2. Total Flavonoid Content As detailed in Table 3, the results from the ANOVAs indicated significant linear (X1 and X3), quadratic (X12, X22, X32, and X42) and interactive (X1X4 and X2X4) effects on TFC. Based on the regression coefficient (F) values, quadratic terms (X12, X22, X32, and X42) revealed the major effects, which were followed by X1, X3, X1X4, X2X4, and X4. The non-significant items were removed, and the formula for predicting TFC values is given in Equation [4]. [ 4]Y2=4.18+0.17X1+0.50X3−0.25X1X4−0.14X2X4−0.25X12−0.26X22−0.25X32−0.27X42 As observed for TPC, the developed regression model (Equation [4]) was strongly correlated with the TFC values ($p \leq 0.001$). The response surface variance analysis displayed high correction coefficients of R2 = 0.925 and R2Adj = 0.920, with a good equation fit ($p \leq 0.05$ for lack of fit). Subsequently, this supported the use of this polynomial model for analyzing and predicting TFC extraction efficiencies using UAE-DES. The interaction of X1X4 and X2X4 showed significant negative effects ($p \leq 0.05$) on TFC, meaning that the TFC gradually increased with increasing water content across a certain range of water contents, but exceeding this range, the TFC gradually decreased with the increase of ultrasonic power. Similarly, the interaction of ultrasonic power and extraction temperature on TFC showed the same trend. Figure 3E,F present the 3D plot and matching contour plot for the interaction between the DES water content and the extraction ultrasonic power (X1X4) on TFC. In the same vein, the interaction of the extraction temperature and ultrasonic power (X2X4) is shown in the 3D and contour plots in Figure 3G,H, respectively. ## 3.3.3. Experimental Validation of the Model The function models enabled the simultaneous optimization for the four extraction variables to provide the highest TPC and TFC. Consequently, the highest theoretical extraction yield was predicted to occur by using the following settings: water content of $15\%$, ultrasonic power of 304 W, 51 °C extraction temperature, and 41 min extraction time. The desirability value was 0.826 within a specific range (0.6–1) acceptable. With the conditions, the predicted values were 7.25 mg FAE/g DW for TPC and 4.18 mg RE/g DW for TFC. For the experimental model validation conducted using the optimized parameters for extraction outlined above, the TPC and TFC were determined to be 7.38 mg FAE/g DW and 4.30 mg RE/g DW. This demonstrated remarkable correlation between the experimentally derived and theoretically predicted values, confirming the predictive accuracy of the model. Furthermore, it supported the use of the optimized extraction protocol for extracting polyphenol compounds from FMH. ## 3.4. TPC, TFC, and Biological Activities In Vitro Table 4 shows the comparisons between the antioxidant activity, TPC, TFC, α-glucosidase and acetylcholinesterase inhibitory activity of the extracts using the UAE-DES and UAE. The UAE-DES method gave a significantly higher TPC and TFC compared to UAE, suggesting that the UAE-DES had a better extraction efficiency than UAE. It may be that the DESs exhibit a high degree of solubility for phenolic compounds due to their ability to form hydrogen bonds with these solutes, which can be dissolved to a greater extent under the assistance of ultrasound [29]. Table 4 also shows that the extracts obtained using the UAE-DES showed higher antioxidant activity than that of UAE across all three of the different in vitro antioxidant assays used in this work. The scavenging ABTS·+ capacity of the phenolic extract by UAE-DES was 13.99 μmoL TE/g, almost twice as much as that of the UAE method, and the results of DPPH and FRAP assays presented similar situations. Generally, there was a significant positive correlation between the extractable phenolic content and the antioxidant activity of the sample [32,33]. As a result, the antioxidant activity of the UAE-DES extracts, which presented a significantly higher TPC and TFC, was higher than that of the extracts obtained byUAE. In recent years, AChE inhibitors have gained increasing attention, and it has been found that some natural polyphenol compounds have certain inhibitory effects on AChE [34,35]. As shown in Table 4, the IC50 of the UAE-DES and UAE extracts were 295.53 μg/mL and 403.51 μg/mL, respectively. The lower IC50 of the UAE-DES extracts suggested stronger inhibitory AChE activity, likely due to the corresponding higher TPC and TFC [36]. The findings indicate that the polyphenol extracts from FMH have the ability to inhibit AChE activity and therefore can be utilized as a potential resource for natural AChE inhibitors. Some chemical drugs, such as acarbose and voglibose, are widely applied to manage type II diabetes, mainly used to inhibit the activity of α-glucosidase; however, some side effects have been found in their applications [37]. Some studies have confirmed that α-glucosidase inhibitors were extracted from cereal products, which have fewer side effects [38,39]. The α-glucosidase inhibitory effect of the extracts from FMH was investigated, and the results are provided in Table 4. The IC50 values of the polyphenol compounds’ extracts were determined to be 190.12 μg FAE/mL and 280.22 μg FAE/mL by UAE-DES and UAE, respectively. The lower IC50 of the UAE-DES extracts suggested stronger inhibitory activity against α-glucosidase. The inhibitory activity against starch digesting enzymes are attributed to phenolic acids and flavonoids [40,41], and therefore the higher TPC and TFC actively resulted in the higher α-glucosidase inhibitory activity [42]. This might indicate that the abounding polyphenol compounds of FMH could be explored as one of potential sources for natural alternative products to manage type II diabetes. ## 3.5. Identification and Quantification of Phenolic Components The mass spectral (MS2) information and UV spectral characteristics of those compounds were compared with the authentic standards or related references, and twelve polyphenol compounds were identified in the FMH extracts. The UPLC profile of polyphenol compounds of FMH at wavelength of 280 nm is shown in Figure 4. The retention time (RT), UV spectral characterizations, MS/MS fragments data, and contents of individual polyphenol compounds are listed in Table 5. The peaks were numbered by their elution times. Compounds 1, 2, 3, 4, 5, and 10 in the extracts were confirmed as p-hydroxybenzoic acid, p-hydroxybenzaldehyde, vanillic acid, chlorogenic acid, and ferulic acid, respectively, by comparing RT, UV spectrum and MS/MS fragments data with those of authentic standards. Compound 6 presented [M-H]− at m/z 237 and the major MS/MS fragments at m/z 163,145, and 119. The daughter ion presented at m/z 119 was a typical fragment of p-coumaric acid produced by the loss of CO2 (m/z 44), and the predominant fragment at m/z 145 corresponded to the loss of glycerol (m/z 92), so compound 6 was identified as 1-O-p-coumaroylglycerol by comparing its mass spectra with previously reported results [18,43,44]. Compound 7 exhibited a molecular ion at m/z 593 and displayed typical fragmentations of C-glycosides 503 ([M-H-90]−), 473 ([M-H-90-30]−) and 353 ([M-H-90-30-120]−), and it was identified as apigenin-C-dihexoside. Compound 9 presented deprotonated molecular ions [M-H]− of m/z 563, which was 30 amu lower than the deprotonated molecular ion of compound 7. It also had typical C-glycoside fragmentations of 473 ([M-H-90]−) and 443 ([M-H-90-30]−), and therefore compound 9 was assigned as apigenin-C-pentosyl-C-hexoside. Compound 7 and compound 9 had been previously identified in millets [45]. Compound 8 exhibited an m/z signal at 436 and MS2 fragments at m/z 316, 273, 193, 145, and 119. It was identified as hydroxycinnamic acid amide, named N′, N″-di-p-coumaroylspermidine, which had been reported in foxtail millet and peanut flowers [18,45,46]. Compound 11 exhibited a deprotonated molecular ion [M-H]− at m/z 413, and the main MS/MS fragments exhibited at m/z 237, 267, 163, and 193, showing the MS/MS signals of p-coumaroylglycerol, feruloylglycerol, p-coumaric acid, and ferulic acid; therefore, compound 11 was identified as 1-O-feruloyl-3-O-p-coumaroylglycerol, which has been reported in Ananas comosus L. leaves and Lilium [47,48]. To our knowledge, this is the first report to find 1-O-feruloyl-3-O-p-coumaroylglycerol in foxtail millet or its byproducts, and its UV spectrum and MS2 fragments are shown in Supplementary Figure S1. Compound 12 presented [M-H]− at m/z 329; the main MS2 fragments at m/z 314, 299, 285, 271 and 227; and the UV spectrum and MS2 fragments are shown in Supplementary Figure S2. Therefore, compound 12 was identified as 3,7-dimethylquercetin, which is being reported in FMH for the first time but has been reported in seaweed [49]. The contents of individual polyphenol compounds are presented in Table 5. 1-O-feruloyl-3-O-p-coumaroylglycerol was the predominant phenolic component, as it showed the highest levels in both UAE-DES and UAE extracts, with the values of 890.27 μg/g and 508.20 μg/g, respectively. The other main phenolic components included apigenin-C-pentosyl-C-hexoside, 1-O-p-coumaroylglycerol, 3,7-dimethylquercetin, and p-coumaric acid. However, the contents of the individual phenolics in the extracts by UAE-DES were all significantly higher than those of the extracts by UAE. The sum of the individual polyphenol compounds in the UAE-DES extracts was far higher than that of the UAE extracts; the results were consistent with the TPC and TFC. ## 3.6. Scanning Electron Microscope Analysis The aim of using scanning electron microscope to observe the microstructures of the FMH cells before and after the extractions is to observe the differences between the UAE-DES and UAE, and the results are shown in Figure 5. Compared to the observation result of the raw FMH sample (Figure 5A), the cell architecture of the FMH treated with UAE-DES (Figure 5B) was significantly damaged, and the sample surface showed many pores and cracks. In the case of the extract by UAE, the pores and cracks were smaller than those observed with UAE-DES, and some of the structure remained relatively undisturbed (Figure 5C). This may be due to the combined action of the cavitation from ultrasound and infiltration into the cell structure by DESs [50]. This lends further support to UAE-DES as a green and efficient method to extract the bioactive phenolic components from FMH. ## 4. Conclusions The DES composed of L-lactic acid and glycol in a 1:2 molar ratio was screened as a suitable solvent for extraction phenolic from FMH, and the optimized extraction conditions were as follows: liquid-to-solid ratio of 25 mL/g, DES with water content of $15\%$, extraction temperature of 51 °C, extraction time of 41 min, and ultrasonic power at 304 W. The extracts of FMH were composed of twelve phenolic compounds, and we found that p-coumaric acid, 1-O-p-coumaroylglycerol, apigenin-C-pentosyl-C-hexoside, 1-O-feruloyl-3-O-p-coumaroylglycerol, and 3,7-dimethylquercetin exhibited higher concentrations. The DES extracts had higher antioxidant, α-glucosidase, and acetylcholinesterase inhibitory activities than UAE ones. The SEM illustrated the mechanisms behind the efficient extraction of the optimized UAE-DES. 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--- title: 'Patient’s and Practionner’s Experiences of a First Face-to-Face vs. Remote Orthodontic Consultation: A Randomized Controlled Trial' authors: - Carole Charavet - Fiona Rouanet - Sophie Myriam Dridi journal: Healthcare year: 2023 pmcid: PMC10048591 doi: 10.3390/healthcare11060882 license: CC BY 4.0 --- # Patient’s and Practionner’s Experiences of a First Face-to-Face vs. Remote Orthodontic Consultation: A Randomized Controlled Trial ## Abstract [1] Aim: The purpose of this study was to assess patients’ and practitioners’ reported experience measures (PREMs) following a first standard orthodontic consultation (face-to-face consultation) versus a first orthodontic teleconsultation (video-assisted remote orthodontic consultation).; [ 2] Materials and Methods: This study was designed as a randomized controlled trial in which 60 patients were randomly allocated to two groups. In the control group, patients received a first face-to-face consultation ($$n = 30$$) whereas, in the test group, patients received a first orthodontic teleconsultation ($$n = 30$$). Patients as well as the orthodontic practitioners completed questionnaires after the experience. [ 3] Results: From the patients’ point of view, overall satisfaction was comparable between the control group and the test group ($$p \leq 0.23$$). Quality of communication with the clinician, understanding of the explanations provided and a sense of privacy were also comparable between the two groups. However, from the practitioners’ perspective, overall satisfaction after the face-to-face consultation was significantly higher than after the first remote consultation ($p \leq 0.01$). [ 4] Conclusions: In the context of a first orthodontic consultation, teleorthodontics appears to be an interesting and complementary approach to a classical face-to-face appointment, but which should by no means become systematic. ## 1. Introduction Since the 20th century, the use of new technologies has increased exponentially and the development of telemedicine [1,2,3,4,5,6,7,8,9] is naturally part of this trend. Remote management of orthodontic treatment has also been introduced and is known as “teleorthodontics ” [10]. The sudden onset of the COVID-19 pandemic has boosted remote medical care [11,12,13,14,15,16,17,18,19,20,21,22,23], including orthodontics. In a study published by Saccomanno et al. [ 24], teleorthodontics are shown to be a valuable tool for orthodontic care follow-up, in the context of an emergency as well as in normal circumstances, to save time and money without unduly reducing quality of care. More recently, in 2022, Saccomanno et al. [ 25] conducted a systematic review on the utility of teleorthodontics for orthodontic emergencies in a context of the COVID-19 sanitary crisis. Out of the 1695 articles available on the four databases selected (PubMed, Science Direct, Cochrane and SciELO), eight articles were included and analyzed. The authors concluded that teleorthodontics is a relevant contribution to the management of orthodontic emergencies in case of contact person limitation. Recently, Lamb et al. [ 26] investigated the adaptations of orthodontic practice during the COVID-19 pandemic and the adaptations which were expected to stay after the crisis by a survey of 34 questions, sent by email in June 2021 to 1000 orthodontist specialists. With a hundred and sixty surveys returned from 38 different states across the United States ($16\%$ response rate), the results showed that during the COVID-19 crisis, the utilization of teleorthodontics increased from $8\%$ to $68\%$. However, only $45\%$ of these orthodontists responded that they would continue to use teleorthodontics after the pandemic. After the pandemic, teleorthodontics were expected to be employed for new patients, such as for aligners treatment monitoring, but not for fixed treatment follow-up. Furthermore, teleorthodontics could be a precious service for patients with difficulties in planning an in-office appointment. France is currently facing a lack of medical resources in certain areas of the country, known as “medical deserts”, hindering access to medical facilities, including orthodontic care. A cross-sectional questionnaire study carried out by Mathivanan et al. [ 27] investigated the interest of teledentistry for rural dental practice among general practitioners in and around Coimbatore district, Tamil Nadu, India. The survey was distributed to 200 dentists and 73 of them responded. Of the $37\%$ of the dentists who have responded to the questionnaire, the results showed that $73\%$ of the dentists estimated that teledentistry could offer access of specialists to the rural population. Additionally, $96\%$ of the respondents agreed that teledentistry would be the future of rural dental practice. In the same vein, Berndt et al. [ 28] investigated the feasibility of a general practitioner carrying out interceptive orthodontic treatment under the control of an orthodontist by teledentistry for disadvantaged children (referral to an orthodontist is not possible). They included 30 children treated by a general practitioner under the control of an orthodontist by teledentistry and 96 children treated by orthodontic residents. The improvement of peer assessment rating (PAR) index was found in the two groups with $35.6\%$ for the teledentistry group and $44.1\%$ for the control group, leading to no difference between the two groups ($p \leq 0.001$). Therefore, teleorthodontics could greatly benefit patients who have difficulty reaching a practice or a hospital. Rouanet et al. [ 9] explored also in a recent systematic review the relevance of teleorthodontics tool, in which an extensive panel of study designs was included to investigate this topic exhaustively. In total, 22 studies were included and analyzed, with a very variable levels of evidence. The studies were mainly from Europe but also from other continents (America, Oceania, Asia), which indicates that teleorthodontics is already used, or at least studied, throughout the world. These studies are mostly recent, with the majority being published between 2019 and 2021. According to the results of their systematic review, they summarized the advantages and disadvantages of the teleorthodontics. The advantages include: the resolution of certain orthodontic emergencies; anticipation for the next appointment (especially in case of an emergency); follow-up in the context of a pandemic, without risk of contamination; better follow-up of retention appointments; better compliance and oral hygiene with remote communication; easy communication between patients and practitioners; reliability of tools and ease of use; attractiveness of the practice, modernity; solution to be explored to facilitate access to orthodontics in medical deserts; time-saving as a result of fewer chairside appointments (less travel to the office) and shorter appointments on average (overall total duration); and saving money (travel costs). On the other hand, disadvantages are as follows: danger of fully remote treatment (to be avoided); not all phases of an orthodontic treatment can be carried out remotely; question of confidentiality, security, data protection; possible impoverishment of the human relationship and, therefore, of the quality of the therapeutic alliance; dissatisfaction and/or difficulty of use (patients less at ease with the new technology or find difficulty in the acquisition of intra-oral photos/scans); investment for the practitioner, such as computer equipment, subscriptions to follow-up services, cost of a scan box, etc., and similarly for the patient in some cases; lack of interest/fear; no shortening of treatment time; and a lack of evidence/data investigating all aspects of telemedicine in orthodontics. Moreover, in a recent systematic review and meta-analysis, Alam et al. [ 29] explored the future of orthodontics. Out of 634 publications from four databases (PubMed-MEDLINE, Web of sciences, Cochrane and Scopus), the authors included 17 articles based on the inclusion and exclusion criteria at the end of process of the study selection. Four categories were highlighted as emerging technologies: 3D printing, computer-aided design and computer-aided manufacturing (CAD/CAM), biopolymers and teleorthodontics. They also mentioned in their discussion section that the COVID-19 pandemic has also amplified the importance of the topic of teleorthodontics. Finally, according to another literature review conducted by Maspero et al. [ 30], teleorthodontics will thus play a role in the near future. Several recent studies have already investigated the reliability of teleorthodontics and demonstrated encouraging results both in terms of effectiveness [31,32,33,34] and satisfaction. Hansa et al. [ 31] compared the total treatment time, number of appointments, number of refinements, time taken up to the first refinement, number of emergency appointments and accuracy of predicted tooth positions between clear aligner treatment assisted by dental monitoring ($$n = 45$$ patients) versus clear aligner treatment without any system of teleorthodontics ($$n = 45$$ patients). The number of appointments were reduced by 33,$1\%$ in the dental monitoring group compared to the control group. Furthermore, a significant reduction in the time of the first refinement was demonstrated in the dental monitoring group, which also indicated a better aligner tracking in the dental monitoring group. Byrne et al. [ 32] explored the level of satisfaction among 59 patients and 62 clinicians after video consultations during the COVID-19 pandemic. They found that $76\%$ of the patients considered a remote consultation to be more convenient than a face-to-face consultation and $66\%$ stated that they would like more remote consultation appointments in the future. Additionally, $90\%$ of the clinicians considered that a remote consultation was appropriate. These results agree with studies conducted by Parker et al. [ 33,34] who also found very encouraging results from both patients’ and clinicians’ points of view. However, to the best of our knowledge, no study has measured patient and clinician satisfaction following an initial remote orthodontic consultation. The objective of the present randomized controlled trial was therefore to investigate patients’ and practitioners’ reported experience measures (PREMs) following a first standard face-to-face consultation versus a first orthodontic teleconsultation (video-assisted remote orthodontic consultation). ## 2. Materials and Methods Registration: This randomized controlled trial (RCT) was approved by the Department of Clinical Research and Innovation of Nice (file number: 447). The trial was registered on the ClinicalTrials.gov website (NCT 05646277). All patients were verbally informed of the purposes and monitoring of the study, and they all signed an informed consent form. Study design: The study was designed as a single center, two-arm, parallel-group, randomized controlled trial (RCT) with a 1:1 allocation ratio and evaluated patients’ and practitioners’ reported experience (PRE) following a first classic chairside consultation versus a first orthodontic teleconsultation (orthodontic remote consultation). Sixty consecutive patients requesting a first orthodontic consultation in the orthodontic unit of Nice University Hospital were included and randomly assigned to the control group (classic chairside consultation; $$n = 30$$) or to the test group (teleconsultation; $$n = 30$$). A CONSORT flow diagram is illustrated in Figure 1. Only the patients who requested a first orthodontic consultation could be included in the trial. Children not accompanied by their legal guardians (under 18 years old) were excluded. The practitioners involved in this study attended three calibration meetings, in which the objectives of the trial, the protocols and the assessment method were jointly reviewed and agreed upon. There were no changes to the protocol after trial initiation. Procedure: Control group (CG): Patients had an initial face-to-face chairside consultation under classic conditions. After the experience, patients completed a questionnaire. Note tipper: the term “initial consultation” refers to the first orthodontic consultation, where no dental cast or dental X-ray are performed;Test group (TG): Patients received a first orthodontic teleconsultation via video communication using a computer equipped with a webcam, a microphone and a speaker. After the experience, the patients completed a questionnaire;Practitioners: After each consultation, the two orthodontic practitioners completed a questionnaire. Data collection:Patient characteristics: The following parameters were collected at baseline for each patient: gender, age and knowledge of telemedicine (yes/no). Distance, time and cost of travel to the hospital were also recorded;Outcome data: Patients’ and practitioners’ reported experience were assessed using questionnaires containing open and closed questions as well as a 0–10 visual analog scale (VAS) after the experience. Practitioners provided each patient with a comprehensive explanation of the use of the VAS and how to enter the outcome measure, according to Wewers and Lowe [35], and collected the questionnaires.⚬For both groups: patients were asked to rate their overall satisfaction, the quality of communication with the clinician, their understanding of the explanations provided and their sense of confidentiality;⚬For the test group only: an additional questionnaire was given in which patients were asked whether they would recommend the procedure to a friend, whether they would undergo the teleconsultation again and whether they would repeat the experience for a follow-up orthodontic consultation. If the answer was negative, the patient was asked to mention the reason. Finally, patients rated their satisfaction with the technical aspects of the consultation;⚬For the practitioners: a questionnaire was also delivered after each consultation. Overall satisfaction, quality of the communication with the patient, feasibility of establishing a clear and definite clinical diagnosis and deciding the need for a complete orthodontic examination (photos, radiographs, dental casts) were evaluated. Additionally, after the teleconsultation, practitioners were asked whether they would recommend the method to a colleague and if they were willing to repeat this type of consultation. Finally, the duration of the consultation was also recorded in the two groups as well as the technical issues that arose during the teleconsultation in the test group. Statistical Analysis: Data were presented as mean ± standard deviation (SD). Groups were compared using Student’s t test. Results were considered significant at the $5\%$ critical level ($p \leq 0.05$) ## 3. Results Patient Characteristics: Sixty patients (33 female; 27 male) aged 16.2 ± 10.5 years were included in the study and randomly allocated to the test group (teleconsultation, $$n = 30$$) or the control group (classic chairside consultation, $$n = 30$$). Patient characteristics of each group are shown in Table 1. One out of two patients had never heard of the concept of telemedicine. The average distance from the patient’s home to the hospital was 9.4 ± 8.7 km for a travel duration of 24.9 ± 13.2 min and a cost of 3.4 ± 3.8 EUR, with no difference between the two groups (Table 2). Outcomes in both groups: The response rate was $100\%$ among patients. Overall satisfaction was comparable between the control group with a classic face-to-face consultation (9.21 ± 1.3) and the test group with a teleconsultation (8.69 ± 1.9) ($$p \leq 0.23$$) (Figure 2). The quality of the communication with the clinician was rated 9.28 ± 1.2 in the control group compared to 9.16 ± 1.5 in the test group. The difference between the two groups was not statistically significant ($$p \leq 0.74$$). Additionally, in each group, one out of 30 patients reported that they did not fully understand the explanations, and there was thus no difference between the two groups. Finally, $96.7\%$ of the patients in the control group considered the face-to-face consultation to be confidential. All patients in the test group considered that the video consultation was confidential. Outcomes in the test group: $10.3\%$ of the patients in the test group would not recommend a remote consultation to a friend. Furthermore, $17.2\%$ of patients would not wish to repeat this first orthodontic teleconsultation, nor would they wish to repeat the experience in a follow-up orthodontic consultation. Among them, $100\%$ considered that face-to-face contact with the practitioner is essential during a consultation. Finally, patient satisfaction with the use of the technology was rated 8.4 ± 2.2. Outcomes from the practitioners: The response rate was $100\%$ among clinicians. The overall satisfaction was 9.8 ± 0.2 after the consultation of the control group compared to 7.65 ± 1.9 after the consultation of the test group. The difference between the two groups was statistically significant ($p \leq 0.01$) (Figure 3). The quality of the communication with the patient was 9.83 ± 0.4 after the consultation among the control group compared to 8.1 ± 1.8 after the consultation among the test group. The difference between the two groups was statistically significant ($p \leq 0.01$). While a clear and definite clinical diagnosis could be established during all face-to face consultations, this was not possible for $53.3\%$ of cases during the remote consultation. However, in $87.5\%$ of cases, the latter procedure was still sufficient to determine whether the patient needed additional investigations (X-rays/dental casts/photos). Duration of the consultation: The average duration of the teleconsultation was 12.14 ± 3.1 min compared to 14.47 ± 1.9 min for the face-to-face consultation. The difference between the two groups was significant ($p \leq 0.01$). Technical issues (test group): For $23.3\%$ of the teleconsultations, a technical problem arose. In $42.8\%$ of the cases, very short, inconsequential, sound interruptions occurred, except for one consultation which had to be reinitiated (the sound was completely cut off). In $28.7\%$ of cases, there was a very short, minor interruption of the internet connection. Another issue was inadequate lighting, hindering intraoral examination. In such cases, the patient was asked to move or to use a sun shield. ## 4. Discussion This randomized controlled trial was the first, to our knowledge, to compare patients’ and practitioners’ experience following an initial remote orthodontic consultation versus a first traditional face-to-face orthodontic consultation. The two groups were homogeneous for all baseline characteristics, except for age, which was significantly higher in the control group. Following a first orthodontic teleconsultation, patient satisfaction was high, while results were more mixed from the practitioners’ perspective. Overall patient satisfaction in the test group was relatively high and, although slightly lower than among patients in the control group, the difference was not statistically significant. Similar results were reported in the medical literature. In a randomized controlled trial, Buvik et al. [ 36] investigated patients’ reported outcomes following a remote orthopedic consultation by telemedicine (199 patients) compared to a standard face-to-face consultation (190 patients). In that study, $99\%$ of the patients evaluated the remote consultation as very satisfactory or satisfactory, and $86\%$ said that they would prefer a video-assisted consultation for their next consultation. In the field of dentistry, Parker et al. [ 33] evaluated 111 satisfaction questionnaires from patients who attended a video consultation and found that one-third preferred the video consultation to a standard consultation, while one-third were neutral. In addition, $90\%$ of the patients would recommend the video consultation. On the other hand, in the present study, patients in the control group remained extremely satisfied with their consultation despite the relatively long travel time and distance as well as the time slots that required them to miss work or school. In the study conducted by Buvik et al. [ 36], $99\%$ of patients in the control group were also very satisfied or satisfied after their face-to-face consultation. In terms of quality of communication with the clinician, understanding of the explanations and their sense of confidentiality during the experience, satisfaction was comparable between the two groups, in agreement with results reported by Parker et al. [ 33]. Indeed, they asked their patients if they could easily talk about the care they received and $72.2\%$ and $20.7\%$ of patients “strongly agreed” and “agreed”, respectively. Additionally, whereas in the Parker et al. [ 33] study, patients who reported a preference for face-to-face consultations mentioned poor internet connection as one of the negative reasons, in the present RCT, only one disadvantage was noted, namely the lack of direct contact with the practitioner, despite some technical issues raised by practitioners during $23.3\%$ of the video consultations. In fact, patient satisfaction with the technology was relatively high. It is worth noting that a similar percentage of technical problems was found in the Byrne et al. [ 32] study ($27\%$), where most problems were related to connection issues (sound loss and temporary video interruption). All in all, although the standard face-to-face consultation is still quite appropriate, the remote consultation in orthodontics in the context of a first consultation is a tool that patients greatly appreciate and consider satisfactory or very satisfactory. The average travel time from patients’ home to the hospital was 24.9 ± 13.2 min, to which must be added the equivalent duration of the return journey, as well as the time spent in the waiting room. Saccomanno et al. [ 24] had already reached an estimate of 50 min (28 min for the round trip, 7 min for parking and 15 min in the waiting room) for the average time spent by patients in addition to the actual appointment. Thus, a remote consultation saves time. It should also be noted that in this RCT, the time spent on the consultation itself was significantly shorter by about two and a half minutes during the video consultation compared to the standard consultation. This reduction in consultation time during video appointments had already been reported by Saccomanno et al. [ 24], and also mentioned by Byrne et al. [ 32], although the duration of the virtual consultation was not investigated in the latter study. However, from a practitioner’s point of view, the results are less encouraging, even though, in most cases, the video consultation was sufficient for the practitioner to know whether or not the patient needed a complete orthodontic examination, which is the initial goal of a first consultation. Orthodontic practice systematically requires obtaining dental casts and X-rays to make a complete and thorough diagnosis and initiate treatment. Moreover, although a clinical examination seems relatively easy to perform remotely, functional and periodontal assessment remains a more complex procedure. A classic face-to-face appointment is therefore essential before starting orthodontic treatment. However, an initial remote orthodontic consultation is still useful in many situations. For example, those living far from a medical facility can thus have access to a practitioner, allowing useful and early detection of orthodontic disorders (complex and/or urgent cases). In addition, during public health crises such as the COVID-19 epidemic, this tool remains the only available link with a practitioner. It can also reassure parents and/or adult patients and raise their awareness of the importance of orthodontic treatment. A first remote consultation is thus a communication tool and should by no means be considered as a substitute for conventional diagnosis or treatment. Recently, Rouanet et al. [ 9] investigated the relevance of teleorthodontics tools in a systematic review and also concluded that “ teleorthodontics is an interesting and complementary tool that is, in no way, a systematic alternative to face-to-face orthodontic appointments in the office”. Finally, some limitations should be addressed. This present RCT is a single-center study that would benefit from being expanded to other centers. Due to the lack of published material on the subject in the field of orthodontics, the sample power calculation was based on our active patient records. The cost of the journey home–hospital was 3.4 ± 3.8 EUR for both groups, which is quite low and could therefore influence the results and make it less attractive to carry out a remote consultation. In addition, it would have been interesting to carry out each remote consultation twice independently by the two orthodontist practitioners to check whether their results were consistent. Further, this study is based solely on satisfaction questionnaires and, considering the rather encouraging results, teleorthodontics should now be more widely investigated (including the data protection, informed consent of patient, long-term results, etc.). ## 5. Conclusions and Perspective Given the limitations of this first randomized controlled trial on this topic, the following conclusions could be stated:The overall satisfaction of patients who received a first remote orthodontic consultation was high and not significantly different from patients who received a first standard face-to-face orthodontic consultation, including in terms of quality of communication with the practitioner, understanding of explanations and their sense of confidentiality;However, overall clinician satisfaction was significantly lower after the teleconsultation compared to the traditional consultation. Teleorthodontics, in the context of a first consultation, appears to be an interesting alternative and complementary approach to a classical face-to-face appointment, but which should by no means become systematic. A first remote consultation is thus a communication tool and should by no means be considered as a substitute for conventional diagnosis or treatment. 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--- title: 'Labour-Market Characteristics and Self-Rated Health: Evidence from the China Health and Retirement Longitudinal Study' authors: - Yuwei Pan - Hynek Pikhart - Martin Bobak - Jitka Pikhartova journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048592 doi: 10.3390/ijerph20064748 license: CC BY 4.0 --- # Labour-Market Characteristics and Self-Rated Health: Evidence from the China Health and Retirement Longitudinal Study ## Abstract In the face of labour-force ageing, understanding labour-market characteristics and the health status of middle-aged and older workers is important for sustainable social and economic development. Self-rated health (SRH) is a widely-used instrument to detect health problems and predict mortality. This study investigated labour-market characteristics that may have an impact on the SRH among Chinese middle-aged and older workers, using data from the national baseline wave of the China Health and Retirement Longitudinal Study. The analytical sample included 3864 individuals who at the time held at least one non-agricultural job. Fourteen labour-market characteristics were clearly defined and investigated. Multiple logistic regression models of the associations of each labour-market characteristic with SRH were estimated. Seven labour-market characteristics were associated with higher odds of poor SRH when controlled for age and sex. Employment status and earned income remained significantly associated with poor SRH, when controlling for all the sociodemographic factors and health behaviours. Doing unpaid work in family businesses is associated with 2.07 ($95\%$ CI, 1.51–2.84) times probability of poor SRH, compared with employed individuals. Compared with more affluent individuals (highest quintile of earned income), people in the fourth and fifth quintiles had 1.92 ($95\%$ CI, 1.29–2.86) times and 2.72 ($95\%$ CI, 1.83–4.02) times higher chance, respectively, of poor SRH. In addition, residence type and region were important confounders. Measures improving adverse working conditions should be taken to prevent future risk of impaired health among the Chinese middle-aged and older workforce. ## 1. Introduction As a result of rapid population ageing, middle-aged and older workers are becoming the main part of the labour force in China [1,2]. According to the Seventh National Population Census in China in 2020, workers aged 45 years or more accounted for $43.2\%$ of the total working population [3]. Thus understanding the characteristics of middle-aged and older workers, and maintaining their health status, are both important for the sustainable development of China’s society. According to the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, noncommunicable diseases (NCDs) such as cardiovascular diseases (CVDs) are leading causes of disability-adjusted life years (DALYs) among people aged 50 years or over [4]. Stroke and ischemic heart disease were reported as the top two leading causes of DALYs in all ages, and in the 50–69 age group in China during 2019 [5]. Self-rated health (SRH), which is a widely-used tool for the measurement of health status, can effectively detect suboptimal health status (SHS) [6]; poor SRH is associated with higher future risk of chronic diseases, multimorbidity, and mortality [6,7,8,9,10,11]. The reliability of SRH has been assessed in different populations [9,12,13]. Generally, moderate to substantial test-retest reliability of SRH was reported among adults [9,12,13]. Labour-market characteristics refers to a wide range of work-related conditions. Previous studies provided evidence for associations between some of the labour-market characteristics and poor SRH. For example, studies of Korean workers indicated that precarious employment, such as temporary work, was associated with 1.31 times higher chance of poorer SRH for men and 1.34 times higher chance of poorer SRH for women [14]. The effects of long working hours on health is well studied in developed countries [15], although there is no strict definition of long working hours and the cut-offs for defining long working hours (e.g., ≥55 h/week) differ across studies [16,17,18,19,20]. Working long hours is associated with increased risk of chronic diseases and poor SRH [17,19,20,21,22,23]. Two of the possible connections linking long working hours and poor health outcomes are physiological and behavioural mechanisms [16]. Other labour-market characteristics, such as unemployment [24], occupation [25], and lack of paid sick leave, are also associated with poor SRH [26]. However, existing evidence mainly comes from developed and western countries, and studies on associations between labour-market characteristics and SRH among the Chinese working population are scarce [16]. The current study, for the first time, used nationally-representative data from China to investigate associations between fourteen labour-market characteristics and SRH in the Chinese middle-aged and older working population. As previous studies of China reported gender and regional differences in SRH, and the effects of occupational stress [27,28,29], we also tested the interactions between sexes and among regions for each labour-market characteristic. ## 2. Materials and Methods We used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally-representative study conducted among community residents aged 45 years or older living in 28 Chinese provinces [30,31]. Institutionalised individuals were not sampled [30]. CHARLS is intended to provide micro, longitudinal data, covering health measures and indicators of socio-economic status, of the middle-aged and older working population, for the study of Chinese age-related demographic issues [30]. Information on family, health status, healthcare, work circumstances, and economic status was collected [31]. The CHARLS national baseline survey was conducted between 2011 and 2012 [31], and four national waves are available. The first national survey was used in the present study. Further information about CHARLS can be found in the CHARLS cohort profile and users’ guide [30,31]. The sample-selection procedure used for the current study is illustrated in Figure 1. ## 2.1. Labour-Market Characteristics for Working Population For the purposes of this paper, working population refers to respondents who were holding, at the time of the survey, at least one non-agricultural job or who did non-agricultural work for at least one hour during the previous week. This includes people who were employed (earning a wage), self-employed, working unpaid in a family business, temporarily laid-off, on leave, or doing on-job training but planning to return within six months. Respondents doing only housework or unpaid activities such as voluntary work were not included in these analyses. Labour-market characteristics in the working population include employment status, weekly working hours, work sector (public or private), quintiles of earned income (quintiles based on income of CHARLS participants), occupation, length of current employment, and whether workers had any social-support insurance (including pension, health insurance, unemployment insurance, worker’s injury insurance, and maternity insurance). Participants without clear employment status (being either employed or self-employed, or doing unpaid work in a family business) were put into a separate category (Not enough information to classify). Weekly working hours included hours spent on the main job plus additional jobs. The public sector includes government organisations, institutions operated as government units, non-government organisations, and firms that were $100\%$ state owned, state-controlled, $100\%$ collective-owned, or collective-controlled. Earned income refers to after-tax salary, plus all bonuses and subsidies for the employee, and income or wages from additional jobs. For self-employed workers and unpaid workers in family business, earned income meant estimated net income for the previous year. For this group, income information was only requested in the household section; therefore, the income could not be considered as individual-earned income. Occupation categories were based on self-reported job descriptions, coded using International Standard Classification of Occupations (ISCO) in CHARLS [30]. Major groups of ISCO-08 consist of 1: Managers; 2: Professionals; 3: Technicians and associate professionals; 4: Clerical support workers; 5: Service and sales workers; 6: Skilled agricultural, forestry, and fisheries workers; 7: Craft and related trades workers; 8: Plant and machine operators, and assemblers; 9: Elementary occupations; and 10: Armed forces occupations [32]. According to skill level, occupations in the current study were categorised into Managers (except hospitality, retail, and other services managers) and professionals, Technicians and associate professionals, Clerks and workers, and Elementary occupations. In addition, government employees (such as civil servants and formal employees of an establishment) and formal employees of an establishment in institutions were not asked about employer-provided insurance in CHARLS because these people were considered to have all necessary insurance by default. Therefore, those people were categorized to “At least one type of insurance” in the section about employer-provided/self-employed covered insurance. ## 2.2. Labour-Market Characteristics for Employees In addition to characteristics available to the full analytical sample, there are additional characteristics available for “employees”, including government employees and company employees. In the work section of the CHARLS questionnaire, some questions were only asked of employees. These work characteristics include whether a person was in a supervisory position, type of employing entity, employment type at current workplace, whether a person had a written labour contract for their current workplace/labour-dispatch company, labour-contract period, number of days for paid vacation or paid sick leave, and fringe benefits. Team leaders, government officials (including section chiefs, directors of a division, and directors-general of bureaus, and above), village leaders, township leaders, division managers, and general managers were not asked the question “Are you in a position to supervise others”, however, they were still considered to be supervisors in the current analysis. In addition, government officials were not asked in CHARLS about their employment type at current workplace, and they were considered to be contract workers. ## 2.3. Self-Rated Health SRH was determined by the question “Would you say your health is very good, good, fair, poor or very poor?”. In the current study, SRH was dichotomised into good or fair (very good, good, and fair) and poor (poor and very poor). ## 2.4. Covariates Sociodemographic factors included age, sex, marital status (unmarried including separated, divorced, widowed, and never married), education level, residence type, and region. Whether the administrative village/neighbourhood that each household belongs to was in an urban or rural area was defined by the National Bureau of Statistics (NBS) of China [30]. Considering the large number of internal migrant workers and the urban-rural gap in China, residence type was defined based on their official household registration (known as Hukou) status, including: urban (urban residents with non-agricultural Hukou), migrant (rural residents with non-agricultural Hukou or urban residents with agricultural Hukou), and rural (rural residents with agricultural Hukou) [33,34], According to the division of NBS in 2011, the 28 provinces included in CHARLS were divided into four regions (East, Central, West, and Northeast) based on their social- and economic-development levels [35]. Health behaviours include smoking and alcohol-consumption status. Occasional drinker was defined as a person who drinks alcohol once a month or less, and frequent drinker meant a person who drinks more than once a month. ## 2.5. Statistical Analyses We calculated the prevalence of poor-SRH among the working population according to sample characteristics and labour-market characteristics. The chi-squared test was used to investigate association between each sample characteristic and SRH. Due to the design of the CHARLS questionnaire, some work-related questions were only asked of paid employees. As a result, there are two groups of labour-market characteristics in the current study. One group is available for the whole sample, and the other is only available for paid employees. Thus, we analysed the two groups separately. To investigate the association between labour-market characteristics and SRH, multiple logistic regression models of the effect of each labour-market characteristic were estimated, controlling for sociodemographic factors (age, sex, marital status, education, residence type, region) and health behaviours (smoking and alcohol-consumption status). Covariates were added to the model sequentially. Interactions between sex and each labour-market characteristic, and between region and each labour-market characteristic were tested, as sex and region are potentially important effect modifiers. Although there was some evidence for the interactions between sex and employer-provided insurance ($$p \leq 0.05$$), and between region and weekly working hours ($$p \leq 0.04$$), generally little evidence was found for statistical heterogeneity. Therefore, homogeneity was assumed, and combined results were reported (Region-specific results was presented in Supplementary Materials Table S2). Furthermore, data from participants who were not asked certain questions were considered missing (e.g., labour-contract period was only asked among those having labour contracts). Data were missing for labour-market characteristics for various reasons (such as implausible values, questions not answered, and questions not asked). Such data were combined and analysed as a separate category. All analyses (including Figure S1 in Supplementary Materials) were performed using Stata/MP 16.1 [36]. ## Sample Characteristics Descriptive characteristics of the analytical sample are shown in Table 1. ( For Table 1, Table 2 and Table 3, percentages may not total $100\%$ due to rounding.) The distribution of SRH measured using the five-point scale among the whole sample is shown in Supplementary Materials, Table S1. The overall prevalence of poor or very poor SRH in subjects was $14\%$. Older age, being female, unmarried, of lower education level, rural residence type, living in Eastern or Central regions, and having a higher drinking frequency, were associated with higher prevalence of poor SRH. Older age groups had significantly higher prevalence of poor SRH (p for trend < 0.001). Migrant status was not significantly associated with higher prevalence of poor SRH, although the prevalence of poor SRH among migrant residents ($13\%$) was slightly higher than among their urban counterparts ($11\%$). There was a significant difference between urban and rural residents ($p \leq 0.001$). The prevalence of poor SRH is significantly higher in Central ($$p \leq 0.01$$) and Western China ($p \leq 0.001$), with the highest prevalence of $18\%$ occurring in Western China, compared with $12\%$ in Eastern China. Table 2 shows the prevalence and odds ratios (ORs) of poor SRH for each labour- market characteristic in the working population. When controlled for age and sex, people who are self-employed and doing unpaid work in a family business have 1.23 times and 2.34 times chance, respectively, of poor SRH compared with employed participants. After further adjustment for marital status, education level, residence type, living regions, smoking status, and alcohol consumption, the ORs dropped slightly but still indicate greater chance of poor SRH for self-employed and unpaid-family-business groups. Only $21\%$ of respondents worked standard hours (40–49 h/week), while $30\%$ of respondents worked ≥60 h/week. In a fully adjusted model, compared with people working standard hours, working for fewer hours (1–39 h/week) and long hours (≥60 h/week) are both associated with higher odds of poor SRH (OR = 1.60 and 1.24). Working in the private sector is associated with a 1.24 times greater chance of poor SRH, compared with working in the public sector. In addition, results suggest a strong dose-response relationship between income level and poor SRH (p for trend < 0.001). Compared with managers/professionals, technicians/associate professionals had lower odds of poor SRH (fully adjusted OR = 0.65). Clerks/workers and elementary occupations had odds of poor SRH similar to managers/professionals (fully adjusted OR = 0.95 and 0.96). Fifty-three percent of respondents had worked in their current job for 10 years or less. Compared with those respondents, people working in their current job for over 10 years had lower odds (OR = 0.91) of poor SRH, controlling for age and sex. While the effects of occupation and length of current employment on SRH were not statistically significant, after adjustment of age and sex, the ORs still indicate the direction of those associations. Finally, compared with participants having at least one employer-provided form of insurance, participants without any employer-provided insurance or self- covered insurance had higher odds of poor SRH (OR = 1.35), although this difference disappeared in the fully-adjusted model (OR = 1.06). Table 3 shows the prevalence and ORs of poor SRH for each labour-market characteristic among employees. People who were not in supervisory positions had 1.86 times greater odds of poor SRH, controlling for age and sex. The association remained significant after further adjustment (OR = 1.62). Only $8\%$ of employees received wages from labour-dispatch companies, and they had 1.27 times greater odds of poor SRH compared with those receiving wages from place of work, controlling for age and sex. Similarly, casual/part-time workers had 1.34 times greater chance of poor SRH, compared with contract workers, controlling for age and sex. However, as reported in Table 3, there was only weak evidence for the effect of receiving wages from labour-dispatch companies and causal/part-time work on SRH. In addition, compared with the $24\%$ of employees who had written labour contracts, employees without such contracts had 1.41 times higher odds of poor SRH, controlling for age and sex. Compared with employees who had paid vacation/sick leave, those without any paid vacation/sick leave had 1.45 (age- and sex-adjusted) times greater odds of poor SRH. Similarly, compared with employees with fringe benefits, employees without any fringe benefits had 1.27 (age- and sex-adjusted) times higher odds of poor SRH. As shown in Table 3, further adjustment reduced these odds ratios, but while non-significant, the direction of the associations remained the same. ## 4.1. Main Finding of This Study In this study, we used national representative data to assess the associations between a wide range of labour-market characteristics and SRH among the Chinese middle-aged and older working population. Several labour-market characteristics were associated with poor SRH among Chinese middle-aged and older workers, including doing unpaid work in family businesses, working for less than 40 h/week or working for 60 h/week or over, working in private sectors, earning lower income, being without any employer-provided or self-provided insurance, not being in a supervisory position, and being without a written labour contract. The associations were generally statistically significant when controlled for age and sex, although less so when fully adjusted. Residence type and region appear to have acted as confounding variables in several associations between labour-market characteristics and SRH. ## 4.2. Findings in the Context of Existing Studies The current study used a widely used version of SRH scale (WHO version) as the measurement of outcome [37]. The distribution of SRH in the current study is shown in Table S1 in Supplementary Materials. Among 3864 working individuals, $34.03\%$ reported “very good” or “good” SRH, $51.71\%$ reported “fair” SRH, and $14.26\%$ reported “poor” or “very poor” SRH. Compared with a previous study based on 12431 CHARLS wave-1 participants (“very good”/“good”: $23.4\%$, “fair”: $51.2\%$, “poor”/“very poor”: $25.5\%$), SRH reported by the working population in the current study was generally better [9]. This may be due to the “healthy worker survivor effect”, as employed individuals tend to be healthier than the general population which includes those who left employment [38]. Previous research from western countries also reported the distribution of the WHO version SRH among middle-aged and older adults. In the study of Jürges et al. using the Survey of Health, Ageing and Retirement in Europe (SHARE), among 11643 participants (aged 50 years and over), $60.5\%$ of them reported “very good” or “good” SRH, $29.8\%$ reported “fair” SRH, and $9.7\%$ reported “poor” or “very poor” SRH [39]. Compared with western populations, Chinese population are more likely to report a “fair” SRH. Some of the labour-market characteristics used in this study generally reflect social disadvantage, for example: working for very long hours, earning lower income, being without any employer-provided insurance or without any formal labour contract. Those characteristics may simply indicate that the individuals have lower socioeconomic status (SES), which is usually associated with poorer health [40]. In addition, some country-specific factors, such as migrant status, should be considered when interpreting the results of the current analysis. Due to the unbalanced regional development and urban-rural gap, there is a huge number of rural migrant workers in China. Rural migrant worker here refers to workers holding rural household registration but who engage in non-agricultural work in their home area, or work outside their home area for six months or more per year [34]. According to the NBS, there were 285.6 million rural migrant workers in 2020, accounting for $38.05\%$ of the total employed population [34]. Fifty-three percent of rural migrant workers worked in the Eastern region where economic development was more advanced [34]. Compared with their urban counterparts, rural migrant workers have many disadvantages, including less access to urban services and lower job stability [41,42]. In addition, rural migrant workers also have higher rates of health-risk behaviours, such as alcohol use and smoking [43], and a poorer mental health status [44,45]. The internal rural migration of workers in China is economically driven and usually temporary. In accordance with previous studies, there was no significant difference in the health status between urban residents and the migrant population [46,47], while compared with both the urban and migrant populations, rural residents reported worse health status. Moreover, the population that returns after migration experience had worse physical health compared with the migrant population, as unhealthy migrants usually choose to return to their hometowns [47]. Over half of rural migrant-working individuals worked in the Eastern region in 2020, where the economy is more developed. Figure S1 (Supplementary Materials) shows the four major economic regions in China. In Table S2 (Supplementary Materials), we report the results of associations between labour-market characteristics and SRH in working population according to region. Generally, the biggest difference is between the Northeast and other regions. However, it should be noted that the number of participants from the Northeast only accounts for $7.6\%$ of the whole sample. The current study found that the prevalence of poor SRH is significantly higher among women, which is in consistent with previous studies [48]. In China, women usually take more responsibility for the care of the family. A recent study of Chinese females found that the double burden of work and informal care significantly increased the probability of reporting poor SRH ($2.35\%$ higher probability compared with women with no care burden), by reducing exercise time and increasing psychological stress [48]. As the CHARLS baseline wave did not ask about unemployment, we could not unequivocally define this important variable. However, an earlier study conducted in three North-Western Chinese cities reported that, compared with the employed population, unemployed people had 1.40 times greater chance of poorer SRH ($95\%$ CI, 1.25–1.55) [49]. In addition, consistent with previous studies, our results indicate that both short working hours (1–39 h/week) and long working hours (≥60 h/week) are associated with an increased risk of poor SRH [18]. The adverse effect of long working hours on health status has been supported by many previous studies [15,21,22]. However, for those working short hours, the higher odds of poor SRH might be due to reverse causality, as people with worse health status may reduce their working hours. ## 4.3. Strengths and Limitations This study used nationally-representative data to assess the effect of a wide range of labour-market characteristics and SRH among the Chinese working population, and investigated the importance of migrant status and region in these associations. Our study provides the basic evidence of understanding labour-market characteristics and the health conditions of the Chinese middle-aged and older workforce. In addition, the present study used information from people who are currently working, instead of employment history, thus the recall bias was greatly reduced. However, this study also has several limitations. First, although we only included participants who are currently holding at least one non-agricultural job, still $44.3\%$ of participants reported engagement in agricultural work for over 10 days in the last year ($86.2\%$ engaged in their own household agricultural work and $61.1\%$ were rural residents). One possible reason for the large proportion of participants doing non-agricultural work but engaged in agricultural work for over 10 days at the same time, is that migrant workers (who account for $29\%$ of the analytical sample) and rural residents (who account for $45\%$ of the analytical sample) may help with their household agricultural work during busy farming seasons. As hours of agricultural work were not counted in the current analysis, this may have affected the association between weekly working hours and poor SRH. Second, as the outcome of interest is SRH, there could be a potential measurement bias. Third, the data we used were from the national baseline wave of CHARLS, which was conducted between 2011 and 2012, therefore requiring future studies assessing the longitudinal association between labour-market characteristics and health outcomes in the Chinese middle-aged and older workforce. Fourth, we decided to keep a specific category for missing values in employment variables. It would be possible to use an alternative approach, such as multiple imputations, but we believe that the proportion missing due to unanswered questions about employment characteristics was relatively low, so multiple imputation would possibly only have little impact on our findings. Fifth, although we used a wide range of working characteristics, some other work-related characteristics, such as support and training provided by employers, or experience of workplace violence and/or bullying, were not available. Finally, we cannot exclude the potential reverse causality, whereby people move to different occupation because of their ill health. ## 5. Conclusions Several labour-market characteristics were associated with higher odds of poor SRH among Chinese middle-aged and older workers when controlled for age and sex. Those characteristics included doing unpaid work in family businesses (OR = 2.34), working for less than 40 h/week (OR = 1.83) or for 60 h/week or more (OR = 1.39), working in private sectors (OR = 1.45), earning lower income (4th quintile: OR = 2.13; 5th quintile: OR = 3.19), being without any employer-provided or self-provided insurance (OR = 1.35), not being in a supervisory position (OR = 1.86), and being without any written labour contract (OR = 1.41). Working subjects with the above characteristics, especially those doing unpaid work in family businesses or who earn lower incomes, are likely to be more socially disadvantaged and have a worse general health status. Most of the adverse labour-market conditions examined in this study can be improved. Based on our results, there are two main aspects needing improvement. 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--- title: Association of NEF2L2 Rs35652124 Polymorphism with Nrf2 Induction and Genotoxic Stress Biomarkers in Autism authors: - Lev N. Porokhovnik - Vladimir M. Pisarev - Anastasia G. Chumachenko - Julia M. Chudakova - Elizaveta S. Ershova - Natalia N. Veiko - Natalia L. Gorbachevskaya - Uliana A. Mamokhina - Alexander B. Sorokin - Anna Ya. Basova - Mikhail S. Lapshin - Vera L. Izhevskaya - Svetlana V. Kostyuk journal: Genes year: 2023 pmcid: PMC10048604 doi: 10.3390/genes14030718 license: CC BY 4.0 --- # Association of NEF2L2 Rs35652124 Polymorphism with Nrf2 Induction and Genotoxic Stress Biomarkers in Autism ## Abstract Increased oxidative/genotoxic stress is known to impact the pathophysiology of ASD (autism spectrum disorder). Clinical studies, however, reported limited, heterogeneous but promising responses to treatment with antioxidant remedies. We determined whether the functional polymorphism of the Nrf2 gene, master regulator of anti-oxidant adaptive reactions to genotoxic stress, links to the genotoxic stress responses and to an in vitro effect of a NRF2 inductor in ASD children. Oxidative stress biomarkers, adaptive responses to genotoxic/oxidative stress, levels of master antioxidant regulator Nrf2 and its active form pNrf2 before and after inducing by dimethyl fumarate (DMF), and promotor rs35652124 polymorphism of NFE2L2 gene encoding Nrf2 were studied in children with ASD ($$n = 179$$). Controls included healthy adults ($$n = 101$$). Adaptive responses to genotoxicity as indicated by H2AX and cytoprotection by NRF2 contents positively correlated in ASD children with a Spearman coefficient of $R = 0.479$ in T+, but not CC genotypes. ASD children with NRF2 rs35652124 CC genotype demonstrated significantly higher H2AX content (0.652 vs. 0.499 in T+) and pNrf2 induction by DMF, lowered 8-oxo-dG concentration in plasma and higher cfDNA/plasma nuclease activity ratio. Our pilot findings suggest that in ASD children the NEF2L2 rs35652124 polymorphism impacts adaptive responses that may potentially link to ASD severity. Our data warrant further studies to reveal the potential for NEF2L2 genotype-specific and age-dependent repurposing of DMF and/or other NRF2-inducing drugs. ## 1. Introduction The term “autism spectrum disorder” (ASD) encompasses heterogeneous neurodevelopmental conditions characterized by delayed development, early-onset deficits in social communication, and restricted, repetitive sensory-motor behavior of variable significance [1]. The causes of ASD include a complex combination of genetic and environmental factors and birth-related parameters that may act in concert during the development of the neural system [2,3]. Although the pathophysiology of psychiatric diseases, including ASD, is far from being fully elucidated, a growing body of clinical and preclinical evidence has discovered the pivotal role of oxidative stress in their pathogenesis [4,5,6]. The studies in this field have indicated that various psychiatric diseases are characterized by oxidation disbalance exhibiting higher levels of oxidative biomarkers and lower levels of antioxidant defense biomarkers in the brain and peripheral tissues. The resultant DNA damage and deficient repair of oxidative DNA lesions have been proposed to contribute to the development of schizophrenia and ASD [7]. The elevated free radicals result in genotoxicity and launch a cascade of sterile inflammation and microglial activation in the affected brain [8]. Oxidative stress, increased inflammation as a hallmark of a dysregulated immune response, and abnormal mitochondrial metabolism are inextricably linked, seemingly representing the common molecular underpinning of many neuropathies [9], including ASD. In most cases, antioxidant therapy yields small or no effect. Studying molecular pathways underpinning the major pathogenic clues to the cellular-level regulation of genotoxic stress and adaptive antioxidant shielding in norm and ASD has recently become a promising avenue to reveal novel approaches to the personalization of ASD management and correction. In response to oxidative/genotoxic stress, the cells initiate the expression of a battery of protective genes encoding antioxidant enzymes and transporters. A coordinated launch of transcription of the cell defense genes is managed by some transcription factors. Nrf2 (nuclear E2-related factor 2) protein is a major transcription factor sensible to the shifts of reductive-oxidative status [10]. After the induction by increased FR level, Nrf2 penetrates the nucleus and launches the expression of 100–200+ genes that contain a special motif in the promotor region, so-called ARE (antioxidant response element) to bind Nrf2: 5′-A/GTGAC//nnnGCA/G-3′ [11]. The ARE-driven genes include genes for antioxidant enzymes (heme oxygenase 1, superoxide dismutase 1, glutathione peroxidase 2, glutamate-cysteine ligase, glutathione reductase, thioredoxin reductase, etc.) and phase II xenobiotic detoxification enzymes (glutathione-S-transferase A, M, P, NAD(P)H:quinone oxidoreductase 1, NRH:quinone oxidoreductase 2, UDP-glucuronosyltransferase A and B, etc.) [ 12]. Thus, Nrf2 should reduce genotoxicity caused by xenobiotics and free radicals. NFE2L2 gene encoding Nrf2 transcription factor is located on human chromosome 2. In 2013, the genetic databases contained 583 allelic forms of NFE2L2, of which 18 single nucleotide polymorphism (SNP) variants were associated with the risk of a particular disease. The involved diseases include respiratory diseases and critical conditions [13,14], male infertility [15], cardiovascular and pulmonary diseases [16], diabetes [17], gastrointestinal diseases [18], and neurodegenerative diseases such as Parkinson’s disease [19,20], Alzheimer’s disease [21], amyotrophic lateral sclerosis [22]. All these disorders share oxidative stress and elevated cell death, which are also intrinsic to ASD. To decrease central oxidative and nitrosative stress, alleviate neuroinflammation, unfolded protein response and endoplasmic reticular stress that all result in genotoxicity and impairment of neural cell functionality and apoptosis, various Nrf2 activators have been proposed in human [23] and animal model settings (see also ClinicalTrials.gov ID NCT01894958). No studies have been performed to personalize potential ASD treatment strategies to enhance NRF2 signaling based on genetic polymorphism. Our study attempted for the first time to link these treatments to NRF2 functional genetic polymorphism in an in vitro model of ASD treatment using the pharmacological NRF2 inductor dimethyl fumarate (DMF). The latter represents a subscription drug to treat Parkinson’s disease and a candidate drug under evaluation for Alzheimer’s disease, Huntington’s disease, and amyotrophic lateral sclerosis [24,25,26]. We believe that thorough justification of re-purposing of the DMF drug to treat ASD on a personalized base is currently highly desirable. The aim of the study was to examine the eventual contribution of functional NRF2 polymorphism to pathogenically significant candidate markers of adaptive responses to genotoxic stress potentially intrinsic to ASD. To that end, we chose a promotor rs35652124 polymorphic site of the NFE2L2 gene that encodes Nrf2 protein based on the proven role of this SNP in the progression of neuropsychiatric disorders and other diseases (see “Discussion”), and determined the frequencies of each allele in a group of children with ASD and healthy volunteers from the same population (Moscow, Russia). In the group of ASD children, we determined the values of Nrf2 expression in lymphocytes at the levels of mRNA and protein product for the purpose of discovering a possible association between the allelic polymorphism and the expression levels of NRF2 and its phosphorylated form (phospho-NRF2) and integral indices of genotoxicity, oxidative stress and cell death (8-oxo-dG DNA modification and expression of H2AX in the immune system cells). Finally, we tried to determine if there is a dependence of the level of NRF2 induction by the specific inducer DMF on the Nrf2 rs35652124 polymorphism in the lymphocytes of children with ASD in vitro. ## 2.1. Study Cohorts The Declaration of Helsinki and the International Conference on Harmonization (ICH) Good Clinical Practice (GCP) Guideline [1998] have been employed to develop a Protocol for this particular study that has received approval from the Ethics Committee of the G.E. Sukhareva Research and Practical Center of Children and Adolescents Mental Health, Moscow Department of Public Health. The Ethics Committee decision number is #3-Jun-27-2017 (dd. 27 June 2017). The ASD cases were recruited from day patients who had been admitted to the preschool division of G.E.Sukhareva Research and Practical Center of Children and Adolescents Mental Health (Moscow, Russia) and Federal Resource Center for Organization of Comprehensive Support to Children with Autism Spectrum Disorders (Moscow, Russia). The clinical group included 181 Caucasian children and adolescents aged 3 to 18 with proven ASD. ASD was diagnosed using the criteria established by the DSM-5. Additionally, the patients underwent the following tests:Autism Mental Status Examination (AMSE) is an 8-item observational assessment that prompts the examiner to observe and document patients’ social, communicative, and behavioral functioning in the context of a routine clinical examination. The AMSE was developed by psychiatrists with autism expertise and is intended to guide clinical judgment in the context of diagnostic decision-making. Childhood Autism Rating Scale (CARS) is a scale for the quantification of the severity of autism pathology. The CARS assesses the child on a scale from 1 to 4 in each of 15 dimensions or symptoms (relating to people; emotional response; imitation; body use; object use; listening response; fear or nervousness; verbal communication; non-verbal communication; activity level; level and reliability of intellectual response; adaptation to change; visual response; taste, smell and touch response; and general impressions). Total scores of or above 30 strongly suggest the presence of autism. Children who have a score from 30 to 36 have mild to moderate autism, while those with scores ranging from 37 to 60 points have severe autism. Communication Questionnaire (SCQ) is a parent questionnaire designed for detecting risk for ASD. The SCQ was originally designed as a screening tool for children 4 years of age or older enrolled in epidemiological research or for studies comparing individuals with ASD and other clinical groups. Exclusion criteria were [1] neurodevelopmental disorders of known etiology (Rett syndrome, fragile X syndrome, or tuberous sclerosis, etc.); [ 2] clinically significant sensory or motor impairment; [3] significant medical conditions known to affect brain development (neonatal brain damage, genetic and/or metabolic syndromes involving the CNS, severe nutritional or psychological deprivation); [4] and a history of inflammatory disorders and allergies. The control group to determine the NFE2L2 allelic frequencies in the general population was formed by 101 adult healthy volunteers who were not relatives of the cases. ## 2.2. Blood Sampling The biological material sampled for testing was venous blood in the amount of 3–4 mL collected from each participant. DNA for genetic typing was isolated from whole blood using Diatom DNA Prep 200 kits pursuant to the instruction enclosed (Isogene, Moscow, Russia). ## 2.3. SNP Selection Criteria When choosing the polymorphism site to be studied, we were guided by three principles. First, minor allele frequency (MAF) should be high enough (≥$15\%$) to ensure representativeness and statistical reliability with a realistic sample size (before the study started, we had found no available data with regard to the Russian population, therefore we had considered the control data of European, American, and Australian Caucasian subjects available in free access in [27]). Second, several published reports should demonstrate the clinical significance of the allele in the pathogenesis of some diseases (see “Discussion”). Six polymorphic variants met this criterion: rs6726395, rs7557529, rs35652124, rs2886162, rs10183914, rs1806649. Finally, the selected SNP should directly affect the Nrf2 expression level, hence we primarily focused on the promotor area searching for mutations with proven affected gene expression status. Based on the above-mentioned criteria, we have chosen one NFE2L2 SNP site: rs35652124, because, unlike the other five variants: (a) the mutation is localized within a promotor region; (b) it associates with the age of Parkinson’s disease onset [19,20,28], as well as with various other diseases in which an oxidation-anti-oxidation balance may pathogenically contribute to the course of the disease [29,30]; and (c) there is multiple evidence that this polymorphism is functional and the alternative alleles associate with distinct levels of NRF2 mRNA [15,31,32] (Table 1). Owing to the selection of the frequent minor allele, it became possible to use data obtained from a sample of adults, not children, as the local controls for comparing with the childhood patients. As judged by the infant mortality in Russia declared for the current year at a relatively low level of 4.997 deaths per 1000 live births [35], it is apparent that even in case of strong negative selection against a particular allele, lethality in infantile age and childhood cannot change the allele frequency in adult population compared to infants. ## 2.4. Polymerase Chain Reaction The following primers were synthesized for NRF2 rs35652124:Direct external (F1)—5′-GTCGCTGGAGTTCGGACGCTT-3′,Reverse external (R1)—5′-GCTTTGGTGGGAAGAGGTTCT-3′,Direct internal (F2)—5′-TCGCAGTCACCCTGAACGCCCT-3′,Reverse internal (R2)—5′-AGACACGTGGGAGTTCAGAGGG-3′. DNA genotyping was performed using tetra-primer polymerase chain reaction (PCR) with GenPak PCR MasterMix Core (Isogene, Moscow, Russia). PCR was carried out in a programmed thermostat GenAmp 9700 (Applied Biosystems, Waltham, MA, USA). The amplification products were separated using electrophoresis in $2\%$ agarose gel followed by visualizing under UV-light (Figure 1). ## 2.5. Flow Cytometry Flow cytometry was applied to measure the 8-oxo-deoxyguanosine fraction in nuclear DNA with primary monoclonal antibodies (SC-66036, Santa Gruz, CA, USA) and secondary antibodies (anti-mouse-FITC, SC-2010, Santa Gruz, CA, USA), double-stranded DNA break degree using antibodies to histones H2AX (NB100-78356G, NovusBio, Englewood, CO, USA) conjugated with DyLight488, as well as expression levels of proteins Nrf2 and its phosphorylated form using anti-NRF-2 antibodies conjugated with Alexa Fluor® 488. Ex: 495 nm, Em: 519 nm (ab194984), and rabbit antibodies to the phosphorylated form NRF-2 (ser40) (bs2013), and secondary anti-rabbit antibodies conjugated with FITC (Sc 2012). ## 2.6. Evaluation of Gene Expression Using Real-Time PCR Gene expression was evaluated with RT-PCR [36]. PCR was performed with the corresponding primers (Synthol, Moscow, Russia) and intercalating SybrGreen dye at StepOnePlus device (Applied Biosystems, San Francisco, CA, USA). As a reference gene expressing in human leucocyte, the housekeeping gene TBP, which codes for a protein binding to TATA motifs (TATA-box Binding Protein), was chosen as the most stably expressed gene in human leukocyte culture [37,38]. RNA was isolated from test specimens (peripheral blood lymphocytes of children with ASD) using YellowSolve kits (Clonogen, Saint Petersburg, Russia), or Trizol reagent (Invitrogen) according to the standard technique [39] followed by phenol-chloroform extraction and precipitation with chloroform and isoamyl alcohol (49:1), or applied RNeasy Mini Kits (“Qiagen”, Hilden, Germany) followed by DNAse I treatment and reverse transcription using Reverse Transcriptase Kit (“Silex”, Russia). We used the following primers (Synthol, Moscow, Russia) (written in the same order (F;R)): TBP (reference gene) (F: 5′-GCCCGAAACGCCGAATAT-3′; R: 5′-CCGTGGTTCGTGGCTCTCT-3′); NRF2 (TCCAGTCAGAAACCAGTGGAT; GAATGTCTGCGCCAAAAGCTG); NQO1 (AGCGAGTGTTCATAGGAGAGT, GCAGAGAGTACATGGAGCCAC) RNA content was determined with dye Quant-iT RiboGreen RNA reagent (MoBiTec, Goettingen, Germany) using the flatbed spectrofluorimeter (PerkinElmer Finland Oy, Turku, Finland) at λexcit = 487 nm, λfluor = 524 nm. Reverse transcription reaction was carried out using chemicals (Sileks, Moscow, Russia) according to the manufacturer’s protocols. The composition of PCR mix per 25 μL: 2.5 μL PCR-buffer (700 mM/L Tris-HCl, pH 8.6; 166 mM/L ammonia sulphate, 35 mM/L MgCl2), 2 μL 1.5 mM/L dNTP solution; 1 μL of 30 pcM/L primer solution for each primer, cDNA. PCR conditions were individually selected for each primer pair. The standard for most primers were the following conditions: after denaturation (95 °C, 4 min), 40 amplification cycles were conducted in a regime as follows: 94 °C–20 s, (56–62) °C–30 s, 72 °C–30 s. Then, 72 °C for 5 min. PCR was performed using the StepOnePlus device (Applied Biosystems, Foster City, CA, USA). Gene expression levels were analyzed in several independent experiments. The data processing was performed using the in-built software with a relative error of $2\%$. ## 2.7. Extraction of cfDNA Fragments from Plasma and cfDNA Quantification Cells were extracted from the blood containing heparin by means of centrifuging at 460× g, followed by mixing of 1 mL of the blood plasma with 0.1 mL of a solution containing $10\%$ of sodium lauryl sarcosylate, 0.2 M EDTA, and 0.075 mg/mL of RNAse A (Sigma-Aldrich, Saint Louis, MO, USA); incubation for 45 min; and treatment with proteinase K (0.2 mg/mL, Promega, Madison, WI, USA) for 24 h at 37 °C. After two purification cycles using the saturated phenolic solution, the DNA fragments were precipitated by adding two volumes of ethanol in the presence of 2 M ammonium acetate. The precipitate was then washed twice with $75\%$ ethanol, dried, and dissolved in water. The DNA concentration was determined by means of measuring the fluorescence intensity after DNA staining with RiboGreen dye (Molecular Probes/Invitrogen, Carlsbad, CA, USA). ## 2.8. Measurement of DNase1 Activity The DNAse1 activity was measured using a protocol as described previously [40]. The substrate for the DNAse1 was synthesized by ‘Syntol’, Russia. The substrate was a double-strand oligodeoxyribonucleotide with a 30-base-pair-sequence, i.e., R6G—ACC CCC AGC GAT TAT CCA AGC GGG-BHQ1. At the 5′-end, the oligonucleotide comprised a fluorescent group. As a result of the endonuclease hydrolysis, elevated dye’s fluorescence was registered. Actually, 10 μL of the blood plasma from a particular sample were added to 90 μL of the DNAse1 solution (10 mM HEPES, pH 7.5, 20 mM MgCl2, 5 mM CaCl2), which contained 3 pM of the oligonucleotide as a substrate. The reaction lasted for 1 h at 37 °C, and the changes in the dye’s fluorescence signal were detected using an Enspire microplate reader (PerkinElmer Finland Oy, Turku, Finland). In oMadison, WI rder to calculate the DNAse1 activity, a calibration curve was plotted, which linked the dye’s fluorescence build-up value with the concentration of a standard DNAse 1 sample (Sigma-Aldrich, MO, USA) in the solution. The activity measurement results are provided in the enzyme units (EU). One EU (ng/mL) corresponds to the activity of the standard DNAse1 solution having a concentration of 1 ng/mL (measured for 1 h at 37 °C). For each sample, not less than three simultaneous measurements were performed with a resultant relative standard error of $5\%$. ## 2.9. Statistical Analysis Statistical data processing and analysis were conducted using the software package PAST v. 2.17c [41]. Besides, the results were statistically processed with the help of the InStat GraphPad software package (GraphPad Software Inc., La Jolla, CA, USA): for binary values, Fisher’s exact tests or chi-square tests were applied followed by calculating p-value with a $95\%$ confidence interval, while quantitative parameters were compared using Mann-Whitney tests and Student’s t-tests. For correlation tests, Spearman rank correlation was calculated. The standard p-value to reject a null hypothesis was $p \leq 0.05.$ Before starting statistical analysis, each sample was tested for anomalous errors (outliers) using Grubbs’s test with the help of an online ‘Outlier calculator’ by GraphPad Software [42]. One anomalous observation of NRF2 protein level was excluded. The allele frequency distribution was tested for compliance with Hardy-Weinberg equilibrium using the χ2 test without Yates adjustment. ## 3.1. Nrf2 rs35652124 Genotype Frequencies in Children with ASD and Healthy Controls The frequencies of rs35652124 polymorphic variants of the Nrf2 gene in children with ASD and healthy adult volunteers (control) are shown in Table 2. The comparisons of frequencies of NFE2L2 allelic variants in ASD children ($$n = 179$$) and the control group ($$n = 101$$) showed no statistically significant difference. ## 3.2. Adaptive Responses for Genotoxic Stress and Their Modulation with NRF2 Inducer in ASD Children Mononuclear Blood Cells Next, in a limited cohort of age- and sex-matched ASD children ($$n = 24$$), selected randomly from the total sample of ASD subjects, we determined the levels of genotoxicity marker, cell-free DNA (cfDNA), and nuclease activity in plasma, the concentration of adaptive response proteins, H2AX, NRF2 and pNRF2, in nuclei of PBMC, and evaluated the potential for modulation of adaptive responses pharmacological NRF2 inducer DMF in vitro in cells from NRF2 rs35652124 genotyped ASD children, Table 3 shows no difference in the content of NRF2 and pNRF2 proteins in cells from children of alternative NRF2 genotypes. However, the content of H2AX protein, blood nuclease activity, and cell-free DNA concentration significantly differed between carriers of CC and T+ (CT + TT) Nrf2 rs35652124 genotypes: ASD children with genotype CC rs35652124 exhibited significantly lower 8-oxo-G level, and increased cfDNA/blood plasma nuclease activity ratio (reduced during chronic oxidative stress [43,44]) (Table 3). As shown in Table 3, when analyzing different genotypes apart, cells with allele C in homozygous state in rs35652124 (CC) demonstrated a higher increment of the content of pNrf2 after the exposure to DMF against the background than carriers of genotypes T+ (CT and TT), with almost doubling after induction ($$p \leq 0.049$$, see Table 4). It is worthy of notice that Grubbs’s test for outliers was performed on pNRF2 expression data at the protein level. The test resulted in the deletion of one outlying case. After deletion, the distribution has become normal (Gaussian), corroborating the correctness of outlier deletion. Power of the test was 0.883, which exceeds the recommended threshold value (0.8), at which the sample size is deemed large enough for the detection of significant differences between groups. The data demonstrated a significant trend in NRF2 genotype-dependent concentration of pNRF2 in which NRF2 CC genotypes exhibited increased responses to the pharmacological NRF2 inducer DMF. When studying possible links between the parameters, we found a statistically significant positive correlation in the levels of Nrf2 protein and H2AX histone protein, a key component of DNA damage response, in the carriers of T+ rs35652124 genotypes (Table 4). ## 4.1. Allelic Frequencies and Possible Clinical Significance Literature search did not reveal reporting on Nrf2 SNP frequencies in infantile autism/ASD (PubMed and SFARI [45]). So, to our knowledge, our research was the first attempt to determine the possible association between ASD and functional NRF2 polymorphism. It is therefore expedient to juxtapose our findings with the reports from NRF2 polymorphism studies in diseases, which also affect the central nervous system, and the pathogenesis of which is also underpinned by impaired oxidative-antioxidative balance. First of all, they include neurodegenerative diseases (Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis), i.e., illnesses accompanied by massive cell death in conditions of chronic oxidative stress, also proven for autism/ASD (see Table 1). The set of principal data is shown below with respect to several NRF2 polymorphisms. Published reports of the role and selective value of rs35652124 alleles are contradictory. Allele T of the promotor SNP rs35652124 in Far Eastern Asian populations (Taiwan, Japan) seems to be a risk allele: genotype TT was associated with hypertension and cardiovascular diseases (CVD). Possible associations of single nucleotide polymorphism rs35652124 (-653A/G) with biomedical measurements and lethality in patients, who had for a long time (9.10 ± 8.28 years) regularly undergone hemodialysis (HD) procedure, were analyzed in diabetic patients (119 male and 97 female patients). As a result, carriers of promotor T-allele rs35652124 were found to have increased CVD risk (OR 2.834; $$p \leq 0.006$$). Carriers of genotype AA (TT) in SNP rs35652124 had higher lethality from CVD compared to carriers of genotypes GG + GA (CC + CT) [29]. In Asians, allele C seems to provide for sufficient NRF2 content, while T does not, affecting among other targets reproductive health: SNP rs35652124 was found associated with oligoasthenozoospermia in Chinese patients, namely, subjects with genotype TT had a higher risk of this diagnosis [15]. The results of a cycle of studies performed by Japanese scientists suggest that NFE2L2 allelic variants are associated with tumorigenesis in the gastrointestinal tract [46] and risk of inflamed bowel syndrome caused by ulcerative colitis. Promotor haplotype rs35652124 C/rs6706649 C was reliably associated with an elevated frequency of p14 methylation, which is a hallmark of Helicobacter pylori infection (OR 2.90; $95\%$ CI 1.14–7.36) [47]. A carrier state of alleles rs35652124 T/rs6706649 C was associated with higher scores of inflammation ($p \leq 0.041$) and a tendency for more severe atrophy of gastric mucosa [46]. In the Japanese population, haplotype rs35652124 C/ rs6706649 C appeared to be protective in regard to gastrointestinal diseases (OR 0.45; CI 0.22–0.93), while Japanese patients, who were heterozygous in each locus (rs35652124 C/T/rs6706649 C/T), were in a greater degree susceptible to ulcerative colitis (OR 2.57; CI 1.01–6.60) [18]. In Caucasians, however, TT might be a protective genotype in regard to Parkinson’s disease. In their research, von Otter with colleagues [19] studied promotor haplotypes (rs35652124 T/rs6706649 C/rs6721961 G) of the NFE2L2 gene in Swedish and Polish cohorts of patients with Parkinson’s disease. The promotor haplotype was found protective with respect to Parkinson’s disease (OR 0.6; CI 0.4–0.9). A meta-analysis of an expanded cohort ($$n = 1038$$), which included Italian, Maltese, and German patients, showed that the promotor allele rs35652124 C (G) increases an individual risk of Parkinson’s disease with earlier onset (the age of onset is decreased by −1.2 year; CI −2.12 to −0.02 year) [20]. The hypothetic difference in adaptive value of the alleles between Asian and European populations is indirectly corroborated by the difference in the frequencies of allele C (G): in Europe, it is a minor allele with a frequency not exceeding 0.25–0.35, supposedly, because of its negative selective value, whereas in Taiwan and Japan, where it is not associated with parkinsonism pathogenesis [48], its frequency reaches 0.4–0.6. A recent study by Ran and colleagues [28] on the European population demonstrated that allele C (rs35652124) was protective with regard to the early onset of Parkinson’s disease (i.e., the presence of allele C delays the onset of clinical signs, hence, prevents its progression). Interestingly, notwithstanding the fact that this mutation affects the promotor region, no association of the patient’s genotype with the expression level of NRF2-specific mRNA was found, in the same manner as in our studies on children with ASD. Perhaps, the peculiarities of a set (compound) of mutations that are included in a haplotype and have an opposite action towards the regulation of NRF2 production prevents in some cases correctly determining the link between expression level and a mutant site in the promotor region. In other words, another NRF2 mutation may alter the effect of the promotor variant allele C (NRF2 rs35652124). Thus, the genetic heterogeneity of each of the disease and the resultant clinical heterogeneity of patterns particularly, age-dependent) are natural obstacles that prevent clarification of pathogenic predictive markers of progression of the ASD and other mental disorders. ## 4.2. Molecular Markers When DNA damage appears due to genotoxic stress, phosphatidylinositol-3 family (PI3K-kinase family) kinases, such as ATM, ATR, and DNA-PK, phosphorylate H2AX to form so-called γ-focuses in DNA lesions to facilitate their repair. We found a significant correlation of protein Nrf2 and histone protein H2AX in the carriers of rs35652124 T+, but not CC genotypes. A study by Gruosso and coauthors [49] revealed a novel mechanism of the relation between the content of H2AX, free radicals (ROS), and Nrf2 in the cells of breast cancer (BC). A model was proposed that elevated ROS exhausted the intracellular pool of the adaptive key repair protein H2AX in chronic oxidative stress typical for BC cells. This process resulted in an increased susceptibility of BC cells to the action of anticancer agents and was mediated by the interaction of protein H2AX with E3 ubiquitine ligase RNF168, which led to proteasomal degradation of H2AX, thereby causing a stable reduction in H2AX in conditions of chronic oxidative stress. In view of the fact that high Nrf2 ensures active transcription of genes for antioxidant enzymes that quench free radicals, the above-mentioned mechanism of heightening susceptibility to genotoxic drugs is only effective in case of low Nrf2 expression. Therefore, in favorable conditions for continuing ROS generation (chronic oxidative stress), one can expect that cells with high NRF2 production would retain high H2AX content, whereas cells with genotypes, which does not provide a sufficient level of NRF2, would demonstrate reduced levels of H2AX. There is a series of reports that an elevated generation of free radicals occurs in children with ASD [4]. The carriers of presumably protective genotype rs35652124 CC (in some reports it is marked as alternative chain, GG) produce enough NRF2 to secure ROS neutralization and avoid a drop of H2AX, whereas in the carriers of genotype rs35652124, T+ H2AX drops with an overabundance of ROS. The ROS dynamics were predicted to be cyclic [50]. Thus, such as in tumor cells [49], similar NRF-H2AX adjoint processes can occur in the immune system cells of children with ASD underpinning the positive correlation of contents of the two proteins we found in the study. At that, carriers of presumably protective genotype rs35652124 CC produce enough NRF2 to secure ROS quenching and avoid lowering H2AX, while in carriers of genotype rs35652124 T+, H2AX is high during low or moderate free radical levels, but drops for the time of ROS surplus periods. Evidence for reduced Nrf2 expression, weakened aerobic respiration, and elevated generation of free radicals in ASD were also obtained on a small sample of children with ASD ($$n = 10$$) compared to healthy controls ($$n = 10$$) [51]. In children with ASD, Nrf2 expression was $45\%$ of the reference level, however, genotypes of children with ASD were not studied in that research. We found that induction of the phosphorylated (that is, active) form of pNRF2 protein was more impressive in carriers of genotype rs35652124 CC as compared to T+, though the significance of the difference was rather marginal ($$p \leq 0.049$$). Also, rs35652124 CC genotype demonstrated, compared to the other genotypes, a lower oxidation rate of circulating cell-free DNA (cfDNA) as determined by the percentage of 8-oxo-deoxyguanosine, which corroborates our data of a higher content of the phosphorylated form of NRF2 in the cells with this genotype. Moreover, in the blood plasma of carriers of genotype rs35652124 T+ we observed lower values of cfDNA/nuclease activity ratio. Low values of this ratio are inherent in chronic oxidative stress and exaggerated cell death [43]. In the aggregate, our findings suggest that genotype rs35652124 CC can be protective with respect to oxidative stress characteristic of the pathogenesis of autism. Our findings demonstrate the potential for clinical significance of NFE2L2 gene polymorphism, which warrants further studies on linking clinically heterogeneous ASD with functional NFE2L2 gene polymorphism. ## 4.3. Nrf2 Induction in the Carriers of Different NFE2L2 Genotypes Another important result of our study is substantiation of the pioneer perspective of personalized approaches to the treatment of patients with neurodegenerative (multiple sclerosis, parkinsonism, Alzheimer’s disease) and neurodevelopmental (autism/ASD) diseases, when the pathogenetically essential fact is insufficiency of antioxidation mechanisms, which could be compensated (at least, in a fraction of patients) by drug inducers of NRF2. Our findings open novel possibilities for the dependence of a significant stimulating effect of DMF on NRF2 functional polymorphism. DMF is currently applied as a drug for the treatment of patients with multiple sclerosis. Few studies conducted in vivo [23,52,53] or in vitro [54] are focused on the prospect of treating children with ASD using another NRF2 inducer, sulforaphane. The studies report a temporary improvement of clinical parameters in a fraction of children. That is why our findings can suggest a possibility that the therapeutic effect of NRF2 inducers or boosters (dimethyl fumarate, sulforaphane, and others), if exists, can to the largest degree emerge in patients with genotype CC rs35652124, rather than CT or TT. Our results suggest that following NRF2 induction with the drug, cells with NRF2 CC rs35652124 genotype can be boosted to a greater extent towards the active form of NRF2 protein. Our study has limitations such as one-time interval after the stimulation and limited cohort of patients, and the absence of age-matched controls to test the similar genotype association in non-ASD children. Certainly, much larger cohorts in future studies will provide more power to establish the most significant links of NRF2 genotypic differences in clinical responses to NRF2 inducers and reveal if NRF2 genetic polymorphism relates to the clinical heterogeneity of ASD course. 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--- title: The Roles of Liver Fibrosis Scores and Modified Stress Hyperglycemia Ratio Values in Predicting Contrast-Induced Nephropathy after Elective Endovascular Infrarenal Abdominal Aortic Aneurysm Repair authors: - Orhan Guvenc - Mesut Engin - Filiz Ata - Senol Yavuz journal: Healthcare year: 2023 pmcid: PMC10048606 doi: 10.3390/healthcare11060866 license: CC BY 4.0 --- # The Roles of Liver Fibrosis Scores and Modified Stress Hyperglycemia Ratio Values in Predicting Contrast-Induced Nephropathy after Elective Endovascular Infrarenal Abdominal Aortic Aneurysm Repair ## Abstract Endovascular aortic repair (EVAR) methods are higher preferred for the treatment of patients with abdominal aortic aneurysms (AAAs). Various markers, including the neutrophil-lymphocyte ratio, have been used to predict the risk of contrast-induced nephropathy (CIN). In this study, we aimed to investigate the role of fibrosis-4 score (FIB-4), aspartate transaminase to platelet ratio index (APRI), and modified stress hyperglycemia ratio (mSHR) values in predicting CIN. Patients who had undergone elective endovascular infrarenal abdominal aortic aneurysm repair in our clinic between January 2015 and January 2022 were included in this retrospective study. Patients who did not develop contrast-induced nephropathy after the procedure were identified as Group 1, and those who did were referred to as Group 2. A total of 276 patients were included in the study. The two groups were similar in terms of age, gender, body mass index, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, hyperlipidemia, and left ventricular ejection fraction. In Group 2, the FIB-4 score, APRI, and mSHR values were significantly higher ($$p \leq 0.008$$, $p \leq 0.001$, and $p \leq 0.001$, respectively). In Group 2, the contrast volume and number of packed blood products used (median 1 (1–4) vs. 2 (1–5)) were significantly higher ($$p \leq 0.003$$ and $$p \leq 0.012$$, respectively). In this study, we demonstrated that we may predict the risk of CIN development with preoperatively calculated, noninvasive liver fibrosis scores and mSHR. ## 1. Introduction Abdominal aortic aneurysms (AAAs) are a prominent cardiovascular disease, and patients with AAAs should be promptly diagnosed and treated. The mortality rate increases greatly when rupture develops, and in recent years, endovascular methods have become highly preferred in the treatment of these patients. These interventions are referred to as endovascular aortic repair (EVAR) [1]. However, there is a risk of renal injury due to the use of contrast media after endovascular interventions, and various inflammatory biomarkers have been the subject of scientific studies seeking to predict this risk. Various precautions, such as ensuring adequate hydration and paying attention to the amount of contrast, are recommended before the procedure. Various markers, including the neutrophil-lymphocyte ratio, have been used to predict the risk of contrast-induced nephropathy (CIN) [2]. Some noninvasive fibrosis scores showing fatty liver have been shown to be important parameters in the prognosis of cardiovascular diseases; the fibrosis-4 score (FIB-4) and aspartate transaminase to platelet ratio index (APRI) are among these scoring systems. Studies have shown their relationship with atherosclerosis. Song et al. analyzed 665 nonalcoholic fatty liver disease subjects without chronic liver disease or heart disease. They demonstrated a significant relationship between FIB-4 scores and coronary atherosclerosis in patients with nonalcoholic fatty liver disease [3]. In another study that included 3433 patients aged ≥40 years, the authors showed a correlation between atherosclerosis and liver fibrosis scores [4]. Moreover, in a study conducted on the general population, a high FIB-4 value was shown to be a risk factor for renal insufficiency [5]. It has recently been shown that stress-induced hyperglycemia affects the prognosis of cardiovascular diseases, independent of whether the patient has diabetes mellitus (DM) [6]. Moreover, in current studies, the stress hyperglycemia ratio (SHR), obtained from the reference glucose value and estimated average chronic glucose levels, has been an important prognostic marker [7]. In this study, we aimed to investigate the role of liver fibrosis scores obtained from preoperative routine blood values and modified stress hyperglycemia ratio (mSHR) values, which we adapted to our study, in predicting contrast-induced nephropathy after endovascular abdominal aortic aneurysm repair. ## 2. Materials and Methods Patients who had undergone elective endovascular infrarenal abdominal aortic aneurysm repair in our clinic between January 2015 and January 2022 were included in the study. Patients who had undergone emergency treatment, patients with pre-procedural creatinine values above 2 mg/dL, those who received nephrotoxic treatment in the preceding week (antibiotherapy due to infection, chemotherapy, etc.), and those with missing data were excluded from the study. After applying the exclusion criteria, 276 consecutive patients were included in the study (Figure 1). Preoperative hemograms of the patients (white blood cell, platelet, neutrophil, and lymphocyte counts and blood glucose value at the beginning of the procedure), biochemistry (alanine transaminase (ALT), aspartate aminotransferase (AST), urea, creatinine, C-reactive protein, sodium, potassium, and magnesium), intraoperative parameters (amount of contrast agent used, blood glucose value at the beginning of the procedure, and duration of the procedure), and postoperative data (daily creatinine values, blood glucose value at the end of the procedure) were all recorded. Patients who did not develop contrast-induced nephropathy after the procedure were identified as Group 1, and those who did were referred to as Group 2. Contrast-induced nephropathy was defined as “0.5 mg/dL or $25\%$ or more increase in serum creatinine value within 48 h after use of contrast material” [8]. Calculation of Scores Used in the Study FIB-4 score = age [years] × AST [IU/L]/(platelets [×109/L] × ALT [IU/L] $\frac{1}{2}$) APRI = AST concentration (IU/L)/upper limit of normal AST (IU/L) × 100/platelet count (109/L) mABG = (Blood glucose level at the start of the procedure + blood glucose level at half an hour after the start of the procedure + blood glucose level at admission to the intensive care unit after the procedure)/3 mSHR = (mABG)/[(28.7 × glycosylated hemoglobin %) − 46.7]. ## 2.1. Preoperative Variables Hypertension was defined as the use of at least one antihypertensive drug and/or arterial blood pressure above $\frac{140}{90}$ mmHg; hyperlipidemia as the use of antilipidemic therapy and/or blood low-density lipoprotein levels above 150 mg/dL; diabetes mellitus as antidiabetic medication use or fasting blood glucose level above 126 mg/dL or above 200 mg/dL during routine examinations; preoperative chronic obstructive pulmonary disease as a post-bronchodilator forced expiratory volume in 1 sec/forced vital capacity of <$70\%$. ## 2.2. Endovascular Procedure All patients whose preoperative preparations were completed were processed in the angiography laboratory. Invasive arterial monitoring was provided to all patients. Two preferably 18 G peripheral vein catheters were placed, and bladder catheterization was performed. Depending on the clinical condition of the patients, the surgical procedure was performed with general or local sedation. At the intervention site, both main femoral arteries were turned and suspended. A 7 French sheet was placed in the middle of the purse sutures placed on the artery. Heparin (10,000 units) was administered to the patients [9]. The diameter of the aorta, wall calcification, presence of thrombus in the vessel lumen, and length of the aneurysm in which the stent graft was to be placed were calculated. After positioning was radiologically confirmed using contrast-enhanced acquisition, the stent-graft was placed (Talent® (Medtronic Vascular, Santa Rosa, CA, USA) or Endurant (Medtronic Vascular, Santa Rosa, CA, USA)). The guide wires were removed, and the purse stitches were tied. The surgical areas were closed, and patients were taken to our intensive care unit for close follow-up. ## 2.3. Statistical Analysis For analysis, IBM SPSS Statistics for Windows version 21.0 (IBM Corp., Armonk, NY, USA) was used. The mean, SD, median (min–max), number, and frequency of the variables were used to express them. To analyze the normality of the numerical data, the Kolmogorov–Smirnov and Shapiro–Wilk tests were utilized. The Mann–Whitney U test was used to evaluate non-normally distributed variables, whereas Student’s t-test was used to study variables with a normal distribution. Categorical variables were compared using the chi-square test. Multivariate logistic regression analysis was used to examine the predictors of CIN. Values with a p-value of <0.2 in univariate analyses were used in the multivariate analysis models. Age, APRI, mSHR, preoperative creatinine value, contrast volume, and packed blood products used were included in Model 1. FIB-4 score, mSHR value, preoperative creatinine value, contrast volume, and packed blood products used were included in Model 2. The receiver operating characteristic (ROC) curve was applied to predict the effects of the FIB-4 score, APRI, and mSHR values. Statistical significance was defined as a p-value of 0.05 or lower. ## 3. Results A total of 276 patients were included in the study. The median ages of the 221 patients in Group 1 and the 55 patients in Group 2 were 71 (65–88) years and 74 (65–82) years, respectively ($$p \leq 0.158$$). The two groups were similar in terms of gender, body mass index, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, hyperlipidemia, and left ventricular ejection fraction. In addition, there was no difference between the two groups in terms of current medical treatment (angiotensin-converting enzyme inhibitor, angiotensin receptor blocker, acetylsalicylic acid, statin, and calcium channel blocker) (Table 1). The patients’ preoperative laboratory values are presented in Table 2. There were no significant differences between the groups in terms of white blood cell count, hemoglobin, platelet count, blood urea nitrogen, aspartate aminotransferase, alanine aminotransferase, C-reactive protein, HbA1c, sodium, potassium, and magnesium levels. In Group 2, the mABG, FIB-4 score, APRI, and mSHR values were significantly higher ($$p \leq 0.008$$, $p \leq 0.001$, $p \leq 0.001$, and $p \leq 0.001$, respectively) (Table 2). The operative and postoperative features of the patients are presented in Table 3. The two groups were similar in terms of operation times, type of anesthesia, and mortality rates. In Group 2, the contrast volume and packed blood products used were significantly higher ($$p \leq 0.003$$ and $$p \leq 0.012$$, respectively). Multivariate logistic regression analysis was performed to predict the factors affecting the development of CIN after the EVAR procedure (Table 4). In the multivariate analysis Model 1, APRI (odds ratio (OR): 0.791, $95\%$ confidence interval (CI): 0.495–0.894, $$p \leq 0.014$$), mSHR (OR: 1.462, $95\%$ CI: 1.110–2.125, $p \leq 0.001$), pre-creatinine (OR: 3.935, $95\%$ CI: 2.571–5.150, $p \leq 0.001$), and contrast volume (OR: 0.632, $95\%$ CI: 0.398–0.814, $$p \leq 0.037$$) were determined as independent predictors of CIN. In Model 2, FIB-4 score (OR: 1.124, $95\%$ CI: 1.060–1.592, $$p \leq 0.005$$), mSHR (OR: 1.639, $95\%$ CI: 1.350–2.780, $$p \leq 0.002$$), pre-creatinine (OR: 4.120, $95\%$ CI: 2.872–6.190, $p \leq 0.001$), and contrast volume (OR: 0.590, $95\%$ CI: 0.386–0.70, $p \leq 0.001$) were determined to be independent predictors of CIN. ROC curve analysis revealed that the cut-off value for mSHR was 1.49 (AUC: 0.801, $95\%$ CI: 0.727–0.874, $p \leq 0.001$, with $70.9\%$ sensitivity and $68.3\%$ specificity), for FIB-4 score was 1.52 (AUC: 0.707, $95\%$ CI: 0.629–0.785, $p \leq 0.001$, with $69.1\%$ sensitivity and $64.8\%$ specificity), and for APRI was 0.28 (AUC: 0.681, $95\%$ CI: 0.596–0.766, $p \leq 0.001$, with $61.8\%$ sensitivity and $66.7\%$ specificity) (Figure 2). ## 4. Discussion Today, the EVAR procedure is easily applied with a high success rate, particularly in infrarenal AAA treatments. However, CIN that occurs due to the use of contrast material can increase the mortality and morbidity of these procedures. For this reason, studies investigating CIN risk factors have been and are being conducted. In this clinical study, we showed for the first time that noninvasive liver fibrosis scores and mSHR values may be risk factors for the development of postoperative CIN. FIB-4 scores and APRI values are important noninvasive parameters indicating liver fibrosis. In addition to being easily available, various studies have demonstrated their prognostic value in cardiovascular diseases. A study by Jin et al. included 5143 patients with stable coronary artery disease, demonstrated by coronary angiography, who were followed for a median of seven years. The FIB-4 score was associated with the severity of coronary artery disease. In addition, the risk of developing cardiovascular events was found to be significantly higher during follow-up when there was a high FIB-4 score (hazard ratio: 1.128, $$p \leq 0.012$$) [10]. In another study conducted by Chen et al., 3263 coronary artery disease patients were included, and the patients were followed for an average of 7.56 (interquartile range: 6.86–8.31) years. A total of 538 deaths occurred during this period, of which 319 were attributed to cardiovascular causes. In this study, FIB-4 scores and APRI values, which we also examined in our study, were found to be associated with cardiovascular and all-cause mortality [11]. Multiple variables involved in CIN cause the renal medulla to become hypoxic. Contrast medium inhibits mitochondrial enzyme activities, which leads to a rise in adenosine through the hydrolysis of adenosine triphosphate and a reduction in the activity of nitric oxide (NO) synthase. Reactive oxygen species are produced during adenosine catabolism, and they consume NO released from endothelial cells subjected to contrast media, along with endothelin and prostaglandin [12]. Additionally, alterations in the proximal tubular cells, podocytes, and mesangial cells’ structural and functional characteristics are linked to ectopic lipids. There is mounting evidence that, in the case of proteinuria, intracellular lipid deposition causes proximal tubule cell dysfunction [13]. The relationship between these parameters (FIB-4 score and APRI value), which noninvasively indicate liver fibrosis, and renal function has been demonstrated. In a study by Schleicher et al. involving a large cohort of the general population, patients were divided into two groups: those with a FIB-4 score ≥ 1.3 ($$n = 66$$,084) and those with a FIB-4 score < 1.3 ($$n = 66$$,084). The study group was followed for 10 years, and renal insufficiency status was recorded. In the patient group with high FIB-4 scores, renal failure developed significantly more often during the follow-up period. At the end of their study, the authors emphasized that the FIB-4 score may be a risk factor not only for liver-related risks but also for renal problems [5]. In another study, Seko et al. included 344 biopsy-proven nonalcoholic fatty liver disease patients. They aimed to investigate the risk factors for the progression of chronic kidney disease in these patients. As a result of this study, high FIB-4 score values were found to be associated with the development of chronic renal failure in DM patients [14]. In a recent study, the relationship between the development of CIN after elective percutaneous coronary interventions and liver fibrosis parameters was investigated. In this retrospective study, which included 5627 patients, CIN developed in 353 ($6.3\%$) patients. The preoperatively calculated FIB-4 scores and APRI values were shown to be significantly associated with the development of CIN [15]. We also found a significant relationship in our study between high FIB-4 scores and APRI values and the development of CIN in patients who had undergone elective EVAR procedures. Hyperglycemia occurs in acute clinical events. The hyperglycemia response of patients to acute conditions, independent of DM, affects clinical outcomes [16]. As a result, oxidative stress increases, prothrombotic pathways are activated, and endothelial damage occurs due to this sudden increase in glucose in the blood [17]. In one study, the role of ABG values in predicting the development of CIN after percutaneous coronary interventions in patients with non-ST elevation myocardial infarction was investigated. In the retrospective study, which included 281 patients, high ABG values were found to be associated with the development of CIN after the procedure [18]. In a meta-analysis investigation that included eight studies, it was shown that procedural hyperglycemia increased the risk of developing CIN after coronary angiography, regardless of whether the patient had DM [19]. In a similar direction, in recent studies, the SHR value was found to be more accurate than the ABG value in predicting mortality and morbidity in acute cardiovascular events [20]. In another study, the SHR value was found to be associated with early mortality after acute myocardial infarction [21]. In a study conducted by Chen et al., the effect of the SHR value on poor outcomes after mechanical thrombectomy was investigated in patients with ischemic strokes. One hundred and sixty patients were included in the retrospective study. At the end of the study, the authors determined that the SHR value calculated from the admission blood values was associated with poor outcomes in the first three months after the procedure [22]. Additionally, Yang et al. investigated the outcome-predicting value of SHR in coronary artery disease patients who had undergone percutaneous coronary interventions. A total of 4362 patients were enrolled in this retrospective observational study. At the end of their study, the authors determined that a high SHR value was associated with poor outcomes [23]. In addition, studies showing the relationship between renal injury and SHR have been reported in the literature. In a study conducted on 1215 patients with DM, the SHR value was shown to be an important parameter in predicting the development of acute renal damage after acute myocardial infarction [20]. In a study conducted by Marenzi et al., the effect of the SHR value on the development of acute renal damage was investigated in DM patients with acute myocardial infarction. A total of 474 patients were included in this prospective study, and acute renal injury developed in 77 ($16\%$) patients. At the end of their studies, the authors determined that the SHR value was a more valuable parameter than the ABG value in predicting renal injury [7]. In a recent study, Liu et al. investigated the importance of the SHR value in predicting acute renal damage after endovascular interventions in patients with ischemic stroke. In this retrospective study, the authors enrolled 717 acute ischemic stroke patients who had undergone endovascular treatment, of whom 205 ($28.6\%$) experienced acute renal injury. At the end of the study, the SHR value was shown to be an independent predictor of the development of acute renal injury (OR: 4.455, $95\%$ CI: 2.237−8.871, $p \leq 0.001$) [24]. In our study, we obtained the mABG value from the blood glucose values at three different times perioperatively in the EVAR procedure that we planned as elective intervention, and we determined the mSHR value based on this parameter. In the multivariate analysis we conducted in our study, we determined that the mSHR value was an independent predictor of the development of CIN. Various studies have investigated the risk factors for CIN after endovascular aortic procedures. In one study that included 167 patients with an endovascular stent placed in the thoracic and abdominal aorta, low preoperative left ventricular ejection fraction and increased blood product transfusion were found to be associated with the development of CIN [25]. In another study, a significant relationship was revealed between preoperative creatinine values and elevated contrast agent use, and the development of CIN after endovascular aortic repair [26]. In a study by Guneyli et al., 139 patients were included retrospectively, and risk factors for the development of CIN after endovascular aortic repair were investigated. CIN developed in 39 ($28\%$) patients, and high preoperative serum urea and creatinine levels were identified as important risk factors for the development of CIN. No significant correlation was found between the amount of contrast used in their study and the development of CIN [27]. We retrospectively included 276 patients who had undergone EVAR and found our CIN rate to be $19.9\%$. We found a significant relationship between increased blood product use, contrast material volume, and preoperative creatinine values, and the development of CIN. There are some limitations to note in our study. Chief among these is that it was planned retrospectively and that it was a single-center study. As a result, the number of patients was limited. In addition, our study did not include quantitative results of liver fibrosis, such as biopsies. Our results now need to be supported by multicenter prospective studies. ## 5. Conclusions In recent years, endovascular methods have come to the fore in the treatment of AAAs. This method can be applied to many patients with very low risk rates. However, CIN that can occur after these procedures is an important postoperative complication. 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--- title: 'Hypercholesterolemia in the Malaysian Cohort Participants: Genetic and Non-Genetic Risk Factors' authors: - Nor Azian Abdul Murad - Yusuf Mohammad Noor - Zam Zureena Mohd. Rani - Siti Aishah Sulaiman - Yock Ping Chow - Noraidatulakma Abdullah - Norfazilah Ahmad - Norliza Ismail - Nazihah Abdul Jalal - Mohd. Arman Kamaruddin - Amalia Afzan Saperi - Rahman Jamal journal: Genes year: 2023 pmcid: PMC10048611 doi: 10.3390/genes14030721 license: CC BY 4.0 --- # Hypercholesterolemia in the Malaysian Cohort Participants: Genetic and Non-Genetic Risk Factors ## Abstract Hypercholesterolemia was prevalent in $44.9\%$ of The Malaysian Cohort participants, of which $51\%$ were Malay. This study aimed to identify the variants involved in hypercholesterolemia among Malays and to determine the association between genetic and non-genetic risk factors. This nested case–control study included 25 Malay participants with the highest low-density lipoprotein cholesterol (LDL-C, >4.9 mmol/L) and total cholesterol (TC, >7.5 mmol/L) and 25 participants with the lowest LDL-C/TC. Genomic DNA was extracted, and whole-exome sequencing was performed using the Ion ProtonTM system. All variants were annotated, filtered, and cross-referenced against publicly available databases. Forty-five selected variants were genotyped in 677 TMC Malay participants using the MassARRAY® System. The association between genetic and non-genetic risk factors was determined using logistic regression analysis. Age, fasting blood glucose, tobacco use, and family history of hyperlipidemia were significantly associated with hypercholesterolemia. Participants with the novel OSBPL7 (oxysterol-binding protein-like 7) c.651_652del variant had 17 times higher odds for hypercholesterolemia. Type 2 diabetes patients on medication and those with PCSK9 (proprotein convertase subtilisin/kexin type 9) rs151193009 had low odds for hypercholesterolemia. Genetic predisposition can interact with non-genetic factors to increase hypercholesterolemia risk in Malaysian Malays. ## 1. Introduction Hypercholesterolemia is one of the risk factors for cardiovascular disease (CVD), the leading cause of mortality and morbidity worldwide [1]. Based on the 2015 Malaysian Ministry of Health report, the overall prevalence of hypercholesterolemia among Malaysian adults was $47.7\%$, and $38.6\%$ were undiagnosed [2]. A recent publication from The Malaysian Cohort (TMC) project, a multi-ethnic population cohort, showed that hypercholesterolemia was prevalent in $44.9\%$ of the participants, of which $51\%$ were of Malay descent [3]. Hypercholesterolemia is a multi-factorial disease; the primary cause is genetic predisposition, and the secondary causes include unhealthy diet, smoking, and hypothyroidism [4]. The genetic disorder in hypercholesterolemia refers to familial hypercholesterolemia (FH), which causes elevated low-density lipoprotein cholesterol (LDL-C) levels [5]. Currently, the genes involved in FH can be divided into tier 1, 2, and 3 genes based on their involvement in lipid metabolism [5,6,7,8]. The tier 1 genes include that for LDL receptor (LDLR), apolipoprotein B-100 (APOB), and proprotein convertase subtilisin/kexin type 9 (PCSK9), which follow the autosomal dominant mode of inheritance, and LDLR adaptor protein 1 (LDLRAP1), which follows the autosomal recessive mode of inheritance (directly implicated in FH) [5,6]. Tier 2 genes are indirectly implicated in FH, but are associated with regulation of LDL or affects the expression of LDL-regulating genes, whereas tier 3 genes are the other genes implicated in lipid regulation [7,8]. FH is a genetic disease with gene dosage effects, being more severe in homozygous compared to heterozygous patients [9]. Most of the common mutations are reported in the LDLR gene ($90\%$) [10]. However, the majority of patients have polygenic hypercholesterolemia, which results from the interaction of genetic factors with a sedentary lifestyle and increased intake of dietary fats [4]. In Malaysia, the frequency of heterozygous and homozygous FH is one in 500 and one in 1 million, respectively, but the actual frequency may be higher due to underdiagnosis [11]. Thus, several studies have attempted to determine the mutational profiles in Malaysian FH [12,13,14,15]. However, most of these studies are small and focus only on either the LDLR or APOB gene. Lye and colleagues investigated 1536 single-nucleotide polymorphisms (SNPs) in 141 FH patients characterized by the Dutch Lipid Clinic Network criteria, and in 111 unrelated controls, and found that 14 SNPs were significantly associated with FH (high-risk: 11, low-risk: 3) [16]. Even then, causative variants were not detected in about $23.4\%$ of FH patients [16], indicating that FH in these patients was caused by possible unknown genetic variants. Therefore, we aimed to comprehensively identify the mutational profiles of participants with hypercholesterolemia of Malay descent from the TMC study via whole-exome sequencing (WES). Subsequently, we validated the findings via genotyping and determined the genetic and non-genetic factors involved in hypercholesterolemia in these patients. ## 2.1. Sampling, Data Collection, and Study Design Participants were selected from the TMC project, an ongoing nationwide prospective project that has recruited 106,527 Malaysians aged 35–70 years [3]. Sociodemographic and lifestyle information were collected via questionnaires and interviews, together with collection of the relevant biophysical and biochemical measurements. The nested case–control study design was used for the discovery phase (WES). Lipid profiles (total cholesterol (TC), LDL-C, triglyceride (TG), high-density lipoprotein cholesterol (HDL-C)) were determined on a Cobas Integra 800 analyzer (Roche Diagnostics, Germany). Based on the Simon Broome criteria, the cut-off points for hypercholesterolemia were 4.9 mmol/L for LDL-C and 7.5 mmol/L for TC. The LDL-C and TC levels were ranked, and 25 participants with the highest LDL-C/TC levels (denoted as HLDL) were selected as the case group. Twenty-five participants with the lowest levels of TC (<5.2 mmol/L) and LDL-C (2.6–3.4 mmol/L) [17] (denoted as LLDL) were selected. All participants were of Malay descent and could be traced back at least three generations. The other inclusion criteria were: (i) Malaysian citizenship; (ii) age 35–70 years; (iii) without debilitating illnesses at the time of the study; (iv) provided written informed consent. The study was approved by the Universiti Kebangsaan Malaysia (UKM) Research Ethics Committee (Ethics Number: FF-205-2007). The characteristics of the 25 HLDL and 25 LLDL participants are summarized in Supplementary Tables S1 and S2, respectively. ## 2.2. DNA Isolation and WES DNA was isolated from 200 µL whole blood using a KingFisher™ Pure DNA Blood Kit (Thermo Fisher Scientific, Waltham, MA, USA) and KingFisher™ Duo system (Thermo Fisher Scientific) according to the manufacturer’s protocols. DNA was quantified with a Qubit™ Broad-Range DNA Quantification Kit (Invitrogen, Carlsbad, CA, USA). High-quality DNA (0.5–1.5 μg) was sheared into 150–250 bp fragments using a Covaris S2 ultrasonicator (American Laboratory Trading, East Lyme, CT, USA). WES was performed using an Ion P1 200 Sequencing Kit (Thermo Fisher Scientific) according to the manufacturer’s protocol. Enriched libraries were sequenced as single-end 150 bp reads on the Ion Proton™ system (Thermo Fisher Scientific). ## 2.3. Bioinformatics Data Analysis Torrent Suite v4.0.2 (Thermo Fisher Scientific) was used for quality control metrics, including bead loading percentage, read length, percentage of alignment to the reference hg19, and total usable sequences. Base recalibration and duplicate removal were applied to the raw data sequence. We ensured that the raw data for each sample had at least 30× mean coverage and 20× coverage for at least $70\%$ of the targeted regions. Germline mutations were detected at 50× coverage [18]. Variants were called using the Torrent Variant Caller plugin with default parameters for Germline Proton TargetSeq Low Stringency. All variants for each sample were annotated and filtered using ANNOVAR [19]. Variants were cross-referenced against the publicly available dbSNP 138, 1000 Genomes Project (April 2012 release), and Exome Sequencing Project databases. The variant effect on the protein structure was predicted using PolyPhen-2 [20] and SIFT [21]; all variants with tolerable effects were filtered out. The variants were classified using the knownGene annotation (University of California Santa Cruz); only variants in exonic regions were considered; synonymous SNPs were filtered out, and frameshift and stop-gain/stop-loss variants were shortlisted for downstream priority over non-synonymous SNPs and non-frameshift insertions/deletions (indels). Variants were also filtered based on whether they were in the FH genes. Tier 1 gene (LDLR, APOB, PCSK9, LDLRAP1) [5,6] variants were given higher priority over tier 2 and 3 gene variants [7,8]. All variants were assessed based on frequency in the HLDL and LLDL samples. Variants exclusively present in HLDL samples were identified as causative, whereas variants present only in the LLDL samples were classified as protective. The impact of these variants was assessed using in silico prediction tools: SIFT [21], PolyPhen-2 [20], MutationTaster [22], and FATHMM [23]. The clinical significance of all known variants was confirmed based on the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar (accessed on 23rd June 2021)). The WES data generated have been submitted to the National Center Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number PRJNA607111. ## 2.4. Internal Validation of 45 Variants In total, 27 variants that increased risk (Supplementary Table S3) and 18 variants that potentially reduced risk (Supplementary Table S4) were selected for replication in a larger sample size. Sample size was calculated assuming an additive model, perfect linkage disequilibrium between risk and marker, $80\%$ power of study, and $47.7\%$ prevalence of hypercholesterolemia [3]. For a genetic risk ratio of 14.78 (APOB rs12720762, minimum allele frequency [MAF] = 0.0056), the sample size needed was 22, while for genetic risk ratio of 1.77 (LDLR rs2569556, MAF = 0.190), the sample size needed was 184 [17]. We replicated the 45 variants in 677 participants (HLDL: 338, LLDL: 339). Similarly to the discovery phase, the participants were selected via nested case–control study design according to their TC and LDL-C levels. Genotyping was performed using an iPLEX® Gold kit on the MassARRAY® System (Agena Bioscience, San Diego, CA, USA) according to the manufacturer’s instructions. Data were analyzed using MassARRAY® Typer v4.0 (Agena Bioscience). Two researchers manually inspected the genotyping reports for all samples independently. The genotypes of low-intensity variants were confirmed using Sanger sequencing using a BigDye™ Terminator v1.1 Cycle Sequencing Kit (Thermo Fisher Scientific) according to the manufacturer’s recommendations. The sequencing data were analyzed using Basic Local Alignment Search Tool (BLAST, http://blast.ncbi.nlm.nih.gov (accessed on 3 June 2020). ## 2.5. Statistical Analyses Descriptive analysis for categorical data is reported as the frequency (n) and percentage (%). Continuous data are described as the mean and standard deviation (SD). Logistic regression modelling was initially performed to identify the association between hypercholesterolemia and individual genes or each environmental risk factor. To produce the most parsimonious model, variables showing no evidence of association (at $p \leq 0.20$) were removed, provided that the removal of the variable produced no substantive changes in the model. Predictive utility and gene–environment (i.e., genetic–non-genetic) interaction were assessed using the variables remaining in the final model. Using the final multivariate model, we estimated the increment in variance explained resulting from adding the genetic variants (risk-increasing or -decreasing) to the model that included non-genetic risk factors only. The risk explained by the risk factors was estimated using Nagelkerke’s pseudo R2. Based on the final model for each group, we calculated the area under the receiver operating characteristic (AUROC) curve and its $95\%$ confidence interval ($95\%$ CI). These statistics measure logistic models’ predictive power and goodness-of-fit. They represent the accuracy with which a model can differentiate between two outcome categories, and thus measure the model’s potential diagnostic utility. An ideal test has an area under the curve (AUC) of 1, whereas random guessing would produce an AUC of 0.5. AUC ≥ 0.8 are often considered clinically useful. We assessed the multiplicative interaction between individual SNPs and non-genetic risk factors using logistic regression. All statistical analyses were performed with SPSS 20 (SPSS Inc., Chicago, IL, USA). ## 3.1. Demographic Data and Non-Genetic Risk Factors Associated with Hypercholesterolemia Table 1 shows the univariable analysis results of the clinical factors associated with hypercholesterolemia in Malays. The data were collected from the internal validation phase ($$n = 677$$). Age at baseline, fasting blood glucose, ever-use of tobacco products, diabetes mellitus (DM) with medication, and family history of hyperlipidemia were associated with increased hypercholesterolemia risk. The mean age at baseline of the HLDL participants was 53.36 (SD 6.36) years compared to the 51.63 (SD 6.46) years of the LLDL participants ($$p \leq 0.001$$). HLDL participants had higher fasting blood glucose [7.15 (SD 3.62) mg/L)] compared to LLDL participants [6.51 (SD 2.79) mg/L)] ($$p \leq 0.012$$). The use of tobacco products increased hypercholesterolemia risk by 1.66 times ($$p \leq 0.005$$). Interestingly, type 2 DM patients with metformin treatment had $38\%$ reduced hypercholesterolemia risk ($$p \leq 0.015$$). Participants with a family history of hyperlipidemia had 2.44 times increased hypercholesterolemia risk ($$p \leq 0.028$$) compared to participants without a family history of hypercholesterolemia. There were no differences in body mass index (BMI), sex distribution, history of stroke, heart failure, obesity with medication, hypertension with medication, family history of hypertension, hyperlipidemia, heart disease, DM, and CVD between the HLDL and LLDL groups. ## 3.2. WES Identification of Risk-Increasing and -Reducing Variants The mean depth of WES for the 50 samples sequenced was 79.19×, with $87.64\%$ of the exome covered at ≥20×. Figure 1 shows the statistics for the coverage of each sample. All samples passed the minimum 30× mean coverage, and $70\%$ of the target regions were covered at 20× after we had removed duplicates. We identified five novel variations among the tier 1 genes: four frameshift deletions (LDLR: 1, PCSK9: 1, LDLRAP1: 2) and one non-frameshift substitution in PCSK9. In addition, 11 known variants were identified (non-synonymous mutations: four in APOB (rs376602710, rs1333175181, rs746414462, rs533617), six in LDLR (rs760436036, rs879254597, rs773658037, rs879254424, rs144172724, rs368708058), one in PCSK9 (rs794728683)) (Table 2). These variants were identified only in the HLDL participants, suggesting that hypercholesterolemia in these patients could be due to genetic factors. Impact prediction of non-synonymous single-nucleotide variants at protein level revealed that seven mutations were damaging (APOB: p. A467G, p.T1222I, p.R1599H, p.H1923R; PCSK9: p. R215H; LDLR: p. E58G and p.E101K) and four were possibly damaging or tolerated. Based on the ClinVar database, five variants were pathogenic/likely pathogenic, two variants had conflicting pathogenicity, one variant was of uncertain significance, and one variant was benign/likely benign. Table 2 lists the variants identified in the tier 1 genes. Only 13 of the 25 HLDL participants carried tier 1 variants, and the number of samples per variant was relatively small. We identified 76 risk-increasing variants in 25 tier 2 genes (Supplementary Table S5). In total, there were 65 novel variants and 11 known variants. The most common variants were in the NYNRIN, CELSR2, PARP10, MAF1, and OSBPL7 (oxysterol-binding protein-like 7) genes (Table 3). These tier 2 genes are not directly implicated in FH, but are associated with LDL regulation and can affect the expression of the LDL-regulating genes. Hypercholesterolemia in these patients could be due to polygenic traits with non-genetic risk factor influences. We analyzed the risk-increasing variants among the tier 3 genes. There were 56 high-frequency variants in the HLDL group (>10 individuals/variant, ≥$40\%$; these variants were not observed in the LLDL group) (Supplementary Table S6). Interestingly, 14 variants (novel: 11, known: 3) had very high-frequency samples per variant (15–19 or 60–$76\%$) (Table 4). Similarly to the tier 2 gene variants, hypercholesterolemia in the patients with tier 3 gene variants could be due to polygenic traits with non-genetic risk factor influences. We also identified 108 risk-reducing variants from 40 genes. There were 17 variants (eight novel and nine known tier 1 protective variants: APOB: 9, LDLR: 1, LDLRAP1: 1, PCSK9: 5) (Supplementary Table S7). Sixty-two tier 2 variants (novel: 19, known: 43) were identified in 10 genes: CELSR2, DCPS, GPAA1, LPA, MAF1, NYNRIN, OPLAH, OSBPL7, PARP10, and SPATC1 (Supplementary Table S8). In tier 3 genes, 23 variants (novel: 17, known: 6) were identified in 23 genes (Supplementary Table S9). Figure 2a,b illustrate the circus plot of the risk-increasing and -reducing variants identified through WES. ## 3.3. Variants and Association with Hypercholesterolemia Risk in Malaysian Malays As both the HLDL and LLDL groups had relatively low prevalence of variants, we performed an internal validation study involving 677 participants. Table 5 shows the significant variants associated with HLDL in Malays. The OSBPL7 variant c.651_652del: p.217_218del was associated with 16.89 times higher odds for hypercholesterolemia ($p \leq 0.001$). The PCSK9 rs151193009 (c.C277T: p. R93C) variant was associated with low odds for hypercholesterolemia ($$p \leq 0.001$$). ## 3.4. Hypercholesterolemia Predictive Models Combining Genetic and Non-Genetic Risk Factors In all three final models, age, fasting blood glucose, and type 2 diabetes on medication were associated with HLDL (Table 6). Participants who were 5 years older had 1.28–1.34 higher odds (odds ratio (OR): 1.055–1.065) for HLDL compared to participants who were 5 years younger. Participants with fasting blood sugar levels higher by 5 mmol/L had approximately two times higher odds (OR: 1.16–1.18) for HLDL. All three models also estimated that diabetic participants on medication had low odds for HLDL. Model 1, which consisted of the non-genetic factors, only explained $11\%$ of the variation in the outcome of HLDL (Nagelkerke’s R2 = 0.11), with an AUC of 0.68 ($95\%$ CI: 0.64, 0.72). In Model 2, participants with a family history of hyperlipidemia had three times higher odds for HLDL (OR: 3.30; $95\%$ CI: 1.44, 7.56). Participants with the novel variant OSBPL7: c.651_652del: p.217_218del had almost 17 times higher odds for high HLDL compared to participants with the wild-type genotype. Incorporating this variant in Model 2 increased the ability of the model to explain the variation of having HLDL to $20.3\%$ [AUC = 0.73 ($95\%$ CI: 0.69, 0.77)]. In Model 3, participants with the CT genotype in PCSK9 rs151193009 had low odds (OR: 0.12; $95\%$ CI: 0.03, 0.42) for HLDL compared to participants with the C genotype. Combining this variant with the non-genetic risk factors slightly increased the chances of HLDL to $14.3\%$ [AUC = 0.69 ($95\%$ CI: 0.65, 0.74)]. There was no evidence of gene–environment (i.e., genetic–non-genetic) interaction between individual SNPs and each non-genetic risk factor. These risk factors for hypercholesterolemia differ slightly between males and females (Supplementary Table S10). In males, history of tobacco use significantly increased risk of hypercholesterolemia by 1.9 times (OR: 1.90; $95\%$ CI: 1.06, 3.37) while those who had diabetes with medication hade $57\%$ reduced risk of hypercholesterolemia (OR: 0.43; $95\%$ CI: 0.24, 0.79), while in females, the factors that increased risk of hypercholesterolemia were age, fasting blood glucose, history of tobacco use, and hyperlipidemia with medication. Similarly in males, diabetes with medication also reduced the risk of hypercholesterolemia in females. For the genetic risk factors, T2FH_OSBPL7_01 increased risk of hypercholesterolemia, while rs151193009 reduced the risk in both males and females. In addition, T2FH_SPATC1_01 reduced the risk of hypercholesterolemia only in males (Supplementary Table S11). ## 4. Discussion In the present study, we identified the genetic and non-genetic risk factors associated with hypercholesterolemia in Malaysian Malays. In total, four of the 18 environmental factors analyzed were associated with increased LDL: age, tobacco use, fasting blood glucose level, and family history of hyperlipidemia, which is consistent with studies on other populations [24]. We identified a novel OSBPL7 (c.651_652del) variant that increased the risk for hypercholesterolemia by 17 times. Patients with type 2 DM on medication and those with the PCSK9 rs151193009 variant showed reduced risk of hypercholesterolemia. The combination of age, tobacco use, fasting blood glucose level, and family history of hyperlipidemia with OSBPL7 c.651_652del increased the hypercholesterolemia risk from $11\%$ to $20.3\%$. Recent studies showed that Malaysian Malays have the highest prevalence of elevated triglycerides and LDL-C in Malaysia [25], and are the second-ranked ethnicity with a high risk of developing cardiovascular disease (CVD) [26]. Importantly, ethnic Malays are the major contributor to the statistics of familial hypercholesterolemia (FH) [27], suggesting a higher influence of genetics on hypercholesterolemia in Malays. By incorporating the variants specific to Malays, these findings could form the basis for early genetic screening of hypercholesterolemia in Malaysia to reduce the morbidity and mortality from related cardiovascular complications. We also showed that age, tobacco use, fasting blood glucose level, and family history of hyperlipidemia increased hypercholesterolemia risk in Malays. Older age is often associated with elevated levels of circulating lipids including LDL-C [28]. One explanation is the change in the lipolysis in adipocytes, in which the reduction of catecholamines and hormone-sensitive lipase by aging causes the adipocytes to reduce their uptakes of the circulating lipids for the storage [29]. Another is the aging-related change in the lipid synthesis pathways (lipolysis, lipid metabolism and lipid transport), in which aging reduces the capacity of the skeletal muscles to oxidize and metabolize the circulating lipids for energy [30]. The hepatic lipid metabolism is also shifted due to aging, whereby lipid synthesis is increased, and fatty acid oxidation is decreased, thus accumulating the lipid particles in the organ [31]. These changes in the metabolic rates subsequently cause increased HDL, LDL, and TG levels [24]. Moreover, aging also increases reactive oxidative species and reduces cellular antioxidant capacity, which leads to increased oxidative stress [32]. This activates 3-hydroxy-3-methylglutaryl–coenzyme A (HMG-CoA) reductase, which increases cholesterol synthesis and LDL-C levels by downregulating LDLR synthesis [32]. However, LDL-C levels also decrease at the age of 50–59 years, possibly due to the low ACAT2 (acetyl-CoA acetyltransferase 2) activity that causes lower very-LDL-C (VLDL-C) secretion and LDL-C production [32,33]. In the present study, patients with high fasting blood glucose or diabetes were more likely to have hypercholesterolemia. The presence of insulin resistance contributes to the dysregulation of lipid metabolism [34]. Thus, the use of diabetes medication such as metformin will likely improve LDL-C levels [35]. Metformin intake reduces blood LDL-C levels by activating adenosine monophosphate (AMP)-activated protein kinase and can suppress fatty acid desaturase (FADS) action [35]. Another hypercholesterolemia risk factor is family history of hyperlipidemia, which increases the risk for developing FH [7]. In the present study, only one lifestyle factor, i.e., tobacco consumption, was associated with higher hypercholesterolemia risk. Tobacco smoking is a known CVD risk factor and is associated with higher serum cholesterol, TG, and LDL-C levels [36]. Nicotine stimulates the production of adrenaline and causes higher serum concentrations of free fatty acids, further inducing hepatic regulation and cholesterol, VLDL, and TG production [36]. All of our non-genetic risk factors were also present in the other populations with high prevalence of hypercholesterolemia. For the Singaporean multi-ethnic population with $52.2\%$ prevalence of hypercholesterolemia, the risk factors are the low education ≤6 years, current smokers, and blue-collar jobs or unemployment with greater unawareness of hypercholesterolemia [37]. In this study [37], the ethnic Malays had the highest risk factors, including for the prevalence of diabetes and hypertension. In another study in Thailand, the prevalence of hypercholesterolemia was $66.5\%$ [38,39]. The regression analysis confirmed that the risk factors included older age, history of alcohol consumption, and family history of dyslipidemia [38,39]. In another Malay ethnic-majority country, Indonesia, the prevalence of hypercholesterolemia is $49.5\%$, and reported risk factors include the inadequate level of physical activity and smoking [40]. From these findings, the Malays in our study had risk factors in concordance with previous publications, and additional fasting blood glucose observed in our study may be due to the additional measurement that was made in our study but was missing in the other publications. We identified 12 novel risk-increasing variants in tier 1 genes (APOB, LDLR, LDLRAP1, PCSK9). As mutations in FH genes are usually ethnicity-specific, the variants might occur in only Malays, but this observation requires validation in other ethnic groups. Four known variants were also identified: rs376602710, rs533617, rs144172724, and rs368708058 [41]. rs376602710 is a missense mutation with uncertain significance in familial hypobetalipoproteinemia (FHBL) and in FH. rs533617 is a missense variant that has been observed in several conditions, including hypercholesterolemia autosomal dominant type B, FH, and FHBL. Interpretations of its pathogenicity are conflicting; therefore, its role in hypercholesterolemia is unknown. rs144172724 is pathogenic and has been identified in patients with FH in Finland, the Netherlands, and France [42]. rs368708058 has been identified in patients with FH in the UK and the Netherlands [41]. The pathogenicity of this mutation is uncertain. In the present study, $48\%$ of the participants could be classified as probable/possible FH based on the Simon Broome criteria or as monogenic FH. Surprisingly, 13 participants ($52\%$) did not have mutations in the FH-related tier 1 genes, suggesting polygenic inheritance. Hypercholesterolemia in patients with tier 2 or 3 gene variants could be due to polygenic traits with non-genetic risk factor influences. We identified 76 risk-increasing variants in 25 tier 2 genes, and the most common variants were in the NYNRIN, CELSR2, PARP10, MAF1, and OSBPL7 genes. The tier 3 genes had a high frequency of variants ($$n = 15$$–19). Most of the tier 3 variants were novel, and only three variants, i.e., rs11243045, rs71557212, and rs1670534, had been identified previously [43]. However, the association of these tier 3 variants with hypercholesterolemia remains unknown. Despite that, these tier 3 variants were only identified in the HLDL participants and not among the LLDL group; thus, we postulate that they could play an important role in lipid metabolism in Malays with hypercholesterolemia. Internal validation of the selected 45 variants in 677 Malay participants with hypercholesterolemia showed that a novel variant in OSBPL7 (c.651_652del) increases hypercholesterolemia risk by 17 times. Overexpression of the OSBPL7 gene can affect serum LDL and TG levels and hepatic TG synthesis [44]. This is possible via SREBP1C (sterol regulatory element–binding protein 1C) [44], a major regulator of lipogenesis. This OSBPL7 variant is the first to be associated with hypercholesterolemia, particularly in Malays. Furthermore, the combination of age, tobacco use, fasting blood glucose level, and family history of hyperlipidemia with the presence of OSBPL7 c.651_652del increased the hypercholesterolemia risk by $8.3\%$. Our study indicates that the OSBPL7 variant might have greater genetic effects on Malay patients. Further studies are needed to understand the role of OSBPL7 in lipid metabolism. The limitation of this study was the validation cohort. We used the same cohort in TMC project because the number of hypercholesterolemia patients in our biobank was insufficient for validation. We are currently recruiting samples of FH patients from the Hospital Chancellor Tuanku Muhriz obesity clinic. We hope to be able to validate these findings in the clinical samples in our future study. Several variants that can reduce the risk of developing hypercholesterolemia were also identified from the LLDL group. In total, 139 were novel, whereas 120 were known; the most common protective variants were identified in the CELSR2, LPA, NYNRIN, OPLAH, PARP10, PCSK9, and SPATC1 genes. PCSK9 rs151193009 reduced hypercholesterolemia risk in Malays, consistent with previous findings on the protectiveness of this variant against high LDL-C and coronary artery disease risk in Asians only [45,46]. PCSK9 is a serine protease that regulates LDLR levels by degradation, and the rs151193009 variant causes PCSK9 loss of function, which in return increases hepatic LDLR expression. Consequently, there is greater removal of cholesterol-rich LDL particles from the plasma [47]. ## 5. Conclusions We have identified the genetic variants associated with hypercholesterolemia risk in Malaysian Malays. Non-genetic risk factors such as age, fasting blood glucose level, history of use of tobacco products, and family history of hyperlipidemia are also associated with hypercholesterolemia. A panel of hypercholesterolemia-associated variants in Malays could be developed for early diagnosis of FH and family screening. Identifying the variants associated with hypercholesterolemia may aid individual risk stratification for hypercholesterolemia for early intervention and disease management. ## References 1. Wong B., Kruse G., Kutikova L., Ray K.K., Mata P., Bruckert E.. 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--- title: 'Prevalence and Associated Risk Factors of Urinary Tract Infection among Diabetic Patients: A Cross-Sectional Study' authors: - Anas Elyas Ahmed - Suhaila Abdelkarim - Maria Zenida - Maisa Ali Hussein Baiti - Atyaf Abbas Yahya Alhazmi - Bushra Ahmed Hussain Alfaifi - Rania Qarmoush Mohammed Majrabi - Nidaa Qasem M. Khormi - Alyaj Alla Ali Hakami - Rafa Abdu Mohammed Alqaari - Raffan Ahmed Alhasani - Ramzi Abdu Alajam - Mohammed M. Alshehri - Aqeel M. Alenazi - Bader Alqahtani - Meshal Alshamrani - Ahmed Alhowimel - Siddig Ibrahim Abdelwahab journal: Healthcare year: 2023 pmcid: PMC10048613 doi: 10.3390/healthcare11060861 license: CC BY 4.0 --- # Prevalence and Associated Risk Factors of Urinary Tract Infection among Diabetic Patients: A Cross-Sectional Study ## Abstract Urinary tract infections (UTIs) are one of the most common long-term complications of diabetes mellitus (DM). Additionally, various factors, such as socio-demographics, type of DM, fasting blood glucose, regular diabetes monitoring, comorbid chronic diseases, HbA1c, body mass index (BMI), and duration of DM, are also thought to predispose individuals to developing UTIs more frequently when they have DM. This research aims to evaluate the risk factors for UTIs and their prevalence among people with DM in Saudi Arabia (KSA). This cross-sectional study was conducted among 440 adults with type 1, type 2, and gestational DM. The participants had to be at least 18 years old, of both genders, and had been suffering from DM for any period of time. A self-administered questionnaire was utilized to collect data on demographic characteristics, such as sex, age, height, weight, material state, education level, income, and clinical profiles of DM and UTI. The crude (COR) and adjusted odds ratios (AOR) were calculated using logistic regression in the IBM SPSS software. The incidence of types 1 and 2 DM and gestational diabetes reached 34.1, 60.9, and $5\%$, respectively. Most of the participants had first-degree relatives with DM ($65.9\%$). UTI was common in $39.3\%$ of participants. A chi-squared statistical analysis revealed that the frequency of UTI varied depending (χ2 = 5.176, $$P \leq 0.023$$) on the type of DM. Burning urination and abdominal pain were the most common symptoms. The CORs for sex, marital status, hypertension, and BMI were significant ($P \leq 0.05$) and had values of 2.68 ($95\%$ CI = 1.78–4.02), 0.57 ($95\%$ CI = 0.36–0.92), 1.97 ($95\%$ CI = 1.14–3.43), and 2.83 ($95\%$ CI = 1.19–2.99), respectively. According to the adjusted model, only sex influenced the occurrence of UTIs. The AOR for sex was 3.45 ($95\%$ CI = 2.08–5.69). Based on this study, the authorities related to the health of DM patients can use its findings to guide awareness programs and clinical preparedness. ## 1. Introduction Diabetes mellitus (DM) has recently been considered a growing health problem worldwide. In 2019, the global prevalence of DM was estimated to be $9.3\%$ (463 million people); it is expected to rise to $10.2\%$ (578 million) by 2030 and $10.9\%$ (700 million) by 2045 [1,2]. In Saudi Arabia (KSA), the prevalence of DM has increased dramatically during the last few decades. At present, it is considered the most prevalent disease in the KSA [3]. The Saudi Health Interview Survey (SHIS) in 2013 showed that the total prevalence of diabetes was $14.8\%$ and $11.7\%$ for males and females, respectively. The prevalence increased with age and ranged from $7.8\%$ among those aged 25 to 34 to $50.4\%$ among those 65 and older. In men, one million were diabetic, 583,000 were on medication for diabetes, and 230,000 had uncontrolled diabetes. For women, 720,000 were diabetic, 367,000 were on medication, and 167,000 had uncontrolled diabetes [4,5]. Diabetic patients are at higher risk for all infections than non-diabetic patients, such as lower respiratory infections, UTIs, sepsis, endocarditis, skin infections, bone infections, joint infections, and mucous membrane infections [6]. Existing data indicate that the most common bacterial infection in diabetic patients is a UTI [7]. According to a study conducted in the KSA, the overall prevalence of UTIs in diabetic patients was $25.3\%$, $7.2\%$, and $41.1\%$ in males and females, respectively. Many risk factors increase UTI frequency among diabetic patients [8,9]. Different studies confirmed that high blood glucose levels that are not adequately controlled could provide a rich source of nutrients for bacteria. Additionally, weakened immune systems in diabetic patients, such as decreased T-cell-mediated immune response and impaired bladder emptying due to autonomic neuropathy, may raise the risk of UTIs in diabetic patients since urine stays in the bladder for too long and becomes a breeding ground for bacterial growth [9,10]. Furthermore, among people with diabetes, females are at higher risk of UTIs than males due to their anatomy and reproductive physiology. A BMI greater than 30 kg/m2 was also discovered to be one of the risk factors associated with UTIs in diabetic patients in the KSA [10,11,12]. The fact that people with diabetes are more likely to suffer from UTIs and that the number of people with diabetes around the world has been growing in recent years may place a big financial strain on healthcare [13]. Further encouraging the development of an antibiotic-resistant urinary pathogen may be the high rates of antibiotic prescriptions, especially broad-spectrum antibiotics, for UTIs in these individuals [14,15]. UTIs in diabetic patients are more common and can lead to severe complications and potentially life-threatening conditions, such as renal papillary necrosis, renal or perirenal abscess, emphysematous pyelitis/cystitis, and emphysematous pyelonephritis, as well as urosepsis and bacteremia [12,16,17]. No studies have assessed the risk factors for UTIs among people with DM in the Jazan population in southwest of the KSA. Therefore, this study aimed to determine the prevalence of UTIs and the factors that put people with diabetes at an increased risk for developing UTIs in Jazan, as well as the incidence of UTI complications. In addition, a multivariate analysis was carried out to determine the causes of UTIs. ## 2.1. Study Design, Area, and Duration For the current study, a cross-sectional design was appropriate for an observational epidemiological study. The investigation occurred at the Diabetes and Endocrinology Center in Jazan, KSA. In 2012, the center was launched to serve people with diabetes. The center offers numerous necessary services to its visitors. It includes a diabetes clinic for adults and children, an eye examination clinic for diabetes, a nerve and artery examination clinic, two health education clinics, two diabetic foot clinics, and a therapeutic nutritional clinic [18]. During the first six months of the 2020 calendar year, the center received 24,619 visits [18]. This study was conducted from December 2021 to June 2022. ## 2.2. Inclusion and Exclusion Criteria The inclusion criteria were diabetic patients aged 18 years and older attending the Diabetes and Endocrinology Center in Jazan, KSA. Pregnant women, people under the age of 18, those with known urinary tract abnormalities, those who had recently used antibiotic therapy, those who had recently been hospitalized, and those who had surgery within the last four months were all excluded. ## 2.3. The Sample Size and Sampling Technique The estimation was based on a sample size calculation for cross-sectional study design (n = (Z)2(1 − α)P(1 − P)/d2), using the following parameters: anticipated population proportion (P) = $50\%$, a $95\%$ confidence level, and an error not greater than $5\%$. The sample size for this study was calculated to be 400 participants among the diabetic patients at the Diabetes and Endocrinology Center in Jazan. Additionally, we assumed a refusal rate of $10\%$. The data were collected from 440 participants. Simple random sampling was applied. ## 2.4. Data Collection and Study Measures The data for this study were collected using a self-administered questionnaire. The questionnaire was designed in Arabic to be suitable for the participants. The questionnaire collected data about demographic characteristics, such as sex, age, height, weight, material state, education level, and income. Additionally, data about DM, such as type of DM, duration, glycemic profile, medications, adherence, and comorbidities, were acquired. The patients were also asked how often they had a UTI infection in the previous 12 months, as well as some risk or protective factors associated with UTIs, such as fasting glucose levels and HA1C. ## 2.5. Pilot Study and Pretesting A pilot study was conducted with 20 participants to test if the questionnaire’s wording was clear and understandable. Each participant in this pilot study was asked to read and sign a consent form before data collection. The data from the pilot study were analyzed but not included in the main study. ## 2.6. Data Analysis IBM SPSS version 23 was used for data entry and analysis. Means, standard deviations, frequencies, and percentages characterized the study variables. At a deeper level of data analysis, the chi-squared test was utilized to look for patterns. It was decided that a result was significant if the P-value was less than 0.05. Logistic regression modeling (LRM) was used to analyze the association between the dependent variable (UTI incidence) and the risk factors. The crude and adjusted odds ratios (OR) were obtained for all the independent variables. All variables included in the model were categorical except for age, which was incorporated as a continuous variable. The ORs were obtained with a P-value and $95\%$ confidence intervals. The goodness of fit of the data to the multivariate logistic regression was tested using the Hosmer–Lemeshow test. ## 2.7. Ethical Considerations Ethical approval was obtained from the Standing Committee for Scientific Research (REC-$\frac{43}{09}$/207). Additionally, informed consent was included in the questionnaire, and the participants’ consent was confirmed before data collection. Each participant was asked to read and sign a consent form before the start of data collection. The data did not include relevant participant details, such as name, file number, and telephone number. ## 3. Results A total of 440 participants were recruited from the Diabetes and Endocrinology Center in the Jazan region, KSA, of whom $46.6\%$ were males, $53.3\%$ were females, and $63.8\%$ were married. More than two-thirds ($69.5\%$) of the sample were over 35 years old, with a mean age of 44.36 and a standard deviation of 14.81. The percentage of university degree holders was $55.9\%$. Participants in the income range of SAR 5 to 15 K (USD 1 = SAR 3.766) made up more than half of the sample ($55.4\%$). Participants with type 1 and type 2 DM and gestational diabetes were $34.1\%$, $60.9\%$, and $5\%$, respectively. One-third of the participants’ fathers held a high school diploma, while two-thirds of their mothers were illiterate. About $63.4\%$ of the participants had a BMI over 26. More details are presented in Table 1. The distribution of diabetes types by gender is shown in Figure 1. A chi-squared statistical analysis reveals that disease rates vary depending (χ2 = 4.335, $$P \leq 0.037$$) on gender. Most of the participants had first-degree relatives with diabetes ($65.9\%$). Only $16.4\%$ of the participants had no family history of diabetes among their first-degree relatives. The majority of participants ($69.3\%$) are committed to a diabetes treatment, and about half ($49.3\%$) regulated their blood sugar levels using pills. More details are depicted in Table 2. The prevalence of previous UTIs was $39.3\%$. Over half of the participants ($60.6\%$) suffered from a UTI 1–2 times a year (Table 3). Approximately one-third of the participants experienced complications from a urinary tract infection. About $37\%$ of the participants experienced UTI complications. Burning urination and abdominal pain were the most common symptoms. The distribution of the frequency of UTIs by type of DM is shown in Figure 2. A chi-squared statistical analysis reveals that the frequency of UTIs varies depending (χ2 = 5.176, $$P \leq 0.023$$) on the type of DM (Table 3). A logistic regression model was conducted to obtain the crude odds ratio (COR) and adjusted odds ratio (AOR) for all the included factors (gender, marital status, educational level, income, type of DM, fasting blood glucose, regular DM checking, chronic diseases, HbA1c, BMI, and duration of DM). The dependent variable is the diabetic patients’ previous exposure to a UTI. A univariate linear regression model was conducted to calculate the COR and its statistical significance for each independent variable separately. Without adjusting or adding all the variables in the multivariate logistic model, the initial results show that sex, marital status, chronic diseases, and BMI are individually and statistically significant factors (Table 4). The CORs for sex, marital status, hypertension, and BMI are 2.68 ($95\%$ CI = 1.78–4.02), 0.57 ($95\%$ CI = 0.36–0.92), 1.97 ($95\%$ CI = 1.14–3.43), and 2.83 ($95\%$ CI = 1.19–2.99), respectively. Then, a multivariate logistic regression model was used to calculate the AORs. However, before that, it was necessary to ensure the validity of the data to be included in the model using the Hosmer–Lemeshow test. The goodness of fit of our data to the multivariate logistic regression was assured ($P \leq 0.05$). Including all independent variables in the multivariate logistic regression model led to the adjustment of some values of the ORs. It is found that sex is the only factor that shows statistical significance (Table 4). The AOR for sex is 3.45 ($95\%$ CI = 2.08–5.69). ## 4. Discussion The purpose of this study, which is the first of its kind, was to evaluate the prevalence of urinary tract infections (UTIs) among diabetic patients in Jazan, KSA, using a cross-sectional study and to analyze the risk factors associated with UTIs using logistic regression modeling. In the KSA, the prevalence of DM has increased dramatically during the last few decades. At present, it is considered the most prevalent disease in the KSA [3]. According to a study conducted in the KSA, the overall prevalence of UTIs in diabetic patients was $25.3\%$, $7.2\%$, and $41.1\%$ in males and females, respectively [4,5]. The current study employed a representative sample of diabetic patients (Table 1). Together, the growing number of diabetic patients with UTIs and the growing number of people with diabetes around the world in recent years may place a big financial strain on healthcare [13]. According to the Saudi Health Interview Survey (SHIS), which was conducted in 2013, $14.8\%$ of men and $11.7\%$ of women had diabetes. This difference was seen between the sexes [3,8]. Figure 1 shows that the current findings align with the previous report [3,8]. The chi-squared statistical analysis reveals that disease rates vary depending (χ2 = 4.335, $$P \leq 0.037$$) on the gender of the diabetic patients. It should be noted that this chi-squared analysis did not include gestational diabetes. According to the World Health Organization and the American Diabetes Association’s respective standards, the prevalence of gestational DM has grown in many racial and ethnic groups over the previous 20 years [19]. As reported in this study, the prevalence of gestational DM is $5\%$ in the Jazan region, KSA. The overall prevalence of gestational DM is $13.2\%$ in Germany [20], $18.9\%$ and $17.8\%$ in India [21], $8\%$ in Egypt [22], $12.5\%$ in Riyadh, KSA [23], $18.7\%$ in Abha, KSA [24], $6.4\%$ in Qatar [25], $24.5\%$ in Morocco [26], and $3.7\%$ in China [27]. In Australia, it was discovered that women whose country of origin was China or India had a greater frequency of gestational DM than women whose county of birth was Europe or Northern Africa [28]. The increased risk of UTIs among diabetic patients and the rise in DM prevalence worldwide in recent years may place a significant financial burden on healthcare [13]. Existing data indicate that the most common bacterial infection in diabetic patients is a UTI [7]. The prevalence of UTIs is $39.3\%$. More than half ($60.6\%$) of the participants in the current research suffer from a UTI 1–2 times each year. A study conducted in the KSA found that the overall prevalence of UTIs in diabetic patients was $25.3\%$ [8]. The prevalence of diabetic UTIs was reported to be $13.8\%$ in Ethiopia [29], $17.5\%$ in India [30], and $9.71\%$ in the USA [31]. A logistic regression model was used to obtain the COR and AOR for each component. Each independent variable’s COR and statistical significance were calculated using a univariate linear regression. The initial results show that sex, marital status, chronic illnesses, and BMI are statistically significant contributors (Table 4). The CORs for sex, marital status, hypertension, and BMI are 2.68, 0.57, 1.97, and 2.83, respectively. According to a study from the KSA, female sex, hypertension, insulin treatment, a body mass index (BMI) of more than 30 kg/m2, and nephropathy all raise the incidence of UTIs in diabetic patients [8]. The AORs were calculated using a multivariate logistic regression, and all independent variables were included in the multivariate logistic regression to obtain the adjusted OR results. Sex is the only statistically significant factor (Table 4). The AOR is 3.45 ($95\%$ CI: 2.08–5.69) for sex. According to previous research using administrative data from the US population, women had a significantly higher annual incidence of UTIs than men ($12.9\%$ vs. $3.9\%$) [32]. In addition, studies with a similar geographic population to ours have indicated the predisposition of the female gender to UTIs [33]. Many variables have been linked to UTIs in people with diabetes in previous studies. Their findings indicate that sex, educational attainment, and a UTI history are predictors. These outcomes are consistent with research from Saudi Arabia [8], China [34], Kuwait [35], and the United States [32]. Due to anatomical differences in the urinary system, women typically have more UTIs than men, which may account for the disparity between the sexes. The lower levels of typical vaginal flora (Lactobacilli), the less acidic pH of the vaginal surface, the poor sanitary conditions, the short and wide urethra, and the proximity to the anus may all contribute to the increased infection rates of UTIs in our study’s female participants. Most patients had HbA1c values of seven or more; these levels were unrelated to the patients’ UTI status, either positive or negative, and had only a weak correlation. This supports the finding from a meta-analysis of 22 studies that the degree of HbA1c derangement does not necessarily impact the biological flora or play any role in UTI susceptibility [36]. Due to the limitations of the cross-sectional study design, it was not possible to find out what caused certain factors to be linked to UTIs in the sample population. Additionally, this study did not look at factors such as where people lived, if they smoked, or how much alcohol they drank, which could have changed the results. One of the drawbacks is that this study was restricted to one province. It might only represent some Saudis, making the generalization of the study’s findings challenging. ## 5. Conclusions The analysis reveals that UTI rates vary depending on the gender of the diabetic patients and that the frequency of UTIs varies depending on the type of DM. In conclusion, the present study shows that the female gender has a higher risk of UTIs among people with diabetes. Based on this study, the authorities related to the health of diabetic patients can use its results to guide awareness programs and clinical preparedness. The findings can be helpful for high-risk patients in selecting the most appropriate infection prevention strategies. ## References 1. Saeedi P., Petersohn I., Salpea P., Malanda B., Karuranga S., Unwin N., Colagiuri S., Guariguata L., Motala A.A., Ogurtsova K.. 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--- title: Efficacy of N-Acetylcysteine on Endometriosis-Related Pain, Size Reduction of Ovarian Endometriomas, and Fertility Outcomes authors: - Emanuela Anastasi - Sara Scaramuzzino - Maria Federica Viscardi - Valentina Viggiani - Maria Grazia Piccioni - Laura Cacciamani - Lucia Merlino - Antonio Angeloni - Ludovico Muzii - Maria Grazia Porpora journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048621 doi: 10.3390/ijerph20064686 license: CC BY 4.0 --- # Efficacy of N-Acetylcysteine on Endometriosis-Related Pain, Size Reduction of Ovarian Endometriomas, and Fertility Outcomes ## Abstract Background: Endometriosis is a chronic, estrogen-dependent, inflammatory disease, whose pivotal symptoms are dysmenorrhea, dyspareunia, and chronic pelvic pain (CPP). Besides the usual medical treatments, recent evidence suggests there are potential benefits of oral N-acetylcysteine (NAC) on endometriotic lesions and pain. The primary objective of this prospective single-cohort study was to confirm the effectiveness of NAC in reducing endometriosis-related pain and the size of ovarian endometriomas. The secondary objective was to assess if NAC may play a role in improving fertility and reducing the Ca125 serum levels. Methods: Patients aged between 18–45 years old with a clinical/histological diagnosis of endometriosis and no current hormonal treatment or pregnancy were included in the study. All patients received quarterly oral NAC 600 mg, 3 tablets/day for 3 consecutive days of the week for 3 months. At baseline and after 3 months, dysmenorrhea, dyspareunia and CPP were assessed using the Visual Analog Scale score (VAS), while the size of the endometriomas was estimated through a transvaginal ultrasound. Analgesics (NSAIDs) intake, the serum levels of Ca125 and the desire for pregnancy were also investigated. Finally, the pregnancy rate of patients with reproductive desire was evaluated. Results: One hundred and twenty patients were recruited. The intensity of dysmenorrhea, dyspareunia and CPP significantly improved ($p \leq 0.0001$). The use of NSAIDs ($$p \leq 0.001$$), the size of the endometriomas ($p \leq 0.0001$) and the serum levels of Ca125 ($p \leq 0.0001$) significantly decreased. Among the 52 patients with reproductive desire, 39 successfully achieved pregnancy within 6 months of starting therapy ($$p \leq 0.001$$). Conclusions: Oral NAC improves endometriosis-related pain and the size of endometriomas. Furthermore, it decreases Ca125 serum levels and may improve fertility in patients with endometriosis. ## 1. Introduction Endometriosis is a chronic, estrogen-dependent, benign, inflammatory disease, affecting between 2–$10\%$ of women in childbearing age, characterized by the presence of endometrial glands and stroma outside the uterine cavity [1]. The pivotal symptom of endometriosis is pain, presenting as dysmenorrhea, dyspareunia, or chronic pelvic pain (CPP), and it is associated with infertility in up to $50\%$ of cases [2,3]. Environmental, dietary, genetic, and immune factors seem to be involved in the etiopathogenesis of endometriosis [4,5,6,7,8,9,10,11]. Above all, the exposure to non-persistent endocrine disruptors, such as dioxins, polychlorinated biphenyls, bisphenol A and phthalates, seems to increase the risk of developing endometriosis, due to the effects on both the endocrine and the immune systems, and to a different capacity in bioactivation and/or detoxication due to both genetic makeup and/or induction/inhibition phenomena in the exposed population [9,10]. The immune system in turn plays a key role in the pathogenesis of endometriosis; growth, angiogenic and adhesion factors, along with proinflammatory cytokines, reside in both the peritoneal fluid and in the endometrium of these patients, enhancing a leading role of inflammation in the initiation and progression of the disease [12]. The inflammatory response in the peritoneal cavity provokes the release of reactive oxygen species (ROS). The ROS include free radicals and non-free-radical oxygen intermediates, which can act on the cellular components and DNA, inducing damage [13]. In physiological conditions, the ROS and the antioxidants are in balance. In case of the ROS overbearing, cellular damage is triggered, causing the downregulation of protein activity and gene expression [14], with subsequent inflammation [15]. Oxidative stress induces the upregulation of these molecules, and it is responsible for the local destruction of the peritoneal mesothelium, for creating adhesion sites for the ectopic endometrial cells, and for promoting apoptosis [16]. The ROS are also implicated in the pathogenesis of endometriosis-related infertility. In fact, the damage produced by the ROS may contribute to a reduced oocyte quality and impaired ovulation [17]. Therefore, an antioxidant diet and the administration of antioxidant drugs may be complementary treatments that go alongside the hormone therapy. N-acetylcysteine (NAC) is a substance that exerts antiproliferative and antioxidant effects on tissues. It facilitates the proliferation-to-differentiation switch and downregulates the gene and inflammatory proteins expression [18]. NAC is a precursor of the antioxidant glutathione, with well-established antioxidant and anti-inflammatory properties, due to both a direct and indirect effect. The direct effect relates to the presence of a free thiol group which scavenges the ROS, while the indirect effect involves its ability to enter the cells and to react with glutamic acid and glycine, increasing the levels of free glutathione which reduces the ROS [19]. In previous studies, we demonstrated that in both animal and human tissues NAC causes a significant reduction in the size of endometriotic lesions and improves pain symptoms [20]. Due to its strong anti-inflammatory action, NAC may also reduce Cancer Antigen 125 (Ca125) production and improve fertility. The objective of this prospective observational single-cohort study was to confirm the effectiveness of N-acetylcysteine in reducing endometriosis-related pain and the size of ovarian endometriomas and to assess a potential role in the reduction of Ca125 serum levels and improvement fertility. ## 2. Materials and Methods Between January 2020 and April 2022, patients with endometriosis referred to the Endometriosis outpatient service of Policlinico Umberto I -Sapienza University Hospital, were enrolled in this prospective single-cohort study. All recruited patients signed an informed consent to the study. The study was approved by the local Ethic Committee (n. $\frac{5926}{2020}$). Inclusion criteria were age between 18 and 45 years old and clinical/instrumental or surgical/histological diagnosis of endometriosis. Exclusion criteria were pre-menarche or menopause, known hypersensitivity or previous adverse reaction to N-acetylcysteine, current hormonal treatment, cancer and ongoing pregnancy. Age, body mass index (BMI), parity, comorbidities, previous surgery, previous medical treatment, intake of nonsteroidal anti-inflammatory drugs (NSADs), size of ovarian endometriomas, presence of dysmenorrhea, dyspareunia and CPP and desire for pregnancy were investigated. Quarterly therapy with 600 mg oral N-acetylcysteine (3 tablets/day, for 3 consecutive days of the week) was prescribed to all patients for 3 months. Dysmenorrhea, dyspareunia, non-cyclic CPP, size of ovarian endometriomas, and Ca125 levels were evaluated at the beginning of the treatment (t0) and after 3 months (t3). The size of ovarian endometriomas was estimated by a transvaginal ultrasound scan (TVUS) performed by the same expert operator (GE Voluson E6, transvaginal 6 MHz volume probe with 3D scan, GE Healthcare, Milwaukee, WI, USA). Pain symptoms were assessed through the Visual Analogical Scale (VAS), consisting of a 10-point score defining pain as mild (VAS 1–4), moderate (VAS 5–7) and severe (VAS 8–10). Furthermore, serum levels of Ca125 were measured on the 8th day of the menstrual cycle in every patient. CA125 levels were determined using the CA125 non-competitive, indirect, two-step sandwich chemiluminescent immunoenzymatic (CLEIA) method conducted on the LUMIPULSE® G1200 automated analyzer (Fujirebio Diagnostics). According to the manufacturer’s indications, normal values of CA125 were considered to be <35 U/mL. Lastly, fertility outcomes among patients with reproductive desire were evaluated 6 months after starting the therapy, assessing the occurrence of pregnancy, the potential history of infertility, the spontaneous pregnancy rate, and the pregnancy rate after Assisted Reproduction Techniques (ART). Statistical analysis was performed using SPSS, version 26 for iMac (IBM, SPSS Statistics, Bologna, Italy) provided by “Sapienza” University of Rome. A preliminary descriptive analysis was performed to assess the patients’ general characteristics. The Shapiro—Wilk test was applied to test for a normal distribution. According to the normal or not-normal distribution, continuous variables were compared using the paired T-test or the Wilcoxon test, as appropriate. Categorical variables were compared using the McNemar test. Statistical significance was set at a p-value < 0.05. ## 3. Results One hundred and twenty patients were included in the study. The average age was 33.2 ± 6.7 years old. The mean BMI was 22.2 ± 3.9. Thirty-four patients ($28.3\%$) had a history of previous surgery for endometriosis. One hundred and four patients ($86.6\%$) had received hormonal treatment before the recruitment, 92 ($88.5\%$) with combined oral contraceptives (COC) or oral progestins, 3 ($2.9\%$) with GnRH analogues and 9 ($8.6\%$) with an intrauterine device releasing Levonorgestrel. Thirty-nine patients ($32.5\%$) had already had at least one pregnancy before the recruitment. The main characteristics of the study population are reported in Table 1. At t0, 97 patients ($80.3\%$) reported dysmenorrhea, with an average VAS score of 6.9 ± 2.0. At t3, dysmenorrhea was still present in 84 patients ($70\%$, $$p \leq 0.001$$) but the mean VAS score was 4.8 ± 1.8 ($p \leq 0.0001$). Dyspareunia was present in 56 patients ($46.7\%$) at t0 and in 50 patients ($41.7\%$) at t3 ($p \leq 0.05$) with a significant reduction of mean VAS score, 6.5 ± 1.7 at t0 vs. 4.9 ± 1.7 at t3 ($p \leq 0.001$). CPP was reported by 50 patients ($41.7\%$) at t0 and by 45 patients ($37.5\%$) at t3 ($p \leq 0.05$), with a significant improvement of mean VAS score, 7.2 ± 1.8 at t0 vs. 5.7 ± 2.0 at t3 ($p \leq 0.001$). NSAIDs intake also reduced from $63.3\%$ ($$n = 76$$) to $53.3\%$ ($$n = 64$$, $$p \leq 0.001$$). A statistically significant reduction of the endometriomas’ average size was observed, changing from 36.5 mm ± 25.4 mm at t0 to 33.0 mm ± 23.5 mm at t3 ($p \leq 0.001$). The Ca125 average serum levels significantly decreased from 45.55 U/mL ± 26.5 U/mL at t0 to 35.6 U/mL ± 24.2 U/mL at t3 ($$p \leq 0.001$$). The main results of the study are reported in Table 2. Fifty-two patients ($43.3\%$) had a pregnancy desire, and twenty-three patients ($19.1\%$) reported a history of infertility. Among the fifty-two patients with reproductive desire, thirty-nine ($75\%$) had a spontaneous pregnancy within 6 months ($$p \leq 0.001$$), while six ($11.5\%$) successfully achieved pregnancy through ART, four through intracytoplasmic sperm injection (ICSI) and two through in vitro fertilization (IVF) (Table 3). Although it was not an aim of the study, we observed a significant decrease in the BMI, as it reduced from 22.2 kg/m2 ± 3.9 kg/m2 at t0 to 21.2 kg/m2 ± 3.8 kg/m2 at t3 ($$p \leq 0.03$$). No side effects were observed. ## 4. Discussion Endometriosis is a chronic disease, influenced by environmental, genetic, and epigenetic factors. Some polymorphisms, such as altered Toll-like receptor 4 (TLR4), which is involved in the activation of immune and inflammation responses, seem to be associated with an increased risk of developing the disease [6]. Furthermore, environmental pollutants and endocrine disruptors may play a role by interfering with the endocrine and immune systems and with ROS generation [9,10]. Therefore, due to the involvement of several mechanisms, the treatment of endometriosis is still challenging. Although there is no definitive cure for endometriosis, hormonal treatments are generally prescribed, and have been shown to be effective on both the disease and painful symptoms. However, these therapies may carry side effects and are not indicated in women with a current desire for pregnancy. Patients with endometriosis require a personalized therapy which considers their needs and the overall clinical picture, including the burden of painful symptoms, the extent of the pelvic anatomy impairment and short- or long-term reproductive desire. In patients seeking pregnancy, the use of NAC can be proposed. In fact, NAC proved to be effective and safe, both in vitro and in vivo, in reducing endometriotic lesions and endometriosis-related pain, and it can be safely used in pregnancy, due to the near absence of reported side effects. The decision to administer NAC quarterly, with a total of 9 doses/week, lies in its pharmacokinetic properties, as a reduction in the absorption and blood concentration was reported after prolonged daily treatments with both high and low doses. Furthermore, considering the plasma NAC half-life (less than 3 h), the fractionation into 3 doses of 600 mg each ensured constant plasma levels, as reported in previous studies [21,22]. The purpose of our study was to demonstrate the benefits of NAC in patients with endometriosis. As previously reported in a pilot study that we conducted a few years ago [20], we found a significant improvement in endometriosis-associated pain after 3 months of treatment. In particular, lower mean VAS scores of dysmenorrhea, dyspareunia and CPP were observed, leading to a significant decrease in analgesics intake. The effect on pain is probably due to the strong antioxidant effect of NAC. Oxidative stress and inflammation play a key role in the pathogenesis of endometriosis-related pain. The release of cysteine, a precursor of reduced glutathione, from NAC, allows the molecule to perform an indirect antioxidant action, with the removal of ROS leading to the inhibition of proinflammatory cytokines (IL-6, IL-8, TNF-alpha), VEGF, and metalloproteinases, whose concentration is increased in the peritoneum of patients with endometriosis [15,22]. These results are in line with data reported in a recent review by Mohiuddin et al., which explains the effects of NAC on chronic pain in adults [19]. Moreover, NAC seems to reduce ferroptosis, a mechanism recently demonstrated in endometriotic cells [23]. In the case of retrograde menstruation, the blood present in the peritoneum undergoes erythrocyte degradation. This results in the release of free iron, increased transferrin saturation and the potential intracellular accumulation of ferritin. Iron excess can activate Fenton’s reaction, with a further release of ROS [24]. Our study showed a significant reduction of the size of ovarian endometriomas after three months of treatment. This effect seems to be related to the strong antiproliferative effect of NAC, which causes morphological, biochemical, and molecular changes that lead to a shift from proliferation to cell differentiation. One of the mechanisms involved is related to the inhibition of the tyrosine-kinase c-Src, whose action regulates and influences the adhesive migratory capacities of the cell. It was observed that NAC treatment may induce colon and ovary cancer cells to show an increase in adhesion complexes, linked to the decreased activity of the c-Src, thus stimulating cell differentiation with reduction of proliferation [25]. A study conducted in 2002 reported how NAC may play a role in cell differentiation, demonstrated by the delocalization of E-cadherin from the cytoplasm to the cellular membrane. In fact, patients treated with NAC showed an increased concentration of E-cadherin at the cell-to-cell lines, with cell differentiation induction [26]. Our study also showed that, after the administration of NAC, there was a significant reduction in the serum levels of Ca125, a marker which increases in several conditions of peritoneal inflammation. Ca125 is a component of the glycoprotein in the epithelia of celomatic origin, such as the endometrium, the endocervix, the fallopian tubes, the pleura, the pericardium, and the peritoneum. It was also found in the pancreas, the colon, the gallbladder, the stomach, the lungs, and the kidneys [27,28]. This glycoprotein is often used as a marker for ovarian cancer, but it is also altered in endometriosis. The increase in this marker concentration may be due to the proliferation of endometrial cells. Since the serum concentration of Ca125 is elevated in multiple conditions, its role in the diagnosis of endometriosis is significantly reduced, in consideration of its low sensitivity and specificity. Nevertheless, for research purposes, we decided to measure the serum marker concentration in patients affected by endometriosis, and we found a significant reduction after treatment with NAC, probably due to the anti-inflammatory action of the molecule at the peritoneal level. The threshold value used was 35 U/mL. To avoid any bias resulting from the monthly fluctuations of the marker, the blood samples were drawn on the 8th day of the menstrual cycle from all patients. Of the fifty-two patients with reproductive desire, thirty-nine successfully achieved spontaneous pregnancy within 6 months from the beginning of therapy, while six achieved pregnancies after ART. The efficacy on fertility outcomes could be related to many factors. Firstly, its undisputed antioxidant efficacy, supported by the improvement of oocyte quality due to the reduced expression of COX-2 [29]. In fact, it was reported that a pro-inflammatory and ROS-dominated environment mediates changes in gametic DNA and, through the stimulation of apoptosis, it promotes embryo fragmentation, implantation failure and abnormalities of the placentation process, significantly increasing the rate of miscarriages and recurrent pregnancy losses [30,31]. Moreover, the mucolytic and fluidifying effect can improve both the quality of cervical mucus, facilitating the ascent of spermatozoa through the female genital tracts, and the tubal function in patient fallopian tubes [32], detectable by using the sonohysterosalpingography [33]. Recently, a study conducted by Fan et al. demonstrated how NAC, in vitro, reduces mitochondrial damage in oocytes [34]. Finally, the reduction of painful symptoms allowed an increased rate of sexual intercourses, with a higher probability of conception. Although it was not an objective of our study, we observed a significant reduction of the patients’ BMI. However, it should be noted that $86.6\%$ of the patients used hormonal therapy before recruitment. This anamnestic data is fundamental, as it is known that treatment with progestins or estroprogestins may result in weight gain [35]. Despite this, it is possible to assume a beneficial effect of NAC on body weight due to its action on glucose metabolism. Several clinical and experimental studies highlighted the potential effects of NAC as a therapeutic agent for the treatment of insulin-resistant and type-2 diabetes mellitus [36]. A study conducted on healthy men, showed how intravenous perfusion of NAC during peak glycemic improves insulin sensitivity and increases peripheral glucose uptake. Moreover, NAC reduces the oxidation of lipoproteins, improving the absorption of circulating lipids [36]. Our study showed that NAC is a safe, inexpensive and valuable short-term alternative for the treatment of endometriosis-associated pain in patients who cannot or refuse to take hormonal therapy or with the desire for pregnancy, as the current hormonal therapy does not allow for conception. Further studies on a larger number of patients are necessary to confirm our data. ## 5. Conclusions N-acetylcysteine is effective in reducing endometriosis-related pain symptoms, the size of endometriomas and the serum levels of Ca125. 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--- title: Missense Variants of von Willebrand Factor in the Background of COVID-19 Associated Coagulopathy authors: - Zsuzsanna Elek - Eszter Losoncz - Katalin Maricza - Zoltán Fülep - Zsófia Bánlaki - Réka Kovács-Nagy - Gergely Keszler - Zsolt Rónai journal: Genes year: 2023 pmcid: PMC10048626 doi: 10.3390/genes14030617 license: CC BY 4.0 --- # Missense Variants of von Willebrand Factor in the Background of COVID-19 Associated Coagulopathy ## Abstract COVID-19 associated coagulopathy (CAC), characterized by endothelial dysfunction and hypercoagulability, evokes pulmonary immunothrombosis in advanced COVID-19 cases. Elevated von Willebrand factor (vWF) levels and reduced activities of the ADAMTS13 protease are common in CAC. Here, we aimed to determine whether common genetic variants of these proteins might be associated with COVID-19 severity and hemostatic parameters. A set of single nucleotide polymorphisms (SNPs) in the vWF (rs216311, rs216321, rs1063856, rs1800378, rs1800383) and ADAMTS13 genes (rs2301612, rs28729234, rs34024143) were genotyped in 72 COVID-19 patients. Cross-sectional cohort analysis revealed no association of any polymorphism with disease severity. On the other hand, analysis of variance (ANOVA) uncovered associations with the following clinical parameters: [1] the rs216311 T allele with enhanced INR (international normalized ratio); [2] the rs1800383 C allele with elevated fibrinogen levels; and [3] the rs1063856 C allele with increased red blood cell count, hemoglobin, and creatinine levels. No association could be observed between the phenotypic data and the polymorphisms in the ADAMTS13 gene. Importantly, in silico protein conformational analysis predicted that these missense variants would display global conformational alterations, which might affect the stability and plasma levels of vWF. Our results imply that missense vWF variants might modulate the thrombotic risk in COVID-19. ## 1. Introduction The devastating COVID-19 pandemic caused by virulent strains of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has overloaded healthcare systems worldwide with unprecedented morbidity, hospitalization, and mortality rates [1]. Critically ill patients were diagnosed with fulminant pneumonia presenting with massive inflammatory infiltrates that culminated in acute respiratory distress syndrome, requiring mechanical ventilation. Airspace filling and collapse due to inflammatory exudates and impaired oxygen diffusion on account of increased alveolar capillary permeability with consequent thickening of the alveolar wall resulted in intrapulmonary shunting, ventilation-perfusion mismatch, and severe hypoxemia, which is further aggravated by hypoperfusion of the lungs due to pulmonary thromboembolism [2]. The incidence of COVID-19 associated thrombosis was found to be as high as $31\%$ among patients in intensive care units, the majority of which was venous thromboembolism [3]. Despite pre-hospital antiplatelet medication, thromboprophylaxis, and routine thrombolysis, autopsies revealed that $71.4\%$ of patients having succumbed to the disease had thrombotic events with elevated D-dimer levels and prolonged prothrombin time [4]. The pathogenesis of SARS-CoV-2 infection associated pulmonary intravascular coagulopathy (PIC), also termed COVID-19 associated coagulopathy (CAC), has been characterized in detail [5,6]. COVID-19 leads to venous thromboembolism by establishing a generalized hypercoagulable state, with elevated plasma levels of key clotting factors including fibrinogen, factors V and VIII, as well as von Willebrand factor (vWF), which is a multidomain plasma protein essential for both platelet adhesion and aggregation [7]. One of the most sensitive molecular markers of CAC is a consistently elevated D-dimer concentration [8]. On the other hand, inflammatory cytokines cause local endothelial dysfunction in the lungs, resulting in thrombotic microangiopathy (immunothrombosis) [9]. A substantial prognostic factor of this condition is the elevation of the plasma vWF protein level, in combination with decreased ADAMTS13 enzyme activity [10]. ADAMTS13 is a liver-derived matrix metalloprotease known to attenuate the prothrombotic properties of vWF by cleaving it into two fragments [11]. In SARS-CoV-2 infection, pro-inflammatory cytokines, in particular IL-8 and TNF released from type 2 pneumocytes and alveolar macrophages, stimulate secretion of thrombogenic, unusually large vWF multimers (UL-vWFM) from the Weibel–Palade bodies of endothelial cells. On the other hand, several mechanisms have been reported to inhibit the production and/or to reduce the enzyme activity of ADAMTS13, including the overproduction of IL-6, IL-8, and TNF cytokines [12], the generation of anti-ADAMTS13 neutralizing antibodies [13], and the sequestration of vWF via binding to platelet factor 4 on the surface of activated thrombocytes [14]. UL-vWFM can bind to heparan sulfate in the glycocalyx of endothelial cells; this interaction activates the alternative complement pathway [15], which in turn, promotes the formation of tissue-factor rich neutrophil extracellular traps [16], mediators of pneumonia-associated pulmonary microvascular thrombosis [17]. Sustained endothelial dysfunction seems to be a major cytopathological determinant of long-term COVID-19 symptoms as well [18]. Genetic variations of the vWF and ADAMTS13 genes were demonstrated to play a role in numerous pathological conditions related to thrombosis and hypercoagulability. Association analyses of complex traits usually aim to analyze common polymorphisms, although mutations and rare variants might also contribute to the genetic risk of these phenotypes [19,20]. Another element is the investigation of genetic variants with putative molecular effects. Missense polymorphisms modify the primary structure of the proteins and can result in altered function; on the other hand, SNPs, in either coding or non-coding sequences, can also have an impact on gene expression at numerous levels [21]. Secretion of vWF significantly increases in cases of impaired endothelium function (e.g., the damages of the endothelium caused by hypoxia, inflammation, or shear stress and high blood pressure). Taking this into account, the role of vWF develops in connection with thrombosis resulting from significant endothelial damage [22]. Polymorphisms of vWF might modulate the function of the protein, and these genetic variations were suggested to contribute to the risk of venous thrombosis. The rs1063856 SNP results in an amino acid change (p.Thr789Ala) in the D’ domain of vWF, playing a role in the interaction with FVIII. The modified vWF protein causes alterations in the function of FVIII [23], as well as increases the half-life of the protein [24,25], resulting in significantly increased plasma vWF-levels [26]. The rs216321 missense SNP modifies the primary structure of the D’D3 domain of vWF (p.Gln852Arg). This subunit also influences the FVIII level, as well as alters the interaction between the A1 domain and platelet Gp1b. The results are, however, controversial. The Gln coding allele was shown to cause decreased vWF levels and collagen induced platelet activation; on the other hand, the FVIII levels were increased. One explanation might be the alteration of the acid–base characteristics of the molecule, and thus, a local conformational change in the polypeptide chain, which might modify the binding site of FVIII [27]. The rs1800378 polymorphism was demonstrated to associate with pulmonary thromboembolism disease. The SNP results in an amino acid change in the D2 domain of vWF (p.His484Arg) [28], and interestingly, might cause a spurious increase in the plasma vWF level by modifying the antibody binding affinity of the protein [25], a phenomenon which might be of immunological significance. Two further missense polymorphisms (rs1800383 and rs216311) in the A1 interaction domain of vWF seem to have a synergistic effect. Interestingly, these SNPs have been shown to be connected with increased thrombosis risk, as well as with hemorrhagic events [29]. It was demonstrated that both SNPs can affect platelet aggregation by vWF with its cofactor ristocetin [30]; moreover, the rs216311 locus influenced interaction with FVIII [31]. The apparent contradictory effects might be resolved by the putative contribution of these SNPs to microangiopathy, which can be related to both systemic clinical conditions (i.e., thrombosis and hemorrhage). The rs2301612 SNP (p.Gln448Glu) in the cysteine-rich domain of ADAMTS13 plays an important role in substrate recognition and cleavage. Besides, the polymorphism is of significant interest because it is one of the most important targets of the autoimmune response in thrombotic thrombocytopenic purpura [32]. It was shown that this genetic variant may decrease the activity of the enzyme, resulting in increased vWF activity [33,34]. In addition, this polymorphism is associated with the risk of multivessel disease in a patient group with type 2 diabetes, raising the possibility that the locus might contribute to microthrombotic events in COVID-19 infection as well. The consequence of the rs34024143 SNP is an amino acid change in the signal peptide of the ADAMTS protein (p.Arg7Trp). This polymorphism might have an effect on the folding and stability of the mature protein, and signal sequence polymorphisms can influence the interaction with phospholipids and the components of the secretory pathway. It was earlier demonstrated that the incidence of pulmonary thrombosis is significantly increased (but well below $100\%$) in critically ill COVID-19 patients [35]. Thus, it can be assumed that genetic factors might play a role in the development of the enhanced thrombotic risk. In light of the pivotal role of vWF and its processing peptidase ADAMTS13 in the development of COVID-19 associated coagulopathy, we aimed to explore whether certain missense polymorphisms of these genes can modulate (and predict) the severity and progression of COVID-19 by attenuating ADAMTS13 activities or stabilizing vWF. ## 2.1. Participants A total of 72 study participants were recruited from in-patients testing positive for SARS-CoV-2 infection and treated at the Department of Emergency and/or Department of Anesthesiology and Intensive Care Unit of the Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary. The diagnosis of SARS-CoV-2 infection was established using the standard RT-qPCR amplification protocol from nasopharyngeal swabs. COVID-19 pneumonia was confirmed by physical, laboratory, imaging, and microbiological examinations. Subjects with malignant, autoimmune, metabolic, or inflammatory comorbidities, as well as those receiving immunosuppressive therapy, were excluded from the analysis. The patients ($$n = 72$$) were grouped into severity cohorts, according to WHO criteria. All participants were of Caucasian origin. ## 2.2. DNA Sampling and Purification Sample collection was carried out using Tempus™ Blood RNA Tubes obtained from ThermoFisher. Samples were stored at room temperature in the hospital for a maximum of 5 days and subsequently transported to the Molecular Genetic Laboratory (Department of Molecular Biology, Semmelweis University) and were kept at −20 °C until further processing. DNA isolation was initiated by adding 450 µL cell proteinase K buffer (0.1 M NaCl, 0.01 M Tris-HCl pH = 8, $0.5\%$ SDS, 0.2 mg/mL proteinase K) to 800 µL of the sample, followed by incubation at 56 °C overnight. Proteins were then precipitated using saturated NaCl and removed by centrifugation. DNA was isolated from the supernatant using the standard ethanol/isopropanol precipitation method. Precipitated DNA was redissolved in 0.5× TE (0.005 M Tris-HCl, pH = 8 and 0.5 mM EDTA). DNA concentrations were measured with a Nanodrop1000 spectrophotometer. The average DNA concentration of the samples was 140 ng/µL (20–572 ng/µL). ## 2.3. In Silico Tools for SNP Selection and Genotype Analysis Genomic sequences of the ADAMTS13 and vWF genes, as well as data about the SNPs, were obtained from NCBI Genebank (http://www.ncbi.nlm.nih.gov/gene, accessed on: 19 July 2022) and Ensembl (http://www.ensembl.org/index.html, accessed on: 19 July 2022). Allele frequency data were obtained from the gnomAD database (https://gnomad.broadinstitute.org/, accessed on: 19 July 2022). To focus on the analysis of SNPs with putative functional effects, missense SNPs were predominantly selected for further investigation. Restriction endonucleases for PCR–RFLP genotyping were selected with help of the NEBcutter v2.0 tool (http://nc2.neb.com/NEBcutter2/, accessed on: 26 July 2022). Primers were designed using the NCBI Primer-Blast software (https://www.ncbi.nlm.nih.gov/tools/primer-blast/, accessed on: 26 July 2022) and checked with the Oligonucleotide Properties Calculator (http://biotools.nubic.northwestern.edu/OligoCalc.html, accessed on: 26 July 2022). ## 2.4.1. Genotype Analysis with qPCR TaqMan Probes Predesigned SNP genotyping assays, containing 2 primers to amplify the adjacent region of the polymorphisms and 2 allele-specific probes labeled with FAM and VIC fluorescent dyes, respectively, were obtained from ThermoFisher (Waltham, MA, USA). Assay IDs were as follows: SNPs in ADAMTS13: rs2301612: C__11571465_1_, rs34024143: C__3183339_10; SNPs in vWF: rs1800378: C__8921130_20, rs1800383: C__11915632_10, rs216311: C_175678231_10, and rs1063856: C__3288406_30. The reaction mixtures contained 1× PCR TaqMan ProAmp mastex mix (AmpliTaq Gold® DNA polymerase, dNTPs, ROX dye, MgCl2 and buffer), 1× TaqMan probe, and 4 ng genomic DNA in a final volume of 6 µL. Thermocycles were initiated with a 95 °C—10 min step to activate the hot-start AmpliTaq Gold® DNA polymerase, followed by 40 cycles of 95 °C—15 s denaturation and 60 °C—1 min combined annealing and extension. A reporter signal was detected during this latter step, and an endpoint allelic discrimination analysis was also performed after PCR amplification to classify the samples into three clusters, according to their genotype. ## 2.4.2. PCR–RFLP Three SNPs (ADAMTS13: rs2301612, rs28729234, and vWF: rs216321) were analyzed by PCR–RFLP. To boost the reliability of the genotyping, primers were designed to include a non-polymorphic, control digestion site into the PCR amplicon in the case of each polymorphism. Table 1 summarizes the technical parameters of the PCR–RFLP methods. PCR amplification was carried out using 0.25 U HotStarTaq DNA polymerase (Qiagen, Hilden, Germany) dissolved in 1× buffer and 1× solution Q. The reaction mixtures contained 0.2 mM of each deoxyribonucleoside triphosphate (dATP, dCTP, dGTP, and dTTP), 1 µM sense and antisense primers, and 4 ng genomic DNA in a total volume of 10 µL. Thermocycles were initiated by denaturation and polymerase activation at 95 °C for 15 min, followed by 40 cycles of denaturation at 94 °C for 30 s, annealing at 60 °C for 30 s, and extension at 72 °C for 1 min, terminated by a final extension step at 72 °C for 10 min. RFLP analysis was carried out by adding 6 µL of a restriction endonuclease mixture containing 0.2 U restriction endonuclease in 1× CutSmart Buffer (New England Biolabs, Ipswich, MA, USA) to the PCR-amplicons, and samples were incubated at 37 °C for 3 h. The digestion products were resolved by conventional slab gel electrophoresis ($2.5\%$ agarose, 13 V/cm electric field). After staining in 0.5 µg/mL ethidium bromide solution for 10 min, the migration patterns were imaged using a GelDoc 1000 gel documentation system (BioRad, Hercules, CA, USA). ## 2.5. Prediction of 3D Protein Structure The I-Tasser software (https://zhanggroup.org/I-TASSER/about.html, accessed on: 22 August 2022) was used to predict the effect of missense SNPs on the corresponding protein conformation. The 3D structure of each haplotype was plotted using the Swiss-PdbViewer 4.1.0 (https://spdbv.unil.ch, 22 August 2022). ## 2.6. Statistical Analysis The Hardy–Weinberg equilibrium (HWE) for genotype distributions was analyzed with the χ2-test. Association analyses were carried out by comparing the genotype frequencies of each polymorphism in all patient cohorts using SPSS v17.0 and HaploView v4.2 [36]. The Bonferroni correction for multiple testing was used to rule out false positive results in the cohort analysis. As 8 SNPs were investigated, the modified level of statistical significance was $p \leq 0.00625.$ Linkage disequilibrium and haplotype analyses were performed by HaploView v4.2, and correction for multiple testing was conducted by permutation analysis (100,000 random permutations). Genotype data of a healthy, Caucasian population for linkage disequilibrium testing were obtained from the 1000 Genomes Browser (https://www.internationalgenome.org/1000-genomes-browsers/index.html, accessed on: 14 August 2022). The analysis of variance (ANOVA) procedure was used to seek association between clinical parameters as continuous variables and genotype data (version 3.05). ## 3.1. Clinical Characterization of Patient Cohorts Clinical progression scores were assigned to all participants according to the clinical progressions scores of WHO [37]. Four cohorts were created, comprising patients with scores of 4, 5, 6–7, and 8–9, respectively, presenting with increasingly severe COVID-19 symptoms (Table 2). The subjects were predominantly males, with a mean age around 50 years in each cohort. Red blood cell counts and hemoglobin levels did not differ significantly across the cohorts, but creatinine concentrations were slightly elevated in cohorts 5 to 8–9. The duration of hospitalization and ICU treatment, the number of infiltrated lobes (lobar involvement), respiratory rates, and Horowitz coefficients (a measure of alveolar gas transfer efficiency) were proportional to the severity of the disease. Importantly, plasma levels of interleukin-6, a pleiotropic pro-inflammatory cytokine known to induce synthesis of acute phase proteins in hepatocytes [38], correlated well with those of its target genes CRP and fibrinogen, with dramatic upregulation in cohort 5 and beyond, although a marked drop was also seen comparing 6–7 to 8–9. Plasma levels of D-dimer, a widely used thrombosis marker in COVID-19 [39], increased parallel with the severity scores, despite essentially unchanged INR values. ## 3.2. SNP Genotyping As both vWF and ADAMTS13 have been shown to play a crucial role in the development of thromboembolism in COVID-19, we aimed to explore whether their missense variants might be correlated with progression scores of the disease by directly modulating the prothrombotic activity of these proteins. To test this hypothesis, a set of SNPs were genotyped in both genes, and association analyses were conducted to uncover any statistically significant enrichment of a certain allele in patient cohorts. The selection criteria of missense SNPs were as follows. We aimed to investigate genetic variants with MAF > $5\%$ in the Caucasian population. We preferred polymorphisms with putative biological effect, such as missense SNPs, where the two-allele codes for amino acids greatly different in terms of charge, size, or hydropathy index [40]. We checked the availability of literary data in connection with the functional role of the SNPs in terms of modulating thrombotic risk or protein plasma levels, etc. According to these criteria, five SNPs from the vWF and three from the ADAMTS13 genes were selected for analysis (Table 3). Two missense SNPs in the vWF gene (rs1063856 and rs216321) caused amino acid changes in the D’ multimerization domain of the protein. These polymorphisms were shown to be associated with plasma vWF:Ag levels [26]. On the other hand, the TT variant of the rs1800378 SNP was positively correlated with the incidence of pulmonary thromboembolism [28]. The rs216311 SNP was found to affect vWF:Ag plasma levels in a blood group dependent manner [41] and, along with rs1800383, was linked to bleeding history in a Nigerian population [29]. Both SNPs alter the primary sequence of the A1 domain that is known to interact with both the platelet integrin glycoprotein Ib and subendothelial collagen. The rs2301612 SNP in the ADAMTS13 gene is associated with cerebral aneurysms, possibly via altered arterial wall remodeling [42]. However, no functional information was available on the rs34024143 missense and the intronic rs28729234 polymorphisms. SNPs were genotyped either by real-time PCR or PCR-RFLP, as described in Section 2.3. Genotyping of the rs2301612 SNP was performed, with both methods producing identical results, proving the reliability of our genotyping protocols. Genotype frequencies are presented in Table 4. Hardy-Weinberg equilibria were fulfilled for each SNP as no significant difference ($p \leq 0.1$) could be observed between the measured and expected genotype frequencies for any of the investigated SNPs. In case of two ADAMTS13 SNPs, however, minor allele frequencies (MAFs) fell slightly outside the range of 5–$40\%$ declared as inclusion criterion ($4\%$ for rs28729234 and $44\%$ for rs2301612). This deviation from the gnomAD genotype data might be attributed to the low sample numbers in our study. Furthermore, no minor allele homozygotes were found in the case of rs1800383 and rs28729234, likely for the same reason. ## 3.3. Linkage Disequilibrium and Haplotype Analysis Figure 1A represents Lewontin’s D’ and R2 values from the pairwise linkage disequilibrium (LD) analysis in our patient population. These results are in good agreement with those obtained from the LD analysis using the dataset of the 1000 Genomes Project (Figure 1B). Generally high levels of LD can be observed in the ADAMTS13 gene region. It is notable that the rs1800378 polymorphism in vWF was not in linkage disequilibrium with the other sites, in agreement with the fact that it is relatively far from the other SNPs on the chromosome. On the other hand, high D’ values, in combination with low R2 values in the context of the rs1800383 polymorphism, disclose high-level linkage disequilibrium of this locus with other SNPs. The unusually high ($100\%$ and $90\%$) D’ scores between this and the rs216311 and rs216321 SNPs might imply the existence of a haplotype block encompassing these relatively close SNPs. ## 3.4. Association Analyses In an attempt to determine whether genetic polymorphisms in the vWF and ADAMTS13 genes are associated with the progressive severity of COVID-19, χ2-tests were performed by comparing the genotype frequencies of each polymorphism in all patient cohorts. As it shown by the data presented in Table 5, no correlation could be detected between the SNPs and the clinical progression scores (i.e., p ≥ 0.15 for all polymorphisms, higher than that of both the nominal ($p \leq 0.05$), as well as the corrected level ($p \leq 0.00625$) of statistical significance) in this population. As no complete linkage disequilibrium was observed between the investigated loci, haplotypes were also constructed, and their frequencies (Supplementary Table S1) were compared in the four cohorts, nor could statistically significant differences be found by employing this approach. However, considering clinical parameters as continuous variances across all cohorts and implementing the ANOVA approach, it turned out that the rs1800383 and rs216311 SNPs affecting the A1 domain of vWF were significantly associated with the hemostatic parameters. Table 6 summarizes data with significant associations; further results of the ANOVA analyses related to each clinical parameter are shown in Supplementary Table S2. Patients carrying the rs1800383 CC homozygote genotype showed average fibrinogen levels of 6.64 g/L. In contrast, mean fibrinogen levels in the CG heterozygotes were 17.62 g/L ($$p \leq 0.032$$). On the other hand, the T allele of the rs216311 SNP seems to prolong the tissue factor dependent clotting time, as indicated by greater international normalized ratios (INR) ($$p \leq 0.039$$). Though seemingly less relevant in terms of pulmonary thromboembolism, the minor TT genotype of the rs1063856 SNP was found to be associated with significantly lower red blood cell counts, as well as lower hemoglobin and creatinine levels, in our patients with nominal p values of 0.049, 0.029, and 0.008, respectively (Table 6). The rest of the SNPs in the vWF and ADAMTS13 genes did not show association with any clinical parameter. ## 3.5. Predicted Structural Changes in Missense Variants In light of the above associations, we aimed to explore whether the missense variants of vWF and ADAMTS13 have any effect on protein conformation, using the ResQ B factor profiling tool [43]. This method predicts the effects of amino acid exchanges on the local secondary structure, solvent accessibility, residue-level accuracy, and thermal mobility (B-factor), while the I-Tasser software was employed to build global conformational models of the polypeptide. Results of in silico conformational analyses are presented in Table 7. Of the 7 allelic variants analyzed, only the rs2301612 G allele triggered a local conformational change, the disruption of an α helical structure by replacing glutamine with glutamate in the ADAMTS13 protein. The T allele of rs216321, resulting in an arginine-to-glutamine exchange, substantially altered solvent accessibility, as glutamine is no longer exposed to the surface of the protein (‘E’ to ‘B’ transition). The only minor variant that confers substantial stabilization on the corresponding protein is the G allele of the rs1800383, encoding an aspartate residue with a negative predicted normalized B factor (BFP) of −0.11. In comparison, the major allele (C) incorporates histidine in the same position, which is slightly less stable (BFP = 0.04). Importantly, the residue-specific quality (RSQ) scores corresponding to the estimated deviation of the residue from the native structure of the protein exhibit the greatest difference in the context of this polymorphism, when compared to the others (4.83 vs. 6.94, a more than $40\%$ increase from the more frequent His to the rare Asp-containing version). Based on these data, it seemed reasonable to assume that the His-to-Asp change might induce not only a local, but even a more extensive structural change in the vWF. To validate this notion, structural models of both proteins with just single amino acid exchanges were generated, as displayed in Figure 2. Interestingly, not only the Asp1472, but also the Thr1381 and Ala789, containing variants of vWF, exhibited dramatic predicted conformational changes at the global level. The Asp1472 protein seems to be less compact, with several disrupted secondary structures, while a hallmark of Thr1381 and Ala789 versions is a large unstructured domain, either jutting out from the central body of the protein (Thr1381), or connecting its N- and C-terminal regions (Ala789). It is remarkable to note that substitution of Arg484 with His and Arg852 with Gln does not lead to conformational changes as extensive as those seen in the case of any of the three other isoforms. In summary, it is remarkable that three minor isoforms of vWF, which are associated with elevated Fg levels and INR values, as well as increased RBC counts, plasma Hb, and creatinine concentrations, have steric structures quite different from that of the most frequently occurring haplotype. Although not showing any association with clinical parameters, structural models of the ADAMTS13 variants were also created. Surprisingly, they display more stretched conformations which are much different from the rather compact structure of the most frequently seen haplotype (Figure 2, upper panel). ## 4. Discussion Over the past two and one-half years, a plethora of scientific information has been published on the pathomechanism of COVID-19. Despite being primarily a respiratory infection, an extraordinarily high incidence of thrombotic events has been observed in severe cases, leading investigators to define a new entity termed, ’COVID-19 associated coagulopathy’ (CAC). CAC is derived from an inflammation-related imbalance of elevated plasma vWF levels and decreased ADAMTS13 activities, presumably mediated by inflammatory cytokines and complement factors. Importantly, clarification of the pathogenesis incited clinical trials with targeted therapeutic modalities, including administration of caplacizumab, an anti-vWF monoclonal antibody [44], activated complement C5a inhibitors [45], recombinant ADAMTS13 [46], and IL1 és IL6 antibodies [47]. Just as is the case with all infectious diseases, the clinical course of SARS-CoV-2 infections is determined not only by virulence factors of the invading pathogen, but also by genetic factors of the host, which govern innate and adaptive immune responses [48]. To obtain an insight into individual differences in the defense mechanisms of the host, high-throughput, genome-wide association studies were conducted to map COVID-19-specific susceptibility loci in the human genome [49,50]. While hemostatic abnormalities are proven to contribute to the mortality of COVID-19 to a large extent, genetic polymorphisms modulating the thrombotic risk of this disease have scarcely been investigated. Recently, a systems biology approach was described by Abu-Farha et al. [ 51] to identify high-risk patients, and a phenome-wide association study was performed to uncover the genetic susceptibility for COVID-19-associated thrombosis [52]. Lapic et al. [ 53] analyzed the role of various missense variants of coagulation factors II, V, and XIII, as well as further proteins supposed to modulate thrombotic risk in COVID-19, including platelet integrin receptors, the endothelial nitric oxide synthase, and methylene tetrahydrofolate reductase. However, to the best of our knowledge, ours is the first study assessing the potential contribution of a set of single nucleotide polymorphisms in the vWF and ADAMTS13 genes, key factors in the background of CAC, to increased thrombotic risk among COVID-19 patients. In the present study, COVID-19 patients were stratified in severity cohorts using internationally acknowledged progression scores [37], and association analyses were performed between the above mentioned SNPs and disease progression, but no statistically significant correlations were found in this context. Either our study was underpowered due to low sample size, or single missense effects are not powerful enough to significantly modify the progression of the disease. Therefore, we proceeded to seek more specific associations between the same SNPs and laboratory findings of patients in general, and hemostatic parameters, in particular. Implementing this approach, three missense SNPs in the vWF gene were found to associate with fibrinogen levels, clotting time (INR), and red blood cell, hemoglobin and creatinine levels in COVID-19 patients, but we failed to find any correlations with ADAMTS13 SNPs. As far as ADAMTS13 is concerned, surprisingly little is known about the association of its genetic variants with any disease. To date, only two studies have been published in this context, the one concluding that the A allele of the intronic rs4962153 SNP confers protection from cerebral malaria [54]. The other investigation found that the rs2301612 SNP, known to be a key determinant of plasma ADAMTS13 levels [33], is associated with cerebral aneurysms [42] due to abnormal arterial wall remodeling. The latter polymorphism was included in our study as well, but neither this nor the two other SNPs were found to be associated with disease severity or laboratory parameters in COVID-19 patients (Table 5 and Table 6). However, our in silico structure analysis revealed that tertiary structures of missense Gln448Glu variants are markedly different (Figure 2), an interesting finding that might underlie differences in plasma levels, rate of secretion, and specific enzyme activities between the Gln448 and Glu448 containing common ADAMTS13 variants [33]. Importantly, the common variant containing Trp in position 7 is predicted to display conformational alterations strikingly similar to those elicited by a Gln448Glu exchange (Figure 2). This observation might also prompt in vitro studies exploring the stability and activity of this common variant. The literature on the genetic associations of missense variants of vWF with hemostatic abnormalities is far more extensive. Importantly, every single SNP we found to be associated with hematological parameters (rs1800383, rs216311, and 1063856, but not the other two) has been shown to modulate the plasma levels of the vWF protein [26,41]. Structural modeling revealed that only these missense variants (but not the rest) entail considerable conformational changes which might also influence the stability and thereby, the plasma levels, of the protein (Figure 2). Both vWF and fibrinogen serve as molecular adhesives during the formation of platelet-rich thrombi, and both proteins are substrates of plasmin, a fibrinolytic protease. It has been shown that the rate of fibrinogen degradation by plasmin is significantly slower in the presence of vWF [55]. This correlation might be implicated in the differential regulation of fibrinogen levels by missense variants of vWF, resulting in higher Fg levels in rs1800383 CG heterozygotes in our study. One can assume that Fg levels would be even higher in individuals harboring the GG genotype, but such a genotype did not occur in our patient cohorts, unfortunately. Cleavage of vWF multimers by plasmin under shear stress has recently been shown to compromise platelet adhesion to collagen [56]. This mechanism might offer a plausible explanation for the prolongation of tissue thromboplastin-induced clotting time (prothrombin time) and consequently elevated INR in rs216311 TT homozygotes. Namely, the Thr1381 containing the vWF variant might be a better substrate of plasmin, resulting in lower circulating vWF levels, impaired platelet adhesion, aggregation, and secondary hemostasis. Finally, a relatively old observation might provide a plausible clue for the seemingly elusive association of the minor C allele of the rs1063856 polymorphism, with slightly elevated red blood cell counts and hemoglobin levels. Wick et al. reported that unusually large von Willebrand factor multimers help sequester young healthy erythrocytes on endothelial cells [57]. It is tempting to speculate that the Ala789-containing variant is less potent in doing so, leaving more red blood cells suspended in the circulation. On the other hand, higher creatinine levels measured in the presence of the Ala789 variant might simply reflect impaired renal function due to thrombotic hypoperfusion of the kidneys. The major limitation of the cohort analysis portion of the presented work is the rather low sample size. Thus, it can be considered as a pilot study only; however, the predicted effects of missense variants of vWF on protein conformation and biological activity deserve further research. The putative, weak associations observed here should be verified in further studies investigating cohorts including a much higher number of participants. Another limitation is the lack of ADAMTS13 and vWF level measurements and functional characterization of the proteins possessing different allelic and haplotype variants. These analyses could provide further insights in the biological role of the investigated SNPs, both at molecular and clinical levels. In conclusion, the common SNPs investigated in our study were predicted to result in substantial conformational changes in the vWF protein, potentially modifying its biological activity. Our small cohort analysis did not reveal an association between the selected polymorphisms and the severity of COVID-19 infection; on the other hand, we found a connection between three missense variants of the vWF gene and some clinical variables (INR, fibrinogen, creatinine, hemoglobin levels, and red blood cell counts). Importantly, these findings can be considered as pilot results only, and further association studies, as well as functional characterization of the polymorphisms, are required to shed light on the exact role of these missense SNPs. ## References 1. Long B., Carius B.M., Chavez S., Liang S.Y., Brady W.J., Koyfman A., Gottlieb M.. 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--- title: Effects of Three Extraction Methods on Avocado Oil Lipid Compounds Analyzed via UPLC-TOF-MS/MS with OPLS-DA authors: - Yijun Liu - Qiuyu Xia - Yangyang Qian - Yu Kuang - Jiameng Liu - Lijing Lin journal: Foods year: 2023 pmcid: PMC10048627 doi: 10.3390/foods12061174 license: CC BY 4.0 --- # Effects of Three Extraction Methods on Avocado Oil Lipid Compounds Analyzed via UPLC-TOF-MS/MS with OPLS-DA ## Abstract Avocado oil is excellent functional oil. Effects of three extraction methods (squeezing extraction, supercritical carbon dioxide extraction, and aqueous extraction) on the species, composition, and contents of lipids in avocado oil were analyzed via ultra-performance liquid chromatography–time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS), and the differential components of lipids were revealed by OrthogonalPartialLeast Squares-DiscriminantAnalysis (OPLS-DA), S-plot combined with variable importance in the projection (VIP). The results showed that the fatty acid composition of avocado oil mainly consisted of oleic acid (36–$42\%$), palmitic acid (25–$26\%$), linoleic acid (14–$18\%$), and palmitoleic acid (10–$12\%$). A total of 134 lipids were identified first from avocado oil, including 122 glycerides and 12 phospholipids, and the total number of carbon atoms contained in the fatty acid side chains of the lipids was 32–68, and the number of double bonds was 0–9. Forty-eight differential lipid compounds with significant effects of the three extraction methods on the lipid composition of avocado oil were excavated, among which the differences in triglycerides (TG), phosphatidylethanol (PEtOH), and phosphatidylmethanol (PMeOH) contents were highly significant, which provided basic data to support the subsequent guidance of avocado oil processing, quality evaluation, and functional studies. ## 1. Introduction Avocado is typical subtropical fruit, and “Hass”, “Choquette”, “Gwen”, “Lula” and “Maluma” are the main cultivated varieties, among which “Hass” has the largest planting area. Avocado is rich in nutrients, containing a variety of vitamins, tocopherols, and trace metal elements such as calcium, magnesium, and zinc, and the oil in the avocado pulp mainly consists of various monounsaturated fatty acids and polyunsaturated fatty acids, of which oleic acid accounts for $34\%$ to $81\%$, 7.2–$38.9\%$ for palmitic acid, 6–$26.6\%$ for linoleic acid, 2.1–$5.8\%$ for linolenic acid, etc. [ 1,2,3]. Avocado oil has been applied to the development of products that aid in lowering blood pressure, are anti-inflammatory, and promote wound healing, with promising applications [4]. Fatty acids are very important components of avocado oil, and their mechanism of action enhances vascular function, reduces the deterioration of nephropathy, and improves nonalcoholic fatty liver in hypertensive rats by improving mitochondrial dysfunction, reducing mitochondrial oxidative stress, decreasing reactive nitrogen species (RNS) production and normalizing NOx activity [5,6,7]. Cristian et al. [ 8] used avocado oil instillation in hypertensive rats and reduced diastolic and systolic blood pressure by $21.2\%$ and $15.5\%$, respectively. In addition, avocado oil not only increases collagen synthesis, reduced the number of inflammatory cells, accelerated the coagulation process and the regeneration of epithelial cells, thus accelerating wound healing [9], but also regulated brain-derived neurotrophic factor (BDNF), oxidative stress and apoptotic molecules, and protected SH–SY5Y cells against cortisol-induced cytotoxicity [10]. Omar et al. [ 11] used rats as a model for type 2 diabetes and confirmed that lipid components such as oleic acid in avocado oil delayed the development of diabetic nephropathy. Pham et al. [ 12] isolated DKB122 from avocado oil extract, which effectively inhibited TNF-α or LPS-induced p65 nuclear migration in HEI-OC1 cells and THP-1 cells and reduced TNF-α-induced expression of inflammatory chemokines and interleukin genes. The functionality of avocado oil was closely related to its nutritional composition, while the nutritional quality of avocado oil was influenced by factors such as fruit variety [3,13], extraction method [14], and fruit storage method [15]. Lozano et al. [ 13] confirmed that total sterols were higher in immature fruits (1.1–$6.2\%$) than in mature fruits (0.8–$2.0\%$) in four avocado varieties, “Zutano”, “Bacon”, “Fuerte”, and “Lula”. Ultrasonic-assisted water extraction [16], mechanical pressing [17], and supercritical CO2 extraction [18,19,20] methods were commonly used to extract avocado, and the results of a comparative study by Tan et al. [ 18,19,20] showed that different extraction methods had an effect on physicochemical properties such as iodine value in avocado oil, but did not have a large effect on fatty acid composition such as oleic acid, which varied in content. Fernanda et al. [ 17] showed that drying of avocado at 60 °C combined with mechanical pressing resulted in better retention of the biological activity of avocado oil. The drying method and storage conditions had a greater influence on the quality of avocado oil. Chaiyavat et al. [ 21] showed that drying conditions at 80 °C and above had a significant effect on the stability of avocado oil and that light-free conditions helped to extend the shelf life of avocado oil. The stability and quality of avocado oil were susceptible to temperature effects and were not suitable for continuous heating processes [22,23]. However, little research had been reported on the effects of extraction methods on avocado lipid composition. In this study, ultra-performance liquid chromatography–time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS) was used to investigate the effects of extraction methods on the components of avocado oil quality, to explore the differential components of avocado oil quality by extraction methods, and to provide basic data support for avocado oil extraction methods, product development, and functional studies. ## 2.1. Preparation of Avocado Oil The variety of avocado was “Hass”, purchased from Zhanjiang Chang-da-Chang Super Shopping Plaza Co., and the fruit was $80\%$ mature (skin color changed from dark green to dark brown). Referring to the method of Liu et al. [ 1], three methods of squeezing extraction, supercritical carbon dioxide extraction, and aqueous extraction were used to extract the oil from avocado pulp, the crude oil was centrifuged in a centrifuge at 5000× g for 10 min, and the collected oil layer was stored at 4 °C. The parameters of that three methods were as follows: Squeezing extraction: Avocado pulp dried at 55 °C for 24 h was squeezed by the sing screw expeller with normal temperature mode, and the crude oils were collected and centrifuged at 5000× g for 10 min, and the crude oil layer was collected. Supercritical carbon dioxide extraction: Avocado pulp dried at 55 °C for 24 h was extracted in a supercritical carbon dioxide extractor. The extraction temperature grades I and II were 45 °C and 50 °C, respectively, and the extraction pressure grades I and II were 5 MPa and 6 MPa, respectively, and the crude oil was collected. Aqueous extraction: A 1 kg sample of avocado oils and 2 kg distilled water were beaten and mixed evenly, and then colloid mill was used for 1 min to obtain slurry solution. Then, 2 kg distilled water was used to clean the machine, and cleaning solutions were collected. The slurry solution and cleaning solution, adjusted to 8.0 with a 1.00 mol/L sodium hydroxide solution, were combined and stirred for 1.5 h at 75 °C water bath, then the mixed solution was centrifuged at 25,000× g for 10 min, and the upper crude oil was collected. ## 2.2. Instrumentations Squeezer (OP101, Shenzhen Yimeikang Electronic Commerce Co., Ltd., Shenzhen, China), supercritical carbon dioxide extractor (HSFE-5 + 1, Jiangsu Gaoke Pharmaceutical Equipment Co., Ltd., Nantong, China), high-speed freezing centrifuge (CR22GIII, Hitachi Limited, Tokyo, Japan), juicer (JYL-C020E, Jiuyang Co., Ltd., Jinan, China), pipeline high shear colloid mill (ZVF300-G5R5/P7R5T4MD, Shanghai Qike Machinery Equipment Co., Ltd., Shanghai, China), ultraviolet–visible spectrophotometer (UV-1780, Shimadzu Corporation, Kyoto, Japan), gas chromatography–mass spectrometry (AOC5000-GC/MS-QP2010plus, Shimadzu Corporation, Kyoto, Japan), ultra-high performance liquid chromatograph–time-of-flight tandem mass spectrometer (LC-30A liquid chromatography, Shimadzu Corporation, Kyoto, Japan), ultra-pure water system (Milli-Q-Synthesis, Milli-pore Company, Boston, MA, USA), multi-tube vortex mixer (MTV-100, Hangzhou Aosheng Instrument Co., Ltd., Hangzhou, China), nitrogen blower (DC-24, Shanghai Ampu Experimental Technology Co., Ltd., Shanghai, China). ## 2.3. Determination of Fatty Acid Composition The fatty acid composition in avocado oil was determined by potassium hydroxide methylation method with reference to the method of Liu et al. [ 24]. A sample of 1.0 μL passed through the chromatographic column (DB-FastFA, 30 m × 0.25 mm × 0.25 μm, Agilent, California, USA) in gas chromatography–mass spectrometry with the inlet temperature of 260 °C, nitrogen as the carrier gas, and the split ratio of 20:1. The initial temperature of the column was 150 °C, then it was raised below the program, and the speed of 10 C/min was raised to 210 °C and kept for 8 min, and the speed of 20 °C/min was raised to 230 °C and kept for 6 min. Finally, the sample passed through a detector with a temperature of 280 °C. ## 2.4. Determination of Lipid Composition The lipid composition in avocado oil was determined equipped with a Phenomenex Kinete C18 column (100 × 2.1 mm, 2.6 µm, Phenomenex, Torrance, CA, USA) with reference to the method of Liu et al. [ 24,25]. One microliter of sample was pumped onto the C18 column at a rate of 0.4 mL/min. The column temperature and chamber temperature were 60 °C and 4 °C, respectively. The mobile phases A and B consisted of H2O–methanol–acetonitrile = 1:1:1 (containing 5 mmol/L ammonium acetate) and isopropanol-acetonitrile = 5:1 (containing 5 mmol/L ammonium acetate). The elution program of mobile phase was performed as $20\%$ B for 0.5 min, $40\%$ B for 1.5 min, $60\%$ B for 3 min, $98\%$ B for 13 min, $20\%$ B for 13 min, and $20\%$B for 17 min. ## 2.5. Data Processing and Analysis All samples were measured 3 times in parallel. The qualitative analysis of shotgun-MS data was treated by the LipidView software (v2.0, ABSciex, Concord, ON, Canada). In the process of data analysis, the analysis parameters were set according to the following figures: the mass tolerance was 0.5, the minimum signal-to-noise ratio was 10, the minimum% intensity was 1, the average flow injection spectrum from the top was $30\%$ TIC, and the total double bonds were ≤12. OriginPro (2021, OriginLab Corporation, Northampton, UK) was used for plotting, thermal map analysis, and statistical analysis, and the SIMCA (14.1, Sartorius Lab Instruments GmbH & Co., KG, Goettingen, Germany) was used for PCA, OPLS-DA, VIP, and S-plot analysis, etc. ## 3.1. Analysis of Fatty Acid Composition and Lipid Composition in Avocado Oil The fatty acid composition in avocado oil was determined by gas chromatography-mass spectrometer (GC-MS), and the retention time of each fatty acid standard was characterized with reference to the retention time of each fatty acid standard, and the relative percentage content was calculated according to the normalization method of chromatographic peak area. The fatty acids of avocado oil mainly consisted of oleic acid (36–$42\%$), palmitic acid (25–$26\%$), linoleic acid (14–$18\%$), palmitoleic acid (10–$12\%$), isoleic acid (6–$7\%$), linolenic acid (0.5–$0.8\%$) and stearic acid (0.5–$0.6\%$). The content of saturated fatty acids and unsaturated fatty acids in avocado oil obtained by three extraction methods was about $26\%$ and $73\%$, among which the content of monounsaturated fatty acids ranged from 54 to $60\%$. UPLC-TOF-MS/MS combined with composite scanning mode was used to analyze the lipid composition in avocado oil, as well as the accurate relative molecular weight, isotope distribution, and secondary mass spectrometry fragmentation information. As shown in Figure 1, a total of 134 lipids were identified in avocado oil, including 122 glycerides and 12 phospholipids. Glycerides were composed of diacylglycerol (DG), ether-linked diacylglycerol (EtherDG), triglycerides (TG), oxidized triglycerides (OxTG), triglycerides (TG_EST), and ether-linked triglycerides (EtherTG), and among of which type numbers were 12, 3, 88, 14, 3, and 2, respectively. Phospholipids were composed of phosphatidylcholine (PC), phosphatidylethanol (PEtOH), phosphatidylglycerol (PG), ether-linked phosphatidylglycerol (EtherPG), and phosphatidylmethanol (PMeOH), and among of which, type numbers were 1, 5, 2, 2, and 2, respectively. As can be seen from Table 1, the total number of carbon atoms in the fatty acid side chains of lipids in avocado oil was 32–68, and the number of double bonds was 0–9. The carbon atoms and double bonds number of DG, EtherDG, TG, OxTG, TG_EST, and EtherTG in glycerides were (32–42, 0–5), (34–36, 2–4), (34–64, 0–9), (50–54, 2–5), (66–68, 3–4), and (53–55, 2–5), respectively. The side chain of glycerides was mainly composed of C15, C16, C17, C18, and C19. The carbon atoms and double bonds number of PC, PEtOH, PG, EtherPG, and PMeOH in glycerides were [34, 2], (34–36, 1–4), (32–34, 0–1), (34–37, 3–5), and [34, 0], respectively. ## 3.2. Analysis of Lipid Content in Avocado Oil The lipid content of avocado oil obtained by three extraction methods was shown in Figure 2. As shown in Figure 2, the highest TG content in glycerides of avocado oil was (830–960) mg/g, followed by DG at (25–30) mg/g, and the highest PEtOH content in phospholipids was (180–1200) ng/g, followed by PMeOH at (40–545) ng/g. The significant difference results showed that the three extraction methods had the highest effect on the TG, PEtOH and PMeOH contents were highly significant, and the differences for EtherDG and PG contents were not significant. ## 3.3. Modeling and Evaluation of Differential Metabolites of Lipids in Avocado Oil From Figure 3A, it can be seen that the avocado oil samples obtained by the three extraction methods could be better distinguished in the OPLS–DA model, and the three oil samples were distributed in the first, third, and fourth quadrants, indicating that they differed from each other. From Figure 3B, it can be seen that the lipid composition data obtained by the three extraction methods were subjected to permutation test and cross–validation analysis (CV–ANOVA), the intercepts of R2 and Q2 curves with vertical coordinates were less than one, and the intercept of Q2 in vertical coordinates was less than zero, indicating that the established OPLS–DA model did not show any overfitting phenomenon. In addition, the significance probability value $p \leq 0.05$ in CV–ANOVA analysis indicated that the established OPLS–DA model was stable, reliable, and statistically significant [26]. As shown in Figure 3C, the avocado oil obtained from the three extraction methods was well clustered. The S–plot was used to identify significant differential metabolites between the two samples, and metabolites with large contributions were concentrated at two ends of the S–plot, while those with small contributions were concentrated around the origin [27]. The abscissa and ordinate represented the co-correlation coefficient and correlation coefficient of the principal component and metabolite, respectively. The red dots in Figure 3D–F indicate metabolites with VIP values >1. From Figure 3D, seventeen significantly different components were analyzed between the squeezing extraction and aqueous extraction methods, including eight metabolites with VIP values >2, namely TG 52:2\|TG 16:0_18:1_18:1 [68], TG 54:3\|TG 18:1_18:1_18:1 [80], TG 52:4\|TG 16:1_18:1_18:2 [70], TG 50:2\|TG 16:0_16:1_18:1 [58], TG 54:5\|TG 18:1_18:2_18:2 [82], TG 50:3\|TG 16:0_16:1_18:2 [59], TG 50:1\|TG 16:0_16:0_18:1 [57], TG 52:5\|TG 16:1_18:2_18:2 [71]. From Figure 3 (E), eighteen significantly different components were analyzed between the supercritical carbon dioxide extraction and aqueous extraction methods, including eight metabolites with VIP values >2, namely TG 52:2\|TG 16:0_18:1_18:1 [68], TG 54:3\|TG 18:1_18:1_18:1 [80], TG 52:3\|TG 16:0_18:1_18:2 [69], TG 54:4\|TG 18:1_18:1_18:2 [81], TG 50:2\|TG 16:0_16:1_18:1 [58], TG 50:1\|TG 16:0_16:0_18:1 [57], TG 50:3\|TG 16:0_16:1_18:2 [59], TG 52:4\|TG 16:1_18:1_18:2 [70]. From Figure 3F, seventeen significantly different components were analyzed between the supercritical carbon dioxide extraction and squeezing extraction methods, including ten metabolites with VIP values >2, namely TG 52:3\|TG 16:0_18:1_18:2 [69], TG 52:4\|TG 16:1_18:1_18:2 [70], TG 54:4\|TG 18:1_18:1_18:2 [81], TG 54:5\|TG 18:1_18:2_18:2 [82], TG 50:3\|TG 16:0_16:1_18:2 [59], TG 50:1\|TG 16:0_16:0_18:1 [57], TG 48:0\|TG 16:0_16:0_16:0 [47], TG 50:2\|TG 16:0_16:1_18:1 [58], TG 52:2\|TG 16:0_18:1_18:1 [68], TG 52:5\|TG 16:1_18:2_18:2 [71]. ## 3.4. Differential Metabolite Differential Analysis and Mass Spectrometry of Lipids in Avocado Oil VIP analysis of lipid components in avocado oil obtained by the three extraction methods was performed on the OPLS–DA model, 77 differential metabolites with VIP value > 1 were obtained, and Krural–Walli’s significance test was performed on the 77 metabolites, and 48 significantly different metabolites were obtained. The differential metabolites were subjected to Z–score transformation to standardize the data, Z–score = (original data – mean)/standard deviation, and the standardized data were produced as a heat map, as shown in Figure 4A. From Figure 4A, it can be seen that the metabolites of the three extraction methods can be categorized into three groups. Groups I, II, and III were the groups with significant upregulation of differential lipid components obtained by the squeezing extraction method, supercritical carbon dioxide extraction method, and aqueous extraction method, respectively, in which 23 lipid components including PC 34:2 [1], PEtOH 34:1\|PEtOH 16:0_18:1 [2], PEtOH 34:2\|PEtOH 16:0_18:2 [3], PEtOH 36:2\|PEtOH 18:1_18:1 [4], TG 60:3\|TG 24:0_18:1_18:2/TG 26:0_16:1_18:2 [102], and TG 61:4\|TG 25:0_18:2_18:2 [107] were upregulated in group I. Seven lipid components including PEtOH 36:4\|PEtOH 18:2_18:2 [6], PG O–37:5\|PG O–16:2_21:3 [10], TG 38:0\|TG 8:0_14:0_16:0 [30], TG 40:0\|TG 10:0_14:0_16:0 [32], TG 40:1\|TG 10:0_12:0_18:1 [33], TG 42:1\|TG 8:0_16:0_18:1 [35] were upregulated in group II. Twenty–three lipid components including TG 54:3\|TG 18:1_18:1_18:1 [80], TG 59:1\|TG 16:0_25:0_18:1 [97], TG 64:2\|TG 28:0_18:1_18:1 [114], TG 50:3;1O\|TG 16:0_18:2_16:1;1O [117], TG 52:2;1O\|TG 16:0_18:1_18:1;1O [118], TG 52:3;1O\|TG 16:0_18:1_18:2;1O [119], TG 52:2;2O\|TG 16:0_19:1_17:1;2O [123], TG 52:4;2O\|TG 16:0_18:2_18:2;2O [125], TG 54:3;2O\|TG 18:1_18:1_18:1;2O [127], TG 54:5;2O\|TG 18:2_19:2_17:1;2O [129] were upregulated in group III. After the precursor ions of selected lipid molecules enter the mass spectrometry Q2, collision–induced dissociation (CID) occurs at a certain collision energy (CE), resulting in fragment ions, and the neutral loss of specific fragment ions or specific functional groups from lipid molecules lead to diagnostic ions. In this study, the differential metabolite PEtOH 34:1\|PEtOH 16:0_18:1 in phosphatidylethanol (PEtOH) was used as an example to analyze its mass spectrometric behavior and fracture mechanism in detail, as shown in Figure 4B. From Figure 4B, m/z 701.5220 corresponded to the mass spectrum information of [M − H]− parent ion of PEtOH 34:1, m/z 125.0009 was phosphoethanol, and m/z 255.2327 and m/z 281.2475 represented the mass spectrum information of [FA 16:0–H]− and [FA 18:1–H]−, respectively. ## 4. Discussion There were few studies on the lipid composition in avocado oil, but there were more studies on pitaya seed oil, coffee bean oil, canola oil, and soybean oil. The present study showed that avocado oil was mainly composed of oleic acid (36–$42\%$), palmitic acid (25–$26\%$), linoleic acid (14–$18\%$), and palmitoleic acid (10–$12\%$), similar to the fatty acid composition in avocado reported by Fernandes et al. [ 28], but there was variability in the fatty acid content, such as low oleic acid content of 10–$20\%$, palmitic acid content was 10–$15\%$ higher, and linoleic acid was about $5\%$ higher, with differences in the variety and origin of avocado leading to differences between the two. In this study, a total of 134 lipid molecules were identified from different extraction methods, which was less than that of pitaya seed oil [152] [24] and cycad oil [169] [29]. Avocado oil was similar to pitaya seed oil in that it consists mainly of glycerides and phospholipids and had the highest content of TG in glycerides and PEtOH in phospholipids, but some variability exists in that avocado oil contained phosphatidylcholine PC, which was lacking in dragon fruit seed oil [24]. Additionally, it was based on the variability of glycerides and phospholipid species in oils and fats that much research work had been completed to identify the source, quality, and variety of oils and fats. Tian et al. [ 30] analyzed and identified 24 triglycerides, mainly OOO (triglyceride of trioleic acid), OOL (triglyceride of 1,2–dioleic acid–3–linoleic acid), OOP (triglyceride of 1,2–dioleic acid–3–palmitic acid), and other unsaturated triglycerides from six different oil tea species and nine different common oil tea varieties and constructed a fingerprint profile of triglycerides in oil tea seeds. The fingerprint profiles of triglycerides in oil tea seeds were also constructed to identify different varieties of oil tea seed oil. The results of Zhao et al. [ 31] showed that LL and OO in DAGs and OLLn and LLL in TAGs were important indicators for the grade identification of olive oil, and these indicators could be used for the quality identification of different grades of olive oil. Therefore, the information on the type and content of microscopic lipid components in oils and fats by profiling could provide new ideas and more accurate analysis for the source, type, and quality identification of oils and fats. The fatty acid content and composition of avocado oil varied depending on the variety, origin [32], and extraction method [1,19,33], with the differences existing in extraction methods being particularly pronounced, yet there were few comparative studies from a microscopic perspective. In this study, 48 differential metabolites were identified from 134 lipid components using OPLS–DA combined with VIP and other methods, among which 23, 7, and 23 differential metabolites were upregulated by the squeezing extraction, supercritical carbon dioxide extraction, and aqueous extraction, respectively, while phospholipids were more abundant in avocado oil obtained by supercritical carbon dioxide extraction, which was in accordance with the principle of similar compatibility. The long extraction process by the aqueous extraction method and the long air contact time resulted in higher OxTG content. In addition, the principles of the pressing method and extraction method were different, resulting in differences in both PMeOH and glycerol ester compounds. Therefore, revealing the differences among the oils and fats obtained by the three extraction methods from the perspective of lipid molecules could provide basic data to support the study of the transformation mechanism of lipid molecules during processing. ## 5. Conclusions In this study, the UPLC–TOF–MS/MS was used to profile the lipid profile of avocado oil first, and 134 lipid components were identified, including 122 glycerides and 12 phospholipids. The total number of carbon atoms contained in the fatty acid side chains of the lipids ranged from 32 to 68, and the number of double bonds ranged from 0 to 9. The differences between the three extraction methods were highly significant for the contents of TG, PEtOH, and PMeOH, and not significant for the contents of EtherDG and PG. 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--- title: Establishment of Residual Methods for Matrine in Quinoa Plants and Soil and the Effect on Soil Bacterial Community and Composition authors: - Xiangjuan Hui - Hongyu Chen - Shuo Shen - Hui Zhi - Wei Li journal: Foods year: 2023 pmcid: PMC10048628 doi: 10.3390/foods12061337 license: CC BY 4.0 --- # Establishment of Residual Methods for Matrine in Quinoa Plants and Soil and the Effect on Soil Bacterial Community and Composition ## Abstract A method was developed for the determination of matrine residues in quinoa (Chenopodium quinoa Willd.) plants and soil by liquid chromatography triple quadrupole tandem mass spectrometry (LC-MS/MS) with QuEChERS clean-up. Matrine from soil, quinoa roots, stems, leaves and seeds was extracted with $25\%$ ammonia, 20 mL acetonitrile/methanol, salted with sodium chloride (NaCl) and purified with anhydrous magnesium sulfate (MgSO4), N-propyl ethylenediamine (PSA) and graphitized carbon black (GCB). Then a chromatographic separation was performed on a Shim-pack XR-ODS II (75 mm × 2.0 mm, i. d., 2.2 µm) column with a gradient elution of 5 mmol/L ammonium formate-methanol as the mobile phase and monitored in multiple reaction monitoring modes (MRM) in electrospray positive ionization mode. The results showed that in the range of 0.005~1 mg/L, the linear correlation coefficients of matrine in the five matrices were all above 0.999. The LOQs for soil, quinoa roots, stems, leaves and seeds were 0.005, 0.005, 0.01, 0.01 and 0.005 mg/kg, respectively. The mean recoveries ranged from $74.42\%$ to $98.37\%$, with RSDs of 1.25–$6.84\%$ at the three concentration addition levels. The average intra-day and inter-day recoveries were 73.92–$92.36\%$ and 78.56–$90.18\%$, respectively, with RSDs below $8.72\%$ and $9.43\%$. The recoveries and reproducibility of the method were superior. The method was used to determine the actual samples, which indicated that the half-lives of matrine in quinoa seeds, leaves, stems and soil were 1.28–1.32, 1.03–1.21, 0.81–0.92 and 0.93–0.97 d. It has a half-life below 30 d, which is an easily dissipated pesticide. The method is simple, sensitive, accurate, reliable and applicable to a wide range of applications, and it can achieve the rapid multi-residue determination of matrine to a certain extent. Next Generation Sequencing was used to explore the effects of exposure to high and low doses of matrine on soil bacterial communities and the composition of the three soils in the Qinghai Province (Haixi, Haidong and Haibei). The results showed that the number of ASVs increased significantly after treatment with matrine at an effective dose of 0.1 mg/kg than after treatment with matrine at an effective dose of 5.0 mg/kg. Similarly, bacterial abundance was higher after 0.1 mg/kg of matrine treatment than after 5.0 mg/kg of matrine treatment. The inhibitory effect on some bacterial flora was enhanced with an increase in matrine application, while the inhibitory effect on bacterial flora was weakened with time. Applying a certain dose of matrine e changed the relative abundance of the dominant bacterial genera of the soil bacteria. ## 1. Introduction Quinoa (Chenopodium quinoa Willd.), a genus of quinoa in the Amaranthaceae family, is also known as quinoa grain, southern quinoa, and quinoa. The high altitudes of the Andes in South America are its origin, with nearly 7000 years of plant cultivation. It was one of the main foods of the ancient Inca indigenous people and was called the “mother of grains” [1]. It is mainly grown experimentally in Africa, Europe and Asia. Quinoa prefers cold and dry but mild relative humidity, highland and alpine areas. The soil needs to be well drained with pH levels from six to nine. The climatic, geographic, and soil conditions of the Qaidam Basin in the Qinghai Province almost replicate the growth and cultivation environment of the Andean highlands of South America, being the best suitable area for quinoa cultivation in the Tibetan Plateau [2]. Quinoa contains all the essential amino acids and is rich in high-quality protein and many trace elements, which makes it a “whole food” [3]. In recent years, people have gradually become aware of the nutritional value of quinoa, with higher demands on its quality and flavor, leading to an increase in the area under cultivation. During its growth, various pests and diseases have emerged, with an increasing trend year by year. At present, the reported diseases of quinoa include downy mildew, leaf spot, black stem, grey mold, root rot, etc., among which downy mildew and leaf spot are the most severe damage to quinoa cultivation in China; insect pests include cutworm, mole cricket, scarab beetles and Plutella xylostella [1,4]. To meet the constant demand for quinoa quality and yield, producers are forced to use pesticides unscientifically, blindly and in large quantities, resulting in resistance and tolerance of pests and weeds, making the efficacy of pesticides gradually decrease or even disappear. At the same time, long-term reliance on chemical pesticide control, resulting in surface pollution, water pollution and other problems, is increasingly presented. Matrine is a quinolizidine alkaloid isolated from Sophora japonica. Molecular formula: C15H24N2O; relative molecular mass: 248.37; CAS No.: 519-02-8; relative density 1.16; melting point 77 °C; boiling point 396.7 °C; the appearance of the original drug is a white powder. It can be soluble in water, benzene, chloroform, methanol, ethanol, and slightly soluble in petroleum ether [5,6,7]. The matrine molecule contains two nitrogen atoms, N16 in the amide state, which is weakly basic, and N1 knotted in the ring as a tertiary amine. Its steric configuration facilitates the acceptance of protons, thus effectively blocking the effects arising from the space-site blocking effects; hence it has a strong basicity [8,9]. The chemical structural formula is illustrated in Figure 1. Domestic matrine formulations include $0.6\%$ aqueous matrine, $0.8\%$ lactone aqueous matrine, $1\%$ matrine solution, $1.1\%$ matrine solution, $1.1\%$ matrine powder, etc. Pesticide products with matrine as the active ingredient have been widely used on various crops such as fruits, vegetables, tea and tobacco [10,11]. The agent has the characteristics of low toxicity, spectrum and safety, with excellent insecticidal and antibacterial effects. It is effective against diseases such as gray mold of grapes, late blight of potatoes, black star disease of pear trees, downy mildew of zucchini, mycorrhizal disease of rape and virus disease of tobacco, as well as pests such as cabbage moth, aphid, tea looper, tea black poison moth, root-knot nematode and chard moth [12,13,14,15]. More than 90 companies in China have registered 115 single or compounded formulations with matrine as the active ingredient. Although matrine is a plant-derived insecticide, it is generally considered to be environmentally compatible. Matrine has a half-life of 6.7–21.9 d in cucumber and kale soils [16] and a half-life of 7.64 d in tobacco [17]. It struggles to move deep into the soil, is concentrated in the shallow 0–10 cm layer of soil and is easily degraded in pond water and river water [18]. However, excessive amounts of matrine can be toxic to humans and livestock; it may paralyse the respiratory system and cause nephrotoxicity. To provide a scientific basis for rational drug use, its safety needs to be evaluated by establishing a stable and convenient assay with high sensitivity [19,20,21]. The “GB2763-2021, National food safety standard: maximum residue limits for pesticides in food” specifies a maximum residue level of 5 mg/kg for matrine on kale, cucumber and pear and 1 mg/kg on orange, tangerine and mandarin. However, no national test standard detection methods were given for testing the residues of matrine in these substrates. Fewer reports involve the detection of residues in soil, and the detection of residues in quinoa has not been reported yet [22]. Currently, there are liquid mass spectrometry [15,23], gas chromatography [16,24], high-performance capillary electrophoresis [25] and thin layer scanning [26] methods for the qualitative and quantitative determination of matrine. Most of the existing domestic literature reports and assays are about the determination of matrine bases in traditional Chinese medicine and other aspects [27,28,29,30,31]; however, the quinoa matrix contains an increased number of special components such as pigments, organic acids and phenols compared to traditional Chinese medicine. It is easy to interfere with the confirmation of the target to varying degrees. Microorganisms are important indicators of soil ecosystem health [32,33] and can characterize changes in soil quality more rapidly than physical and chemical indicators in the soil. The bacterial community structure of the soil is involved in energy flow and elemental cycling in ecosystems [34], such as rhizobacteria and nitrifying bacteria [35]. It affects the fertility status of the soil and alters the microbial diversity of the soil and the carbon source utilization capacity of the microbial community [36,37]. Given this, this study was conducted to establish a method for the rapid determination of matrine residues in quinoa plants and soil by QuEChERS-LC-MS/MS. It will provide technical support for the safety of quinoa and give methodological references for the determination of other kinds of pesticides in the soil environment. Next Generation Sequencing of bacteria in the soil to determine changes in bacterial communities provides a theoretical basis for the rational application of matrine. ## 2.1. Chemicals and Reagents The chemicals and reagents used are as follows. Methanol (purity, $100\%$), acetonitrile (purity, $100\%$), acetic acid (purity, $100\%$), formic acid (purity, ≥$98\%$), ammonium formate (purity, ≥$99\%$), ammonium acetate (purity, ≥$99\%$, chromatographic grade, Merck, Darmstadt, Germany); anhydrous magnesium sulfate, sodium chloride and ammonia (analytical grade, Sinopharm Chemical Reagent Co., Ltd., Shanghai, China); ethylenediamine-N-propyl silane (PSA), graphitized carbon black (GCB), Florisil and octadecyl bonded silica gel (C18) (Angela Technologies, Tianjin, China). Matrine standard (purity, $95.2\%$, Pesticide Quality Supervision and Inspection Center, Shenyang Research Institute of Chemical Industry, Shenyang, China); $0.6\%$ matrine aqueous (Inner Mongolia Qingyuanbao Biotechnology Co., Ltd., Bayannur, China). ## 2.2. Field Experiments Dissipation experiments: field experiments were designed according to the requirements stated in the Guidelines for Pesticide Residue Tests and the Standard Operating Procedures for Pesticide Registration Residue Field Tests [38,39]. Each plot was 15 m2 and applied at a rate of 720.36 g a.i./hm2 matrine, with $0.6\%$ aqueous active ingredient and with 1, 2 and 3 applications each. Samples were collected at 2 h, 1 d, 3 d, 5 d, 7 d and 14 d after application. Each treatment was set up in three replicates with a protective isolation zone between treatments and blank control. Residue experiments: two application rates of 360.18 g a.i./hm2 (recommended rate) and 720.36 g a.i./hm2 (twice the recommended rate) were set; experiments were conducted in accordance with the Guidelines for Testing Pesticide Residues in Agricultural Crops (NY/T788-2018) [38]. Each treatment was applied 1, 2 and 3 times, with 3 replicates for each treatment. The sampling intervals were 3 d, 7 d and 14 d from the last application. There was a blank control and a protection zone between treatments. Sample collection and preparation: roots, stems, leaves, and quinoa seeds were collected randomly per plot, their impurities were removed, and they were cut. The samples were shrunken to over 1 kg and packed in closed plastic bags. Sampling labels were glued and stored in a refrigerator at −20 ± 2 °C. The soil was selected from a 0–10 cm tillage layer and filtered through a 40 mesh sieve to remove impurities. Soil samples were stored as in quinoa plants. The basic physicochemical properties of the test soils were determined by conventional analytical methods (Table 1). ## 2.3. Equipment, Mass Spectrometry and Chromatographic Conditions Equipment: a liquid chromatography-triple quadrupole tandem mass spectrometer (LC-MS/MS 8040 Shimadzu, Kyoto, Japan), a high-capacity high-speed centrifuge (TGL-50, Changzhou Shen Guang Instrument Co., Ltd., Changzhou, China), an ultrasonic cleaner (SB-3200DTD, Nanjing Emmanuel Instruments Co., Ltd., Nanjing, China), a vortex mixer (Vortex Genie 2; Scientific Industries, Inc., Bohemia, NY, USA), and an electronic analytical balance (SQP, Sartorius Scientific Instruments Co., Beijing, China). Mass spectrometry conditions: the molecular mass of matrine is 249, which has a strong ionization efficiency in ESI positive ion mode; the atomization gas flow rate was 3.0 L/min; the drying gas flow rate was 15 L/min; the DL temperature was 250 °C; the heating block temperature was 450 °C. According to the results of the precursor ion scan and the product ion scan of matrine, the precursor ion m/$z = 249$ and product ions m/$z = 148$, 97.95 and 55 were monitored. The quantitative ion was m/$z = 148$, which was used for the qualitative and quantitative analysis of matrine. Other mass spectrometry parameters are listed in Table 2. Chromatographic conditions: the analytical column was a Shim-pack XR-ODSⅡ(75 mm × 2.0 mm, i. d., 2.2 µm); the flow rate was 0.3 mL/min; the injection volume was 1 μL; the column temperature was 40 °C; the mobile phase included A as methanol and B as a solution containing 5 mmol ammonium formate; the mobile phase gradient elution was: 0~1.5 min, $10\%$ A; 1.5~2 min, 10~$90\%$ A; 2~6.5 min, $90\%$ A; 6.5~7.5 min, 90~$10\%$ A; 7.5~8 min, $10\%$ A. ## 2.4. Sample Preparation and Clean-Up Procedure Soil: weigh 10.00 g of soil (accurate to 0.01 g) in a 50 mL stoppered centrifuge tube, add 2 mL of $25\%$ ammonia, vortex well and let stand for 10 min, then add 20 mL of acetonitrile, vortex and shake for 3 min to mix well. Add 1 g NaCl and 4 g anhydrous magnesium sulfate, vortex and shake for 1 min; centrifuge at 5000 r/min for 5 min. Take 1.5 mL of supernatant in a 2 mL centrifuge tube with 100 mg PSA and 100 mg anhydrous magnesium sulfate, vortex for 1 min, and centrifuge at 12,000 r/min for 2 min. Filter the supernatant through a 0.22 μm membrane and transfer it to a liquid chromatography glass vial for measurement. Quinoa roots, stems and seeds: weigh 2.00 g (to 0.01 g) of quinoa root, stem and seed samples in a 50 mL stoppered centrifuge tube, add 3 mL of $25\%$ ammonia, vortex well, and let stand for 10 min. Add 20 mL of acetonitrile, vortex and shake for 3 min, mixing well. Add 1 g NaCl and 4 g anhydrous magnesium sulfate, vortex and shake for 1 min; centrifuge at 5000 r/min for 5 min. Add 1.5 mL of the root and stem supernatants into a 2 mL centrifuge tube pre-spiked with 100 mg PSA, 20 mg GCB and 100 mg anhydrous magnesium sulfate. Take 1.5 mL of the seed supernatant and add to a 2 mL centrifuge tube pre-spiked with 70 mg PSA, 20 mg GCB and 100 mg anhydrous magnesium sulfate, vortex for 1 min and centrifuge at 12,000 r/min for 2 min. Filter the supernatant through a 0.22 μm membrane and transfer it to a liquid chromatography glass vial for measurement. Quinoa leaves: use 20 mL of methanol as an extractant, 150 mg of PSA, 20 mg of GCB and 100 mg of anhydrous magnesium sulfate as a purifying agent. Other pretreatment conditions were the same for quinoa roots, stems and seeds. ## 2.5. Standards Weigh 10 mg of matrine standard precisely, dissolve it with methanol and fix the volume in a 100 mL brown volumetric flask. Dissolve it by ultrasonication and prepare the standard stock solution with a concentration of about 100 mg/L, then keep it in a cooler at −18 °C away from light. Accurately measure 1 mL of matrine standard stock solution, dilute it step by step with a blank sample extract, and prepare matrix-matched standard solutions with mass concentrations of 0.005, 0.01, 0.05, 0.1, 0.5 and 1.0 mg/L. Matrine was measured under the above conditions. The standard curve was plotted with the mass concentration of the sample as the horizontal coordinate and the corresponding peak area as the vertical coordinate. ## 2.6. PHI and Risk Assessment PHI can be defined as the number of days between the last application of pesticide to the crop and harvest [40,41]. It is determined based on the maximum residue limit (MRL) values and degradation curves. PHI can be calculated with the help of the following formula [41]:PHI = [Ln (initial deposit) − Ln (MRL)]/slope of the regression equation The pesticide dietary exposure assessment methodology was assessed using the NEDI model with the following equations [42]:NEDI = STMR × Fbw [1] RI = NEDIADI × $100\%$ [2] where F is the domestic dietary consumption per capita (kg/d) and bw (body weight) is the body weight per capita (kg); all body weights in China are calculated at 63 kg. STMR is the median residue test value (mg/kg); ADI (Acceptable daily intake) is the allowable daily intake of pesticides (mg/kg bw); RI is the risk index, where an RI < $100\%$ indicates that the dietary risk is in an acceptable range, while the opposite indicates that the risk is unacceptable, and the higher the value, the higher the risk. ## 2.7. Next Generation Sequencing to Study—The Effect of Matrine on Different Soil Bacterial Community Soils from three locations in the Qinghai Province (Haixi, Haibei and Haidong) were collected for this experiment, and ultra-pure water was added to bring the water content of the soil to $24\%$ (equivalent to $60\%$ of the field water holding capacity). Moreover, 0, 1 and 50 µg of matrine were added to give a concentration of 0 (control), 0.1 (low dose) and 5.0 mg/kg (high dose) to the soil. They were vortexed for 5 min, mixed, and then preincubated in a constant temperature incubator at 25 ± 2 °C protected from light. The soil water content was maintained by regular water replenishment. Samples were taken at 3 d and 10 d and stored in a cold room at −18 °C for measurement (Table 3). Three replicates were set up for each treatment group. Fifty-four soil samples (including three replicates) were selected from three locations in the Qinghai Province: Xiangrid in Haixi, Jintan Township in Haibei and Ledu in Haidong. The 16SrDNA Next Generation Sequencing analysis of bacteria was performed. Total genomic DNA was extracted from the samples using the CTAB/SDS method, and the quality of the total extracted DNA was checked by $1\%$ agarose gel electrophoresis. PCR was performed using the Phusion® High-Fidelity PCR Master Mix from New England Biolabs, Ipswich, MS, USA. The PCR reaction conditions are as follows: 98 °C (10 s), 50 °C (30 s) and 72 °C (30 s) for 30 cycles; 72 °C (5 min). The PCR products were examined by electrophoresis using a $2\%$ concentration agarose gel. Next Generation Sequencing was performed using NovaSeq 6000 (Illumina, San Diego, CA, USA). Sequencing was performed by Beijing NovoSeq Technology Co. (Beijing, China). The Clean Tags were compared with the reference database using Vsearch (Version 2.15.0) to detect the chimera sequences, and then the chimera sequences were removed to obtain the Effective Tags [43]. The spliced sequences were further processed using the QIIME2 (Version QIIME2-202006) analysis software to obtain the initial ASV; ASVs with an abundance of less than 5 were filtered out [44]. In order to analyze the diversity, richness and uniformity of the communities in the sample, alpha diversity was calculated from 7 indices in QIIME2, including Observed_otus, Chao1, Shannon, Simpson, Dominance, Good’s coverage and Pielou_e. To find out the significantly different species at each taxonomic level (Phylum, Class, Order, Family, Genus, Species), the R software (Version 3.5.3) was used for MetaStat and t-test analysis. ## 3.1. Optimization of Chromatographic Conditions The relative abundance of the target ions to be measured in the organic solvent methanol and acetonitrile was examined. Methanol is a proton-like solvent that forms hydrogen bonds and has improved selectivity for separating compounds with acids and bases. Acetonitrile is a polar molecule. Its carbon-nitrogen triple bond contains unbonded electrons and can bind to compounds containing empty orbitals. The results showed that the response intensity and peak area of matrine in methanol were better than those in acetonitrile, as indicated in Figure 2. Therefore, methanol was used as the organic phase of the mobile phase in the experimental optimization. Given the attainment of a good peak shape, response value and a high signal-to-noise ratio, aqueous solutions of $0.01\%$, $0.1\%$ and $0.2\%$ formic acid and acetic acid, aqueous solutions of 5 mmol, 10 mmol and 15 mmol ammonium formate, an aqueous solution of 5 mmol ammonium acetate, an aqueous solution of $0.1\%$ formic acid-5 mmol ammonium formate and an aqueous solution of $0.1\%$ acetic acid-5 mmol ammonium acetate were investigated as mobile phases, respectively. The results demonstrate that the addition of acid to the aqueous solution in the mobile phase resulted in an early peak time, low response value and broad peak shape; the addition of 5 mmol ammonium formate or ammonium acetate to the aqueous phase could adjust the pH value of the solution, improve the ionization efficiency and the peak area response value of the mass spectrometry, and improve the peak shape of the target compounds, and also result in a high signal-to-noise ratio. However, the relative abundance of ions was higher when 5 mmol ammonium formate was used. Then the ammonium formate dosages of 5 mmol, 10 mmol and 15 mmol were optimized. There was no significant difference in the effects of the three ammonium formate dosages on the ion response intensity and peak shape, etc. Therefore, 5 mmol ammonium formate was selected as the aqueous phase by experimental investigation. ## 3.2. Optimization of Mass Spectrometry Conditions Matrine is a weakly basic compound containing nitrogen in its chemical structure, and due to the easily additive protons with positive charges, its suitable for detection in ESI+ mode [45,46]. Based on the molecular structure characteristics and chemical ionization properties of the compounds, the signal differences of the compounds in positive and negative ion modes of ESI sources were examined, respectively. In this research, the compounds’ parent ions were first obtained by a preliminary mass spectrometry scan (Q1 MS) using a needle pump with continuous injection in positive ion mode. Then it was subjected to a sub-ion full scan. Two fragment ions with strong response signals were selected, the one with a higher response value being the quantitative ion and the other being the qualitative ion. The parent ion and these two fragment ions were then formed into a detection ion pair, and the declustering voltage (DP) and collision energy (CE) were optimized in MRM mode [47]. The optimization results are shown in Table 4. After the optimized daughter ion pair, cleavage voltage, and collision energy, the compound had a more robust mass spectrometry signal response. The stability and reproducibility of the mass spectrometric determination were enhanced to meet the quantitative limit requirements. ## 3.3.1. Selection of Extractants for Different Matrices Alkaloids can be divided into lipophilic alkaloids and water-soluble alkaloids according to their solubility properties. Matrine has tertiary amine bases with a small molecular weight, which are soluble in both lipophilic organic solvents such as benzene, ether, and trichloromethane, etc., and hydrophilic organic solvents such as methanol and acetonitrile, as well as in aqueous alkali solutions [48,49]. When lipophilic organic solvents are used, extraction, centrifugation, rotary evaporation and concentration processes are required. The operation procedures are complicated, so to shorten the extraction time and save organic solvents, the effects of two hydrophilic extractants, methanol and acetonitrile, on the recovery of matrine in different matrices were screened. The results showed (Figure 3A) that the average recoveries of matrine in soil, root, stem, leaf and seed were $10.48\%$, $66.10\%$, $67.36\%$, $43.36\%$, $54.79\%$ and $23.57\%$, $74.26\%$, $71.21\%$, $40.07\%$, $69.75\%$ when methanol and acetonitrile were used as extractants, respectively. Thus, methanol was selected as the best extractant of matrine in leaves and acetonitrile as the best extractant of matrine in soil, roots, stems and seeds. ## 3.3.2. Optimization of the Dosage of Different Matrix Extractants The effects of three dosage volumes (10 mL, 20 mL, 40 mL) of methanol or acetonitrile on the average recovery effect of matrine in different matrices were compared. The results showed that (Figure 3B): the average recoveries of matrine in soil were $17.72\%$, $33.48\%$ and $18.36\%$ when the extraction solvent volumes were 10, 20 and 40 mL, respectively; and the average recoveries were higher when 20 mL acetonitrile was selected as the soil extractant. The average recoveries of matrine in roots were $58.24\%$, $67.78\%$ and $68.56\%$, respectively. The average recoveries of matrine in stems were $61.38\%$, $66.14\%$, and $66.21\%$, respectively. The average recoveries of matrine in leaves were $38.91\%$, $43.80\%$ and $45.09\%$, respectively. The average recoveries of matrine in the seeds were $51.67\%$, $60.43\%$ and $60.51\%$, respectively. The average recovery of matrine in the five matrices increased with the extractant dosage, and there was no significant difference in the average recovery of matrine at 20 mL and 40 mL of extractant, respectively. Hence, to reduce the number and amount of organic solvents as well as the risk to humans, 20 mL acetonitrile was selected as the soil, root, root stem and seed extractant and 20 mL methanol as the leaf extractant. ## 3.3.3. Optimization of Ammonia Dosage for Different Substrates The chemical structure of matrine contains amino (-NH2), which is weakly basic, and it is known from a literature survey that matrine is better extracted under weakly basic conditions [50]. Therefore, experiments were conducted to investigate the extraction recovery of matrine at 20 mL acetonitrile + 1, 2, 3 and 5 mL ammonia, respectively, using soil, quinoa roots, stems, and seeds as substrates, and quinoa leaves as substrates at 20 mL methanol + 1, 2, 3 and 5 mL ammonia, respectively. The results showed that (Figure 3C) the average recoveries of matrine in the four extracts were: $72.74\%$, $97.86\%$, $94.36\%$ and $90.26\%$, $72.72\%$, $76.68\%$, $83.56\%$ and $83.31\%$, $68.40\%$, $72.26\%$, $75.44\%$ and $75.95\%$ for soil, quinoa root, stem, leaf and seed substrates, respectively; $75.96\%$, $74.45\%$, $71.25\%$, and $70.36\%$, $76.64\%$, $77.65\%$, $78.63\%$, and $77.80\%$. Improving the recovery rate, reducing the amount of ammonia and the environmental risk were considered. Consequently, 20 mL acetonitrile + 2 mL ammonia water was selected for soil, 20 mL acetonitrile + 3 mL ammonia water for quinoa roots, stems and seeds, and 20 mL methanol + 1 mL ammonia water for quinoa leaves as the best extraction solvent. ## 3.3.4. Selection of Sorbent The QuEChERS method is fast, efficient, time-saving and simple to operate, eliminating the need for complex purification processes. It reduces the pretreatment time of organic solvents, thus reducing the harm to the human body. Commonly used purification agents for the Qu ECh ERS method are ethylenediamine-N-propyl silane (PSA), octadecyl bonded silica gel (C18), Florisil, graphitized carbon black (GCB), etc. Soil purification: it is complex to remove impurities from the soil matrix that interfere with the detection of the target compounds. The effects of different doses of PSA, C18, Florisil and GCB on the recovery of matrine were compared. As shown in Figure 4A–D, PSA had no significant adsorption effect on matrine, with recoveries mostly ranging from 80–$100\%$, while GCB, Florisil and C18 had strong adsorptions on matrine. The adsorption of impurities by 30 mg PSA was unsatisfactory, and the recovery was low. The recovery increased when the PSA content was greater than 50 mg. When the PSA content was more significant than 100 mg, the recovery was unchanged with an increase in the PSA dose. The recoveries of C18, Florisil and GCB were below $75\%$ when they were used as purifying agents, for which the recoveries of matrine gradually decreased with an increase in purifying agent dosage. GCB produced a more substantial adsorption effect on the compounds. When comparing the four purifying agents, PSA recovered relatively quickly and also removed most of the impurities in the soil, with less interference of spurious peaks and low pigment content in the soil. Moreover, PSA (100 mg) was used to purify the soil samples. Purification of the quinoa plant: quinoa is rich in vitamins, polyphenols, flavonoids, saponins and phytosterols. It also has high protein, $83\%$ unsaturated fatty acids in its fat content and high pigment content [51,52]. An examination of the effect of different doses (30 mg, 50 mg, 70 mg, 100 mg, 150 mg) of PSA, C18, Florisil and GCB (20 mg, 40 mg, 60 mg, 80 mg, and 100 mg) on the recovery of matrine from quinoa plants was required. As shown in Figure 4A, the recoveries of quinoa roots were above $80\%$ when different doses of PSA were used. At 100 mg PSA, the recovery was $96.54\%$, and at 150 mg, the recovery showed a decreasing trend. The recoveries of quinoa stems were between 65–$80\%$; with increasing PSA doses, the recoveries of matrine gradually increased. When the PSA dose was more than 100 mg, there was no significant increasing trend in the recovery. The recovery of quinoa leaves was 75–$85\%$. The recoveries were above $80\%$ at 70 mg PSA. The dose of purgative increased, but there was no significant increasing trend in the recoveries. For quinoa seeds, the recoveries were in the range of 80–$95\%$. With the same effect on soil, quinoa root, stem and leaf substrates, all of which gradually increased with an increasing PSA dose matrine recovery, the highest recovery was achieved at 150 mg PSA. The recoveries of quinoa roots, stems, leaves and seeds were between 60–$75\%$, 45–$65\%$, 70–$75\%$ and 60–$80\%$ when different doses of C18 were used, depending on the matrix. As shown in Figure 4B, the recoveries of matrine in all four matrices decreased with increasing doses of the C18 purification agent. Although C18 can adsorb non-polar interfering substances such as sugars, fats, lipids and sterols in the sample matrix, its adsorption of pigments was not satisfactory. As shown in Figure 4C, the recovery of quinoa roots and stems was inversely proportional to the purifier dose when using different doses of Florisil. Quinoa root, stem and leaf recoveries were 60–$80\%$, 60–$65\%$ and 60–$75\%$. There was no significant difference between seeds with less than $70\%$ recovery at 30 mg Florisil, $86.3\%$ recovery at 50 mg Florisil, or 75–$80\%$ recovery at Florisil doses greater than 50 mg. As shown in Figure 4D, the recovery of matrine in quinoa roots, stems, leaves and seeds decreased as the dose of GCB increased, using different doses of GCB. It is related to the fact that GCB has a strong adsorption feature and pigment removal ability, but it also adsorbs some pesticides. With 20 mg GCB, the recoveries of matrine in quinoa roots, stems, leaves and seeds were $81.15\%$, $65.47\%$, $67.41\%$ and $81.37\%$. GCB was more effective in removing pigment compared to C18 because of the high pigment content in quinoa plants. To reduce the damage to the machine and the waste of resources, the experimental protocol of 20 mg GCB with a suitable dose of PSA adsorbent was used to purify the four substrates to improve their recovery. In summary, the recovery rate and the degree of pigment purification selected the best purification method. Multiple repetitions were performed to determine the optimal ratio and dosage of the reagents used in the purification process. The highest recovery of quinoa root was $96.54\%$ at 100 mg PSA, and the trend of decreasing recovery was observed at 150 mg, so the purification method of quinoa root was 100 mg PSA + 20 mg GCB. Quinoa stems showed the highest recovery at 100 mg PSA with $77.97\%$. When the dose of the purifying agent was greater than 100 mg, there was no significant increasing trend in the recovery, so its purification was 100 mg PSA + 20 mg GCB. At less than 70 mg PSA, the recovery of matrine in quinoa leaves was less than $80\%$. While at 70 mg, 100 mg, and 150 mg PSA, the recoveries were in the range of 80–$85\%$, no significant differences were found between the recoveries of matrine at the three doses. So the experiment was further optimized for the dosage of PSA; 70 mg PSA + 20 mg GCB, 100 mg PSA + 20 mg GCB 150 mg PSA + 20 mg GCB were analyzed. The results showed that the recovery of matrine in quinoa leaves was ideal when 150 mg PSA + 20 mg GCB was used (Figure 4E). Less than 70 mg PSA resulted in less than $85\%$ recovery of matrine from quinoa seeds, while $86.81\%$, $87.83\%$ and $96.00\%$ were recovered at 70 mg, 100 mg and 150 mg PSA. To reduce the waste of resources, the dosage of PSA was also further optimized and analyzed for 70 mg PSA + 20 mg GCB, 100 mg PSA + 20 mg GCB and 150 mg PSA + 20 mg GCB. The results showed that the best recovery of matrine in quinoa seeds was achieved at 70 mg PSA + 20 mg GCB (Figure 4F). ## 3.4. Evaluation of Matrix Effects In this study, matrix effects were calculated based on the slope of the curve at six concentration levels of the matrix-matched calibration curve compared with the corresponding slope of the solvent calibration curve. A positive value of ME indicates signal enhancement of the target caused by the matrix, while a negative value indicates signal suppression. It is generally considered that when \|ME\| < $20\%$, the matrix effect is negligible, and the solvent standard curve can be used for quantitative analysis; with $20\%$ < \|ME\| < $50\%$, the matrix effect is considered vital and a blank matrix standard curve is needed for quantitative analysis; while with an \|ME\| > $50\%$, a new pretreatment method suitable for this sample matrix needs to be established [53]. The results showed (Table 4) that the absolute values of ME of matrine in all five matrices were less than $20\%$. Due to the weak matrix effect, the solvent standard curve was finally used for quantitative analysis in this study. ## 3.5. Validation The prepared matrix standard solution was determined according to the above instrumental method. The standard curve was drawn with the sample’s mass concentration (x) as the horizontal coordinate and the peak area (y) as the vertical coordinate. The linear regression equations of matrine in soil, quinoa roots, stems, leaves and seeds were $y = 2$,866,193x + 490,856, $y = 3$,214,181x + 7320, $y = 3$,282,544x + 7607, $y = 3$,042,360x + 45,875 and $y = 3$,056,940x + 7513 with the correlation coefficients of R2 of 0.9994, 0.9993, 0.9992, 0.9993 and 0.9994. It was shown that the mass concentration and peak area of matrine were linearly correlated in the range of 0.005–1 mg/L. The LOD of matrine in soil, quinoa roots and seeds was 0.001 mg/kg, and the LOQ was 0.005 mg/kg. The LOD of matrine in quinoa stems and leaves were 0.003 mg/kg, and LOQ was 0.01 mg/kg for both; details are shown in Table 4. The linear equation, LOQ and LOD followed the pesticide residue detection requirements (NY/T 788-2018) [38]. Around 2.0 g each of quinoa plant samples without matrine and 10.0 g of soil samples were weighed. The addition recovery test was performed in blank soil at three concentration levels of 0.01, 0.1 and 1 mg/kg. Matrine was set at three concentration levels of 0.1, 1.0 and 10 mg/kg in the blank quinoa root, stem, leaf and seed substrates for the recovery test. Five parallel injections were performed at each concentration level, and the determination was carried out according to the above experimental method. The recoveries were calculated. The results showed (Table 5) that the matrine in soil, quinoa roots, stems, leaves and seeds were 86.42–$89.76\%$, 80.59–$98.37\%$, 72.42–$87.03\%$, 74.62–$89.72\%$ and 88.93–$94.42\%$, respectively. The RSDs were 3.08~$6.36\%$, 2.63~$6.84\%$, 2.22~$6.45\%$, 1.25~$4.64\%$ and 2.02~$4.12\%$, respectively, which met the requirements for pesticide residue analysis. Intraday precision was determined by analyzing six parallel determinations of each spiked level over 1 day. Interday precision was calculated by analyzing three consecutive days of measurements with three replicates per day. The intra- and inter-day precision of the method was expressed as RSD [54,55]. The results showed (Table 5) that the intraday and interday precision studies of the five matrices fulfilled the requirements for pesticide residue detection with RSDs below $8.72\%$ and $9.43\%$. ## 3.6.1. Dissipation Dynamics of Matrine in Quinoa Plants and Soil By regulating the residue dissipation test protocol in the field, $0.6\%$ matrine aqueous at 720.36 g a.i./hm2 was applied one, two and three times at the 4–6 leaf stage of quinoa with three replications per treatment, sampled at 0 d, 1 d, 3 d, 5 d, 7 d and 14 d. The residue dynamics details were determined by analyzing the samples from the quinoa plant and soil for the dissipation dynamics test and are shown in Table 6. Figure 5 shows that the dissipation of matrine on quinoa seeds, stems, leaves and soil showed a fast and slow trend. The dissipation dynamics of $0.6\%$ matrine aqueous at 720.36 g a.i./hm2 on quinoa plants (seeds, stems, and leaves) and soil satisfied the first-order kinetic equation [56]. Residues of matrine below 0.01 mg/kg were detected on roots after one, two and three applications. The residues of matrine on soil, quinoa seeds, leaves and stems were $83.58\%$, $88.55\%$, $86.37\%$ and $97.22\%$ on the fifth day after the first application; the residues of matrine on soil, quinoa seeds, leaves and stems were $78.39\%$, $83.44\%$, $86.25\%$ and $94.09\%$ on the fifth day after the second application; the residues of matrine on soil, quinoa seeds, leaves and stems were $76.63\%$, $69.08\%$ and $78.09\%$ on the fifth day after the third application. Matrine residues were detected on soil, quinoa seeds, leaves and stems at less than 0.01 mg/kg after one, two and three applications on the fourteenth day. The residual degradation of matrine with time was fitted with a first-order kinetic equation. Results are shown in Table 7. The half-lives of matrine on soil, quinoa seeds, leaves, and stems after one application were 0.97 d, 1.28 d, 1.03 d and 0.81 d, respectively; after two applications, the half-lives of matrine on soil, quinoa seeds, leaves and stems were 0.95 d, 1.29 d, 1.04 d and 0.82 d, respectively; after three applications, the half-lives of matrine on soil, quinoa seeds, leaves and stems were 0.93 d, 1.32 d, 1.21 d and 0.94 d. The results showed that matrine is a readily degradable pesticide. The degradation rates of matrine on quinoa plants and soil, fitted according to the first level kinetic equation, were stem > soil > leaf > seed after all three applications. ## 3.6.2. Terminal Residues of Matrine in Quinoa Plants and Soil Based on the above sample pretreatment and instrumental conditions residue analysis methods, the final residue test samples of one, two and three applications were analyzed and determined, with the results shown in Table 8. The residues of matrine decreased significantly with an increase in the sample interval at the doses of 360.18–720.36 g a.i./hm2 with the application times of one, two and three times. The residues of matrine in soil were 0.104–0.774 mg/kg at the last application interval of 3 d. At the interval of 7 d, the residues of matrine in soil were 0.013–0.029 mg/kg after the third application, but the residues of matrine were lower than 0.01 mg/kg at the remaining doses and application times. The residues were lower than 0.01 mg/kg at 14 d intervals. At 3 d, 7 d, and 14 d between the last application, the residues of matrine in quinoa seeds were 0.472–1.433, <0.01–0.106, and <0.01 mg/kg; the residues of matrine in quinoa leaves were 0.332–1.018, <0.01–0.039, and <0.01 mg/kg; the residues of matrine in quinoa stems were 0.152–0.858, <0.01–0.032, <0.01 mg/kg. Results showed that the residues of matrine in quinoa plants and soil increased with increasing application dose and application frequency and gradually decreased with the extension of the harvesting period. As a plant source pesticide, the dissipation rate of matrine in soil was greatly influenced by factors such as soil physicochemical conditions and environmental conditions of the test site. ## 3.7. PHI of Matrine and Risk Assessment To our knowledge, there are no published studies on the PHI of matrine. The deposition of matrine in quinoa seeds under three applications at 0 d was determined to be 1.574 mg/kg, 2.117 mg/kg and 3.079 mg/kg. To ensure the safe consumption of quinoa seeds, the MRL value (1 mg/kg) was based on the national standard for food safety on oranges and mandarins. Based on these results, the final residue levels of the target compounds in quinoa seeds harvested at PHI (0.84 d–2.14 d) were below the MRL established in China. These results also provide the necessary information for conducting a dietary risk assessment. The quinoa industry annual report shows that the average adult consumption is 0.1 kg/d, and GB 2763 [2021] specifies an ADI value of 0.1 mg/kg bw for matrine. This resulted in a risk index (RI) of $0.00168\%$ for matrine in quinoa. The results showed that the dietary risk of quinoa after treatment with matrine was in the acceptable range. ## 3.8.1. Evaluation of Sequencing Depth and Sequencing Results of Soil Samples A total of 54 samples from three soils (x for Haixi, b for Haibei, and d for Haidong) were measured in this experiment. A total of 22,234 optimized sequences were obtained by high-throughput sequencing, with an average effective read length of 250 bp. The classify-sklearn algorithm of QIIME2 was used to annotate species for each ASV using a pre-trained Naive Bayes classifier [57,58]. A total of 44 phylum, 115 class, 304 order, 501 family, 1121 genus and 714 species were obtained based on the annotated results of ASVs and the feature list of each sample. The number of randomly selected sequencing strips from a sample was used as the horizontal coordinate, and the number of species observed by the number of sequencing strips was used as the vertical coordinate to plot the dilution curve (Figure 6A), which was used to reflect the sequencing depth. Different samples were represented using different color curves. It was found that the number of ASVs increased gradually with the increasing number of sequences, and the individual curves gradually leveled off. This indicates that the amount of sequencing data is reasonable, and more data does not have a significant effect on the number of observed species. The species accumulation boxplot can be used to judge the adequacy of the sample size, with the horizontal coordinate as the sample size and the vertical coordinate as the number of ASVs after sampling. The species accumulation boxplot position tended to level off as the sample size increased (Figure 6B). Therefore, it is considered to reflect the community and structure of bacteria in each soil sample, and the sequencing depth and data volume are reasonable. Figure 7 shows the Venn diagrams of the distribution of bacterial ASVs in soil samples at 3 d and 10 d with the application of matrine at effective doses of 0, 0.1 and 5.0 mg/kg. The total number of ASVs between the blank soil sample (x.CK.3) and the two treatment groups (x.L.3, x.H.3) at 3 d of the matrine soil treatment (Figure 7A) was 1319. The number of unique bacterial ASVs in the soil samples treated with matrine at an effective dose of 0.1 mg/kg (x.L.3) was 1792. The number of unique bacterial ASVs in soil samples treated with an effective dose of 5.0 mg/kg (x.H.3) was 1,448. At 10 d (Figure 7B), the total number of ASVs between the control (x.CK.10) and treated groups (x.L.10, x.H.10) was 1507. The number of unique bacterial ASVs in the soil samples treated with 0.1 mg/kg (x.L.10) was 1451 and the number of unique bacterial ASVs in the soil samples treated with 5.0 mg/kg (x.H.10) was 1247. The total number of ASVs between the blank soil sample (b.CK.3) and the two treatment groups (b.L.3, b.H.3) was 1551 at 3 d of the Haibei soil treatment (Figure 7C). The number of unique bacterial ASVs in the soil samples treated with matrine at an effective dose of 0.1 mg/kg (b.L.3) was 1388. The number of unique bacterial ASVs in the soil samples treated with an effective dose of 5.0 mg/kg (b.H.3) was 1283. At 10 d (Figure 7D), the total number of ASVs between the control (b.CK.10) and treated groups (b.L.10, b.H.10) was 1394. The number of unique bacterial ASVs in the soil samples treated with 0.1 mg/kg (b.L.10) was 1375, and the number of unique bacterial ASVs in the soil samples treated with 5.0 mg/kg (b.H.10) was 1679. The total number of ASVs between the blank soil sample (d.CK.3) and the two treatment groups (d.L.3, d.H.3) at 3 d of the Haidong soil treatment (Figure 7E) was 1349. The number of unique bacterial ASVs in the soil samples treated with matrine at an effective dose of 0.1 mg/kg (d.L.3) was 2387. The number of unique bacterial ASVs in the soil samples treated with 5.0 mg/kg (d.H.3) was 1880. At 10 d (Figure 7F), the total number of ASVs between the control (d.CK.10) and treated groups (d.L.10, d.H.10) was 1447. The number of unique bacterial ASVs in the soil samples treated with 0.1 mg/kg (d.L.10) was 1969, and the number of unique bacterial ASVs in the soil samples treated with 5.0 mg/kg (d.H.10) was 1540. The results showed that an increase in the number of ASVs was significantly higher after the treatment with 0.1 mg/kg of matrine than after the treatment with 5.0 mg/kg of matrine. Similarly, the bacterial abundance was higher after 0.1 mg/kg of matrine than after 5.0 mg/kg of matrine. ## 3.8.2. Effect of Matrine on the Structure of Different Soil Bacterial Communities Figure 8 shows the structural composition of the bacterial community between the different treatments of the three soils at the phylum taxonomic level. The main phylum was Proteobacteria (26.45–$39.20\%$), Firmicutes (1.42–$1.78\%$), Actinobacteriota (14.09–$29.72\%$), Acidobacteriota (6.07–$14.94\%$), Bacteroidota (4.57–$9.24\%$), Crenarchaeota (1.54–$6.70\%$), Chloroflexi (4.70–$8.57\%$), Gemmatimonadota (3.62–$8.03\%$) and other bacterial phyla. The most superior phylum for each treatment was Proteobacteria, and the second most superior phylum was Actinobacteriota. The relative abundance inhibition of Proteobacteria was $11.56\%$ and $12.47\%$ at the effective dose of 0.1 mg/kg (x.L.3) and 5.0 mg/kg (x.H.3), respectively, on Haixi soil at 3 d. The relative abundance growth rate of Firmicutes was $81.93\%$ and $87.50\%$, and that of Bacteroidota was $43.83\%$ and $33.69\%$, respectively. At 10 d, the effective dose of 0.1 mg/kg (x.L.10) inhibited the relative abundance of Proteobacteria by $12.68\%$ and −$0.99\%$ and the relative abundance of Firmicutes by $0.17\%$ and $9.88\%$, respectively. The relative abundance inhibition rates of Firmicutes were $9.45\%$ and $25.84\%$ for the effective doses of 0.1 mg/kg (b.L.3) and 5.0 mg/kg (b.H.3) at 3 d in Haibei soil, respectively. The relative abundance growth rates of Actinobacteriota were $6.97\%$ and $18.83\%$, respectively. At 10 d, the effective doses of 0.1 mg/kg (b.L.10) and 5.0 mg/kg (b.H.10) inhibited the relative abundance of Firmicutes by $22.48\%$ and $9.51\%$, and Actinobacteriota by $29.70\%$ and $11.12\%$, respectively. The relative abundance inhibition rates of the suboptimal phylum Actinobacteriota were $10.20\%$ and $17.51\%$ for the effective doses of 0.1 mg/kg (d.L.3) and 5.0 mg/kg (d.H.3) on Haidong soil at 3 d, respectively. The relative abundance growth rates of the Firmicutes were $18.01\%$ and $18.47\%$, respectively. At 10 d, the effective doses of 0.1 mg/kg (d.L.10) and 5.0 mg/kg (d.H.10) inhibited the relative abundance growth rates of the Actinobacteriota by $4.52\%$ and $14.51\%$, respectively. The relative abundance inhibition rates were $40.56\%$ and $22.05\%$ for Gemmatimonadacea, respectively. The results showed that the inhibitory effect on some bacterial flora increased with the increase in matrine application. Meanwhile, the inhibitory effect on bacterial flora was weakened with time. Figure 9 shows the structural composition of the bacterial community among the different treatments of the three soils at the generic taxonomic level. The main genera were Skermanella (3.05–$15.45\%$), Lactobacillus (0.00–$3.49\%$), Aminobacter (0.15–$0.78\%$), Ralstonia (0.02–$2.73\%$), Candidatus_Nitrocosmicus (0.23–$3.07\%$), Clostridia_UCG-014 (0.00–$2.00\%$), Pseudarthrobacter (1.12–$4.09\%$), Sphingomonas (0.95–$4.35\%$), Nocardioides (0.68–$1.85\%$), Bacillus (0.32–$2.69\%$), and a variety of unnamed bacteria. Among the bacterial genera observed and identified, the dominant species group in the soils of Haixi, Haibei and Haidong was Skermanella. The relative abundance of Aminobacter and Sphingomonas in the Haixi control soil (x.CK.3) were both $0.18\%$ and $2.50\%$. At 10 d (x.CK.10), their relative abundances were $3.04\%$ and $1.18\%$. The effective doses of 0.1 mg/kg (x.L.3) and 5.0 mg/kg (x.H.3) reduced the relative abundance of Skermanella by $0.72\%$ and $4.88\%$ at 3 d in Haixi soil. The relative abundance of Lactobacillus increased by $1.61\%$ and $3.49\%$. At 10 d, the effective doses of 0.1 mg/kg (x.L.10) and 5.0 mg/kg (x.H.10) reduced the relative abundance of Skermanella by $4.22\%$ and $5.94\%$, respectively. The relative abundance of Ralstonia in the control Haibei soil (b.CK.3) was $0.12\%$. At 10 d (b.CK.10), its relative abundance was $1.15\%$. The relative abundance of Lactobacillus was reduced by $0.26\%$ at the effective dose of 0.1 mg/kg (b.L.3) and increased by $0.29\%$ at the effective dose of 5.0 mg/kg (b.H.3) in Haibei soil at 3 d. At 10 d, the effective doses of 0.1 mg/kg (b.L.10) and 5.0 mg/kg (b.H.10) increased the relative abundance of Lactobacillus by $0.92\%$ and $0.28\%$. The relative abundance of Pseudarthrobacter in the control Haidong soil (d.CK.3) was $2.26\%$. At 10 d (d.CK.10), its relative abundance was $0.11\%$. The relative abundance of Pseudarthrobacter was reduced by $0.79\%$ and $0.70\%$ at the effective dose of 0.1 mg/kg (d.L.3) and 5.0 mg/kg (d.H.3) on Haidong soil at 3 d. At 10 d, the effective doses of 0.1 mg/kg (d.L.10) and 5.0 mg/kg (d.H.10) increased the relative abundance of Pseudarthrobacter by $0.23\%$ and $1.01\%$, respectively. The results showed that applying a certain dose of matrine could change the relative abundance of dominant genera of soil bacteria. ## 4. Discussion Compared to the use of solid phase extraction (SPE) [9], microwave-assisted extraction (MAE) [59], supercritical fluid extraction (SFE) [60] and molecular Imprinting Technique (MIT) [61], the QuEChERS method is fast, efficient, time-saving and simple to use, eliminating the need for complex clean-up processes as well as reducing pretreatment time and the use of organic solvents. Compared to the application of thin layer scanning [26,62], high-performance liquid chromatography [63,64] and capillary electrophoresis [65] methods for the determination of matrine bases, the LC-MS/MS method has high detection sensitivity and accuracy and low detection limits, thus, ensuring rapid and accurate analysis results of the samples. Matrine has also been found to be a readily degradable pesticide in several studies. In wheat field soil, cabbage field soil and apple orchard soil, matrine degraded quickly with a half-life of 3–6 d [18]. The degradation half-lives of matrine were 5.18–6.70 d (tomato) and 7.45–8.08 d (soil) in an open field and greenhouse cultivation [66]. The half-life of matrine in soil and tobacco leaves was 4.2–4.6 d [67]. In cucumber and soil, the half-lives of matrine were 5.19–7.42 d and 6.70–9.18 d, respectively [16]. Dissipation studies of pesticides in soil are necessary. The physicochemical properties of soil are important factors influencing their degradation. L. Kaur et al. [ 68] found that imazethapyr had the fastest dissipation rate in alkaline soils (pH = 8.0–8.8), followed by neutral soils (pH = 7.4) and acidic soils (pH = 5.0). The test soils for this experiment were located in the plateau region of the Qinghai Province (102°19′ E and 36°34′ N), and their soil physicochemical properties are shown in Table 1. The digestion half-life of matrine in the soil (pH = 8.16) was 0.97 d, while Qiu [9] found that in the test soil (pH = 7.91), the digestion half-life of matrine was 1.45 d. Due to its unique geographical conditions, Qinghai Province has high altitudes and alkaline soils. The pH was the main factor affecting the degradation of matrine. The inhibitory effect on some bacterial communities was enhanced with an increase in matrine application, while the inhibitory effect on bacterial communities was weakened with time. Lu et al. [ 69] found that microbial diversity was negatively correlated with the amount of pesticide residues in soil samples. The relative abundance of different bacteria in the soil samples varied. Firmicutes, Chloroflexi, Actinobacteria, Cyanobacteria and Armatimonadetes were resistant to pesticide toxicity and had the potential to use pesticide residues as nutrients for growth. In contrast, Proteobacteria, Acidobacteria, Nitrospirae, Latescibacteria, Gemmatimonadetes, Verrucomicrobia and Chlorobi are sensitive to pesticide residues. They have the potential to be used as indicators for assessing pesticide residue levels. Fernandes A et al. [ 70] found an increase in the abundance of Enterobacteriaceae and Burkholderiaceae at week 4 after the application of atrazine. The abundance of Conexibacteraceae, Solirubrobacteraceae and Gaiellaceae also increased at week 8. At 12 weeks after the application of atrazine, the bacterial community in the soil consisted mainly of members of the Proteobacteria and Actinobacteria families. ## 5. Conclusions An analytical method was developed for the determination of matrine residues in quinoa plants and soil by LC-MS/MS with the QuEChERS technique for the clean-up of quinoa plants and soil. The method is simple, sensitive, accurate, reliable and widely applicable and can achieve rapid multi-residue determination of matrine to a certain extent. The half-life of matrine in quinoa seeds, leaves, stems and soil was less than 30 d. It is a readily dissipative pesticide. The final residue levels of the target compounds in quinoa seeds harvested at PHI (0.84 d–2.14 d) were all below the Chinese MRLs. The risk index RI for matrine in quinoa was $0.00057\%$, which is within the acceptable dietary risk range. Next Generation Sequencing was used to identify the bacterial communities and composition of the soils at the three sites after high and low doses of matrine treatment. The bacterial communities and composition of the soils in different areas differed due to environmental factors. The bacterial community and composition also differed in the same area after treatment with high and low different doses of matrine. The results showed that the inhibitory effect on some bacterial flora groups increased with matrine application. At the same time, the inhibitory effect on bacterial groups diminished with time. The application of certain doses of matrine was able to change the relative abundance of dominant bacterial genera in soil bacteria. These findings provide a primary basis for understanding the structure and composition of soil bacterial communities in the Haixi, Haibei and Haidong areas of the Qinghai Province with the application of high and low doses of matrine. ## References 1. 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--- title: Variations in BCO2 Coding Sequence Causing a Difference in Carotenoid Concentration in the Skin of Chinese Indigenous Chicken authors: - Yan Wang - Shiyi Gan - Chenglong Luo - Sijia Liu - Jie Ma - Wei Luo - Chuxiao Lin - Dingming Shu - Hao Qu journal: Genes year: 2023 pmcid: PMC10048632 doi: 10.3390/genes14030671 license: CC BY 4.0 --- # Variations in BCO2 Coding Sequence Causing a Difference in Carotenoid Concentration in the Skin of Chinese Indigenous Chicken ## Abstract ### Simple Summary The deposition of carotenoids in chicken skin makes the skin color turn from white into yellow. The enzyme β-carotene oxygenase 2 (BCO2) plays a key role during the degradation process of carotenoids in chicken skin. Hence, the concentration of carotenoids in chicken skin was measured, and significant differences in BCO2 gene expression in the back skin between yellow and white skin and one SNP c.890A>G in BCO2 were found to be potentially associated with the chicken skin color. The results of this study showed that the c.890A>G may be used as a genetic marker in breeding for yellow skin in Chinese indigenous chicken. ### Abstract Carotenoid consumption decreases the risk of cancer, osteoporosis, or neurodegenerative diseases through interrupting the formation of free radicals. The deposition of carotenoids in chicken skin makes the skin color turn from white into yellow. The enzyme β-carotene oxygenase 2 (BCO2) plays a key role during the degradation process of carotenoids in skin. How the BCO2 affects the skin color of the chicken and whether it is the key factor that results in the phenotypic difference between yellow- and white-skin chickens are still unclear. In this research, the measurement of the concentration of carotenoids in chicken skin by HPLC showed that the carotenoid concentration in chickens with a yellow skin was significantly higher than that in white-skin chickens. Moreover, there were significant differences in BCO2 gene expression in the back skin between yellow- and white-skin chickens. Scanning the SNPs in BCO2 gene revealed a G/A mutation in exon 6 of the BCO2 gene in white and yellow skin chicken. Generally, one SNP c.890A>G was found to be associated with the chicken skin color and may be used as a genetic marker in breeding for yellow skin in Chinese indigenous chickens. ## 1. Introduction Carotenoids are natural pigments that are synthesized by plants, algae, and some bacteria and fungi and are an important source of vitamin A for livestock [1]. In vivo, besides acting as pigments, carotenoids also have other biology functions; they can, for instance, function as antioxidants that have an important role in protecting DNA from oxidative damage [2]. Oxidative stress has been recognized as the main contributor to the age-related diseases such as atherosclerosis, osteoporosis, obesity, dementia, diabetes, cancer, and arthritis [3,4,5]. Carotenoid consumption is associated with a lower risk to develop these diseases by interrupting the propagation of free radicals [6,7,8]. Animals, however, cannot synthesize carotenoids de novo; they intake carotenoid from their food, with a way of absorbing in intestine and transporting in plasma exclusively by lipoproteins to various organs, such as skin. [ 9,10]. Carotenoids are therefore an important component in a well-balanced human diet. Similarly, carotenoids are included in the feed of production animals in a species-specific way to support animal health and achieve a high performance [11,12]. In poultry, feed high in carotenoid content leads to carotenoid-pigmented egg yolk, skin, legs, beak, comb, and feathers of some breeds. The skin color of chickens in some countries, such as China, is an important trait, influencing the choice of consumers in their decision to buy a chicken or not [13]. The skin color of chickens may differ depending on how much of the carotenoid pigment is deposited in the skin. Consumers think that skin pigmentation is an important health indicator in poultry, as weak health greatly reduces the absorption of carotenoids; thus, poultry farmers add carotenoids to chicken feed to achieve the desired color to satisfy customers’ preference. For the marketing of poultry products, in a number of countries in the world, chicken skin color is a trait that influences consumer behavior. A bright yellow skin color is often associated with freshness and health and has become an indicator of high-quality products. A uniform and good pigmentation generally means good health and good practical hygienic conditions [14]. In chickens, the capacity to accumulate carotenoids in skin is, however, mostly genetically determined [15]. The accumulation of carotenoids gives the skin a yellow color, while the enzymatic degradation of carotenoids or the absence of carotenoid absorption gives the skin a white appearance. There are two enzymes involved in carotenoid metabolism, namely β-carotene 15, 15′-monooxygenase 1 (BCMO1) and β-carotene oxygenase 2 (BCO2). BCMO1 is responsible for the oxidative cleavage of β-carotene into two retinal molecules [4,5], while BCO2 can mediate the degradation of carotenoid into colorless substances [6,7,8,9]. The expression of these enzymes is tissue dependent. Recently, research has demonstrated that BCOM1 and BCO2 work together in the gut and liver in carotenoid degradation, while other tissues, e.g., adipose tissue, depend on BCO2 for carotenoid metabolism [9,16]. Several previous studies have revealed that point mutations in the BCO2 gene can influence the deposition of carotenoids in the skin, thus influencing skin color. For example, a nonsense mutation (c.196C>T) leaving the BCO2 gene inactive is associated with the accumulation of carotenoids in adipose tissue in sheep [9]. Furthermore, mutations in BCO2 have been shown to affect the concentration of carotenoids in bovine milk [17]. In chickens, three SNPs, namely G>A, A>G, and G>A (chr24: 6,264,085 bp, 6,273,428 bp and 6,287,900 bp, WUGSC 2.1/galGal3), in the BCO2 gene are associated with a yellow skin trait [15]. We also tested those SNPs in five Chinese indigenous chicken breeds, and the results showed that some breeds carried alleles at this locus that are not so closely related to white-skin or yellow-skin alleles in our domestic breeds. Some research studies have shown that the genetic structure is different between Western breeds and Chinese local chicken breeds; by whole-genome re-sequencing 126 chicken individuals, combined with the genome sequencing data of 31 chickens that were previously published, Luo et al. observed the highest levels of genome-wide genetic differentiation between each commercial population (White Recessive Rock chicken, Ross308 chicken, Rhode Island Red chicken, and White Leghorn chicken) and Chinese indigenous chickens, especially between White Leghorn chickens and Huiyang Bearded chickens [18,19,20]. The BCO2 and its flanking genes (IL18 and PTS) are also consistent as candidate positively selected genes for the yellow pigmentation phenotype by the analysis of genome-wide scans for signals of selection [21]. Above all, BCO2 is a strong candidate gene to affect the deposition of carotenoids. The aim of this investigation was to study differences in BCO2 expression between yellow-skin and white-skin chickens. This will help us understand the molecular mechanisms involved in skin carotenoid metabolism and provide a valuable theoretical basis for the selection of the skin trait during the selective breeding of Chinese indigenous chickens. ## 2.1. Ethics Statement Our study was approved by the Animal Care Committee of the Institute of Animal Science, Guangdong Academy of Agricultural Sciences (Guangzhou, China), with approval number GAAS-IAS-2009-73. All birds were housed in individual battery cages with ad libitum access to food and water and humanely euthanized. ## 2.2. Samples Two breeds of chicken were used. The Guangxi Huang (S4, with yellow skin) chickens were bred by the Institute of Animal Science, Guangdong Academy of Agricultural Sciences (Guangzhou, China). The Qingjiao Ma (Q, with white skin) chickens were bred by the Sichuan Agricultural University (Ya’an, China), which kindly provided the samples used in this study. All birds had free access to feed and water, used the same feed, and no additional carotenoids were added to the feed. Screening for polymorphisms in chicken BCO2 gene was performed in a total of 60 birds of the Guangxi Huang and Qingjiao Ma chickens, 30 birds in each breed. A total of 1054 DNA samples from eight different chicken breeds collected at the Institute of Animal Science, Guangdong Academy of Agricultural Sciences, were genotyped and determined allele frequencies: Huiyang Beard, Mahuang with black shank, Mahuang with navy shank, Youxi Ma, Qingjiao Ma, Fast-Growing Lingnanhuang Line A, and Guangxi Huang Chicken. These breeds or lines have all been kept by us or the Sichuan Agricultural University (Ya’an, China) for over 15 years (inbred for at least 15 generations), without interbreeding with any other breeds/lines. At the age of 70 days, blood samples were collected in 1.5 mL tubes with $1.5\%$ EDTA, stored at −20 °C, and then used for DNA isolation. Birds were humanely euthanized, and tissue samples, including heart, liver, spleen, lungs, kidney, muscular stomach, glandular stomach, intestine, breast muscle, ovary, cerebrum, cerebellum, hypothalamus, hypophysis, and back skin, were collected. Tissue samples were collected for RNA isolation and were put in 1.5 mL tubes with RNA later (Sigma-Aldrich, St. Louis, MO, USA) and stored at −20 °C. Genomic DNA was isolated from blood samples by using standard phenol and phenol/chloroform-purification-based protocols. ## 2.3. Measurement of Carotenoid Concentration in Chicken Skin The measurement of the carotenoid concentration in chicken skin at the age of 70 days was performed in a total of 6 females of the Guangxi Huang and Qingjiao Ma chickens, 3 birds in each breed. Back skin tissue (0.5 g) was added to 5 mL mixture reagent (CHCL3:CH3OH = 2:1) in a 50 mL tube and homogenized. Then 5 mL mixture reagent was added to the homogenate, followed by 2 mL $0.9\%$ (8.5 g/L) NaCl, after which the tube was vortexed for 2 min. Samples were centrifuge at 648 g for 10 min at 4 °C. After centrifugation, the CHCL3 layer was transferred to a new tube, while the water layer was transferred to another tube with 5 mL hexane, vortexed for 2 min, and centrifuged again at 648 g for 10 min at 4 °C. From this tube, the hexane layer was removed and added to the tube with the CHCL3 phase [22,23]. The content of the tube was put in a water bath at 50 °C and was dried by nitrogen, using a Rotary Evaporator (RE-3000A, Shanghai Yarong Biochemistry Instrument factory, Shanghai, China); the formed pellet was dissolved in 0.15~5 mL ethanol, after which 50 μL of the ethanol samples was analyzed using High-Performance Liquid Chromatography (LC-20AD, SHIMAZU Inc., Kyoto, Japan). Lutein and zeaxanthin were used as the internal standard (Guangzhou Juyuan Biochemical Co., Guangzhou, China) and measured at a wavelength of 445 nm and 451 nm, respectively. Differential carotenoid concentrations in chicken skin between the Guangxi Huang and Qingjiao Ma breeds were determined using a t-test with SAS 8.0 software (SAS Institute, Cary, NC, USA). ## 2.4. Primers Primer pairs were designed for 18 fragments (F1/R1-F18/R18), together responsible for amplification of the complete CDS of the BCO2 gene. Primers for PCR-RFLP: YSD-F/R, primers for RT-PCR: BCO2-F/R, and the housekeeping genes GAPDH-F/R and β-actin-F/R. Three primers, namely BCO2-A-F/R, BCO2-B-F/R, and BCO2-C-F/R, were also designed to identify the SNPs (SNP A, SNP B and SNP C), which were from before research [15]. All primers were synthesized by Sangon Biotech (Shanghai, China) Co., Ltd., Shanghai, China. The primer sequences are shown in Table 1. ## 2.5. SNP Scanning and Genotyping The 18 fragments were amplified by PCR, using DNA as the template. To check whether the correct products were amplified, the PCR products were sequenced (Sangon Biotech (Shanghai) Co., Shanghai, China). PCR reaction amplification was performed in 25 μL reaction volume on a GeneAmp PCR system 9600 (Perkin Elmer, Foster City, CA, USA). Mixes comprised 100 ng chicken genomic DNA as a template, 1 μM of each primer, 1× PCR reaction mix, and 1 U Taq DNA polymerase (Dongsheng, Guangzhou, China). The PCR amplifications were performed with the following cycling parameters: initial denaturalization at 94 °C for 4 min, 35 cycles of 94 °C for 45 s, annealing at optimal temperature for 45 s, and 72 °C for 1 min. A final extension was performed at 72 °C for 10 min. All amplified products were separated on agarose gels and purified using a Gel Extraction Kit (Sangon). The purified PCR products were cloned into the pMD19-T vector (Takara) and sequenced in both directions. Potential polymorphic sites were analyzed by sequence comparison, using DNAstar software (DNAstar Inc., Madison, WI, USA). The sequences were aligned between the Guangxi Huang and Qingjiao Ma chickens to identify the SNP site. A 444 bp fragment was amplified using specific primer pairs (YSD-F/R, Table 1). For genotyping by PCR-RFLP assays, 7 μL of PCR products were digested with 4 U SduI (ThermoScientific, Shanghai, China) in a 1 × digestion buffer, in a total volume of 10 μL, following digestion for 6 h at 37 °C. Then the digested products were separated by electrophoresis on a $2.0\%$ agarose gels. Three SNPs (SNP A, SNP B, and SNP C) were identified by PCR; we used primers BCO2-A-F/R, BCO2-B-F/R, and BCO2-C-F/R; and the PCR products were sequenced (Sangon Biotech (Shanghai) Co., Shanghai, China). ## 2.6. Real-Time PCR Three female chickens, 70 days old, of the Guangxi Huang and Qingjiao Ma breed (respectively) were used for this analysis. Fifteen tissues, namely heart, liver, spleen, lungs, kidney, gizzard (muscle part stomach), glandular stomach, intestine, breast muscle, ovary, cerebrum, cerebellum, hypothalamus, hypophysis, and back skin, were collected. The back skin tissues from three female chickens of Guangxi Huang and Qingjiao Ma breed, respectively, at 2 d, 21 d, and 42 d were also collected and transferred into RNAlater solution (Life Technologies, Carlsbad, CA, USA) and stored at −20 °C. Tissues were weighed (approximately 0.2 g of tissue was used), grounded into a powder in liquid nitrogen, and homogenized in 2 mL of TRIzol (Life Technologies, Rockville, MD USA), using a handheld electric homogenate instrument (Germany, Staufen, IKA). The homogenized samples were left for 5 min at room temperature and then centrifuged at 12,000 rpm (representative g value) at 4 °C for 10 min. Total RNA was isolated using the RNAiso Plus kit (Takara Bio Inc., Dalian, China) according to the manufacturer’s instructions. The amount and quality of the samples was estimated using the NanoDrop ND-1000 spectrophotometer (ThermoScientific, MA, USA) and agarose gel electrophoresis (Bio-Rad Laboratories, SYSTEM GelDoc XR+, Hercules, CA, USA). First-strand cDNA synthesis and reverse transcriptase PCR were performed as described in the instructions for the PrimeScriptTM II reagent Kit with gDNA Eraser (TaKara, Dalian, China). Primer pairs BCO2-F/BCO2-R (Table 1) were used to determine the relative expression values of the BCO2 gene by qPCR. The PCR amplifications were performed in 20 μL reaction volumes comprising 0.5 μL of chicken cDNA, 0.5 μL of each primer, and 10 μL of SYBR Green Real-time PCR Master Mix (TOYOBO, Tokyo, Japan) in a LightCycler 480 Real-Time PCR System (Roche Applied Science, Indianapolis, IN, USA). The PCR conditions were 95 °C for 1 min, followed by 40 cycles of 95 °C for 15 s, 58 °C for 15 s, and 72 °C for 20 s. The level of fluorescence was used to calculate the threshold cycle (Ct) value for each sample. The relative gene-expression levels were analyzed using the comparative Ct method, in which the housekeeping genes β-actin and GAPDH (Table 1) were used as internal controls, and the geometric averaging of those two internal control genes was calculated according to a previously published method [24]. The geometric mean of 2 reference genes according to the ΔCT [16] 2−ΔCT method were used to calculate the expression level [25]. Expression abundances of the BCO2 gene in 15 tissue and back-skin samples from four different times, between the Guangxi Huang and Qingjiao Ma breeds, were determined and analyzed using a t-test with SAS 8.0 software (SAS Institute, Cary, NC, USA); differences showing $p \leq 0.05$ were considered significant. ## 3.1. Carotenoid Concentration in Chicken Skin and Difference of BCO2 Expression Level in Tissues The carotenoid concentration in chicken skin was determined by HPLC. No β-carotene was detected, but the lutein and zeaxanthin content of yellow-skin chicken-S4 were significantly ($p \leq 0.05$) higher than that of white-skin chicken-Q (Figure 1). BCO2 was expressed in all tissues of Qingjiao Ma chickens. BCO2 had a lower expression level in the heart, intestine, back skin, breast muscle, lungs and ovary in the Guangxi Huang (S4) compared with those in Qingjiao Ma (Q). However, only in the back skin was the average relative abundance of BCO2 mRNA of the Guangxi Huang chickens significantly lower than that of the Qingjiao Ma ($p \leq 0.01$). The expression levels of BCO2 were much higher in the hypophysis in both breeds compared with those in the other tissues (Figure 2). As shown in Figure 3, BCO2 was expressed in the back skin of Qingjiao Ma chickens at the age of 2, 21, 42, and 70 days, but the mRNA expression was very low, only slightly greater than zero, in the Guangxi Huang chickens. The mRNA expression of BCO2 in the back skin of Qingjiao Ma chickens was significantly higher than that in Guangxi Huang chickens at the age of 21, 42, and 70 days ($p \leq 0.05$). ## 3.2. Single Nucleotide Polymorphism Scanning in BCO2 Coding Sequence Scanning for SNPs in the BCO2 CDS gene by TA-cloning and sequencing did not lead to previously described SNPs for this gene. However, a G/A mutation was found in exon 6 (c.890A>G, GenBank accession No. XM_417929.6). The 444 bp PCR amplicon containing the SNP was detected by digestion with SduI, resulting in allele G (263 + 181 bp) and allele A (444 bp) (Figure 4). The Qingjiao Ma breed had two genotypes, i.e., AA and AG, whereas the Guangxi Huang breed was only homozygous for the GG genotype. We also tested three SNPs, namely G>A, A>G, and G>A (chr24: 6,264,085 bp, 6,273,428 bp, and 6,287,900 bp, WUGSC 2.1/galGal3), in five Chinese indigenous chicken breeds; the results showed that some breeds carried alleles at this locus that are not so closely related to white-skin or yellow-skin alleles in our domestic breeds, such as Silkies, Fast-Growing Lingnanhuang Line A, and Guangxi Huang chickens. In contrast, the SNP c.890A>G screen showed that all five tested breeds carried alleles at this locus that are closely related to white-skin or yellow-skin alleles in Chinese domestic chicken (Table 2). This result was confirmed by PCR-RFLP in other Chinese indigenous chicken breeds (Table 3). The allelic frequencies of the SNP c.890A>G in white-skin and yellow-skin chicken breeds were notably different. As shown in Table 3, allele A was predominant in those breeds with a white skin, such as Qingjiao Ma, Youxi Ma, and Mahuang with black shank. Allele G was predominant in yellow skin breeds, including Huiyang Beard, Fast-Growing Lingnanhuang Line A, Mahuang with navy shank, and Guangxi Huang Chicken. ## 4. Discussion Carotenoids are an important factor for the growth and health of birds and their color, and therefore carotenoid additives and vitamin A supplements may be added to poultry feed formulations. As an alternative for this, some studies have used established high-carotenoid maize for these supplements in laying hens and broilers [26,27,28]. Our results show that carotenoids are deposited in the skin of Guangxi Huang chicken with yellow skin, while in white-skin chickens, there is a lower deposition of carotenoids, as no β-carotene was detected, while the lutein and zeaxanthin were detected to be significantly lower in Qingjiao Ma chickens than that of yellow-skin chickens. In chickens, the BCO2 gene is a classic yellow-color gene, and in line with these observations, there was almost no expression of the BCO2 gene in the skin of the Guangxi Huang (yellow skin) breed, in contrast to the Qingjiao Ma (white skin) breed. The absence of BCO2 expression in the skin of the Guangxi Huang breed was specific for the skin, as no significant differences in BCO2 gene expression were observed among the two breeds in any of the other tissues investigated. Because BCO2 cleaves orange/yellow carotenoids into colorless apocarotenoids, it is concluded that BCO2 is the gene that regulates the deposition of carotenoids in chicken skin. However, adding more color additives to poultry diets does not induce a change from white skin to yellow skin color, indicating that the coloring of the chicken skin is more complex. Furthermore, the presence of zeaxanthin in the feed might interfere with the absorption of b-carotene, as chickens fed on diets with low levels of zeaxanthin accumulated higher levels of retinol in the liver [29]. Scanning for the presence of skin color related SNPs (G>A chr24: 6,264,085 bp, A>G chr24: 6,273,428 bp and G>A chr24: 6,287,900 bp, WUGSC 2.1/galGal3) between yellow-skin and white-skin chicken did not lead to the identification of previously reported SNPs [15]. An explanation for this discrepancy may be related to differences in the chicken breeds used in the two studies. Instead, we found a new SNP in the BCO2 gene, which, when tested in other Chinese indigenous chicken breeds, may be used as a genetic marker associated with skin color. The G/A mutation found in exon 6 of BCO2 (c.890A>G, GenBank accession No. XM_417929.6) was a same-sense mutation and did not directly change the amino acids of protein, but they were not entirely negligible either, as they affected BCO2 gene expression. Silkie breed displaying Fibromelanosis (FM), which is characterized by intense pigmentation of the dermal layer of skin across the entire body, results in a dark blue appearance when viewed through the clear epidermis [30], and when there are carotenoids depositing in the epidermis, this results in a yellow-to-dark-green appearance. Yellow pigmentation in the epidermis is determined by the W locus, at which the W allele inhibits epidermal xanthophyll pigmentation and is completely dominant to w [31,32]. We found 9 out of 30 birds have yellow-to-dark-green shank color and are different with other dark blue shanks, and the results of the SNP c.890A>G polymorphisms were consistent with this phenotype. However, although the BCO2 may determine whether the skin is yellow or white, it cannot determine the yellowness value of skin [33]. Several reports indicate that copy-number variations are highly associated with specific phenotypes. For example, CNVs in intron 1 of the SOX5 gene cause the Pea-comb phenotype in chickens [34], while two duplications containing EDN3 result in an abnormal skin pigmentation phenotype in the silkie breed [30]. CNVs in the testis-specific Y-encoded protein (TSPY) gene have been linked to prostate cancer and human male infertility [35,36,37]. CNVs in the stem cell factor receptor (KIT) gene are linked to porcine white coat color [38], while CNVs in the agouti signaling protein (ASIP) gene influence the coat color of goat breeds [39]. We also detected the CNVs in BOC2, with the copy number in the Qingjiao Ma (white skin chicken) being almost two-fold larger than in the Guangxi Huang (yellow skin chicken) breed, suggesting that a lower CNV may lead to the downregulation of BCO2 expression. However, this only indicated the difference in CNVs in the BCO2 gene based on the TaqMan real-time PCR; we would give the information of gene structure with these CNVs by the re-sequencing and validation. Despite our observations, one has to keep in mind that SNPs may not be the only cause of differences in BCO2 gene expression between the Qingjiao Ma and Guangxi Huang chicken breeds. There may be other factors that can be responsible for the reduced expression of the BCO2 gene expression in the Guangxi Huang chicken breed, such as DNA methylation [40,41,42], histone modification [43,44,45], and UTR function and, thus, translational modification [46]. Additional experiments need to be performed to elucidate the specific difference in BCO2 expression in the skin of Qingjiao Ma and Guangxi Huang chicken breeds without differences in gene expression in any other tissue. ## 5. Conclusions In conclusion, the reduced expression of the BCO2 gene may be responsible for the deposition of carotenoids and, as a consequence, a yellow skin color in Guangxi Huang chickens. One SNP c.890A>G was found to be associated with the chicken skin color and may possibly, in the future, be used as a genetic marker in breeding for yellow skin in Chinese indigenous chickens. ## References 1. Maoka T.. Carotenoids as natural functional pigments. J. Nat. Med. (2020) 74 1-16. DOI: 10.1007/s11418-019-01364-x 2. Krinsky N.I.. Carotenoids as antioxidants. Nutrition (2001) 17 815-817. DOI: 10.1016/S0899-9007(01)00651-7 3. Giudetti A.M., Salzet M., Cassano T.. 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--- title: Trust in and Use of COVID-19 Information Sources Differs by Health Literacy among College Students authors: - Xuewei Chen - Darcy Jones McMaughan - Ming Li - Gary L. Kreps - Jati Ariati - Ho Han - Kelley E. Rhoads - Carlos C. Mahaffey - Bridget M. Miller journal: Healthcare year: 2023 pmcid: PMC10048640 doi: 10.3390/healthcare11060831 license: CC BY 4.0 --- # Trust in and Use of COVID-19 Information Sources Differs by Health Literacy among College Students ## Abstract People’s health information-seeking behaviors differ by their health literacy levels. This study assessed the relationship between health literacy and college students’ levels of trust in and use of a range of health information sources of COVID-19. We collected data from August to December 2020 among college students ($$n = 763$$) through an online survey. We used a health literacy measure containing three self-reported survey questions, developed by the CDC. We assessed the extent to which participants trusted and used any of the sixteen different sources of information about COVID-19. Respondents reported high levels of trusting and using COVID-19 information from the CDC, health care providers, the WHO, state/county/city health departments, and official government websites when compared to other sources. After controlling for demographic characteristics (i.e., gender, age, race, ethnicity, and income), those who reported having lower health literacy were significantly less likely to trust and use COVID-19 information from these health authorities when compared to participants who reported having higher health literacy. Students with lower self-reported health literacy indicated not trusting or using official health authority sources for COVID-19 information. Relying on low-quality information sources could create and reinforce people’s misperceptions regarding the virus, leading to low compliance with COVID-19-related public health measures and poor health outcomes. ## 1. Introduction The regular and comprehensive communication of public health information is an important step in containing the spread of COVID-19, protecting people at risk of serious complications or death, and reducing the burden on the health system [1]. Diffusion of knowledge, especially about the COVID-19 virus, is also critical to promoting social, political, and economic development [2]. The COVID-19 pandemic created an infodemic [3], where a vast amount of information, including misinformation and disinformation, spread rapidly and impeded effective crisis management [4,5]. Misinformation refers to false information that is created and spread, regardless of an intent to harm or deceive, and disinformation refers to false information intended to be deliberately deceptive [6]. The vast amounts of COVID-19 information led to information overload, which includes the presence of unpleasant emotions (e.g., feeling overwhelmed), a reluctance to follow suggested health behaviors (e.g., physical distancing and wearing a mask), and information avoidance [5,7]. For example, when people experience emotional strain (e.g., confusion, frustration, fear) by what they characterize as excessive, inconsistent, conflictive, or inaccurate COVID-19 information, they may cope by setting boundaries, limiting the amount of information they are exposed to (i.e., not seeking information every day and not reading every piece of information), and limiting the sources of information they attend to [8]. Individuals with lower health literacy face greater challenges in evaluating the quality of health information and differentiating misinformation/disinformation from accurate information [6,9]. According to Healthy People 2030, personal health literacy is “the degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others” [10,11]. Those with low levels of health literacy may hold misconceptions, such as assuming, incorrectly, that farmers have strong immune systems, with low chances of contracting COVID-19; these misconceptions might lead to a lack of preventative behaviors that protect against the virus [8]. A recent study conducted among undergraduate students majoring in healthcare in South Korea reported that e-health literacy (skills in obtaining and comprehending online health information) was positively associated with COVID-19 preventive behaviors [12]. Therefore, identifying the sources that are preferred by individuals with low health literacy for health information related to COVID-19 can provide evidence-based guidance for public health professionals, in order to identify the best sources for disseminating high quality and easy-to-understand COVID-19 information to different populations. These sources can be used to reduce uncertainty, decrease knowledge gaps, and, therefore, diminish health disparities among individuals with low health literacy. People’s health information-seeking behaviors differ by their health literacy levels [6,9]. A previous study found that people with low health literacy are less likely to use and trust general health information from sources of authority (e.g., medical websites and health professionals), but are more likely to use and trust health information from social media (e.g., YouTube and celebrity blogs), which often contains inaccurate information [13]. However, individuals may use different strategies to seek information, depending on the specific health topics [14]. For example, the Health Information National Trends Survey (HINTS) data have shown that people in the U.S. seek cancer-related health information primarily from the Internet [15,16], people in Germany primarily seek cancer-related health information from their health care providers [17], and people in China primarily use the television to seek cancer information [18]. Another recent study found that people in New York City use a variety of sources to access information about dietary supplements, including the Internet, product packaging, books, friends, pharmacists, dietitians/nutritionists, and family members [19]. These findings suggest that focusing on specific groups of information seekers, health literacy levels, and predefined health topics will provide the opportunity for an in-depth analysis and comparison of health-information-seeking behaviors by a specific audience about COVID-19. Thus, we focused, in this study, on examining as to how U.S.-based college students with lower levels of health literacy seek health information about COVID-19. This study will contribute to the field because the literature shows that low health literacy skills can influence one’s exposure to misinformation and disinformation related to COVID-19, leading to information avoidance [8,9], which might accelerate uncertainty and create difficulty in making appropriate decisions about COVID-19-preventive behaviors [6]. Thus, the purpose of this study was to examine the relationship between health literacy and people’s trust, and to use of a range of potential health information sources for COVID-19 among college students. We proposed the following research questions: What sources were highly trusted and frequently used by our participants for COVID-19 information? How does health literacy play a role in our participants’ trust in and use of a range of potential health information sources for COVID-19? ## 2.1. Procedures and Participants Data for the present analyses were derived from a larger cross-sectional online survey study, designed to investigate college students’ experiences during the COVID-19 pandemic [20]. Recruitment and data collection, using Qualtrics, were conducted from August 2020 to December 2020. The larger study was approved by Oklahoma State University Institutional Review Board. Detailed procedures are reported by McMaughan et al. [ 20]. Of the 849 students who submitted survey responses, 72 were dropped for straight line, missing, speedy or fake responses. An additional 14 were dropped due to failing the validation item associated with the trust in sources section, resulting in a final sample of 763 participants in our data analyses for this current study. ## 2.2.1. Health Literacy We used a health literacy measure developed by the Centers for Disease Control and Prevention (CDC) in the 2016 Behavioral Risk Factor Surveillance System (BRFSS) [21]. The measure contains three self-reported survey questions: [1] assessing individuals’ abilities to find information, “How difficult is it for you to get advice or information about health or medical topics if you needed it?”; [ 2] understanding oral information, “How difficult is it for you to understand information that doctors, nurses, and other health professionals tell you?”; [ 3] understanding written information, “You can find written information about health on the Internet, in newspapers and magazines, and in brochures in the doctor’s office and clinic. In general, how difficult is it for you to understand written health information?”. Each item was assessed on a 4-point Likert type scale ranging from 1 = “very difficult” to 4 = “very easy”. We calculated the sum score, with possible scores ranging from 3 to 12. A higher score indicated that the participant reported having lower health literacy. Furthermore, to identify the individuals who were at high risk of having a low health literacy, we defined low health literacy as a response of “very difficult” or “difficult” to at least one of these three questions [22]. In addition, the item assessing an individual’s ability to find information included a response of “I don’t look for health information”, and the item assessing the understanding of written information included a response of “I don’t pay attention to written health information”. Both responses were coded as 0. ## 2.2.2. Information Trust and Use We also assessed the extent to which participants trusted and used sixteen sources (see Table 1) of information about COVID-19. ## 2.2.3. Demographics Demographics included gender, age, ethnicity, race, and annual family income. Race and ethnicity were determined by asking participants to select from a list of racial (i.e., White, Black or African American, American Indian or Alaskan Native, Asian, and Native Hawaiian or Pacific Islander) and ethnic (i.e., Hispanic, Latinx, or of Spanish origin) categories, those which best matched their own identity. Multiple options could be selected. Gender included three options: male (including transgender men), female (including transgender women), and self-described (non-binary, gender-fluid, agender, etc.), with an option to provide an open answer. Participants were asked to identify their annual family income through the following options: less than $ (U.S. Dollar) 20,000, $20,000–$34,999, $35,000–$49,999, $50,000–$74,999, $75,000–$99,999, and over $100,000. ## 2.3. Data Analysis Individuals who chose either “I don’t look for health information” or “I don’t pay attention to written health information” were treated separately from those who self-reported their levels of difficulty in finding information, understanding oral information, and understanding written information. We performed independent t-tests to compare trust in and use of different sources for COVID-19 information, between those who chose either “I don’t look for health information” or “I don’t pay attention to written health information”, and those who self-reported their levels of difficulty in finding information, understanding oral information, and understanding written information. Among participants who indicated levels of difficulty in finding information, understanding oral information, and understanding written information, we investigated the relationship between their self-reported health literacy capacity (independent variable) and their trust in and use of each source of COVID-19 information (outcome variable), and we performed bivariate linear regressions (without covariates) and multiple linear regressions (controlling for demographic characteristics). We included the demographic variables (gender, age, ethnicity, race, and annual family income) in our multiple linear regression analysis as covariates, because these demographic variables are associated with health literacy based on prior research [23]. We conducted separate regressions for each COVID-19 health information source, in terms of participants’ trust in and use of them. We used Stata 16 for statistical analysis. The significance level was set at α = 0.05. ## 3. Results Most participants were White ($75\%$), women ($66\%$), and between 18 and 24 years old ($80\%$). Participants’ ages ranged from 18 to 59 ($M = 23.13$, SD = 7.38). Table 2 shows the demographic characteristics of our participants. A total of 87 participants ($11.40\%$) chose either “I don’t look for health information” (54 participants) or “I don’t pay attention to written health information” (48 participants). Among those who indicated their levels of difficulty in finding information, understanding oral information, and understanding written information ($$n = 676$$, $88.60\%$), their health literacy scores ranged from 5 to 12 ($M = 9.75$, SD = 1.56). The health literacy score data were moderately skewed towards the high end, which indicated that most of our participants reported having adequate health literacy ($$n = 526$$, $68.94\%$). About $19.66\%$ of our participants ($$n = 150$$) were at a high risk of having low health literacy (i.e., provided a response of “very difficult” or “difficult” to at least one of the three health literacy questions). ## 3.1.1. Trust Among those who indicated their levels of difficulty in finding information, understanding oral information, and understanding written information ($$n = 676$$), the most trusted sources of COVID-19 information were doctors and other health care providers ($M = 3.24$, SD = 0.70), the CDC ($M = 3.07$, SD = 0.88), the WHO ($M = 2.86$, SD = 0.97), state/county/city health departments ($M = 2.77$, SD = 0.84), and official government websites ($M = 2.56$, SD = 0.91). The least trusted sources were social media ($M = 1.60$, SD = 0.66), (former) President Trump ($M = 1.70$, SD = 0.91), classmates ($M = 1.71$, SD = 0.59), TV ($M = 1.76$, SD = 0.72), and coworkers ($M = 1.78$, SD = 0.64). We observed the same patterns among our participants who were at a high risk of having low health literacy ($$n = 150$$), as well as among those who chose either “I don’t look for health information” or “I don’t pay attention to written health information” ($$n = 87$$). However, we noticed that those who reported being at risk of having low health literacy had a lower levels of trust in various sources when compared to those who reported having adequate health literacy. Those who chose either “I don’t look for health information” or “I don’t pay attention to written health information” had the lowest levels of trust for various sources among these three groups. ## 3.1.2. Use Use of a source was positively associated with trust in that source across all the sixteen sources (all $p \leq 0.001$). Among those who indicated their levels of difficulty in finding information, understanding oral information, and understanding written information ($$n = 676$$), the most commonly used sources (used more than $50\%$ of time) of COVID-19 information were the CDC ($M = 4.33$, SD = 1.80), as well as doctors and other health care providers ($M = 4.19$, SD = 1.67). State/county/city health departments ($M = 3.66$, SD = 1.78), the WHO ($M = 3.63$, SD = 1.94), official government websites ($M = 3.40$, SD = 1.84), and family members ($M = 3.20$, SD = 1.63) were also used more than $30\%$ of the time. The least commonly used sources were the state governor ($M = 1.93$, SD = 1.39), magazines and newspapers ($M = 1.95$, SD = 1.26), (former) President Trump ($M = 1.96$, SD = 1.54), radio and podcasts ($M = 2.00$, SD = 1.31), and classmates ($M = 2.00$, SD = 1.19). We observed the same patterns among our participants who reported being at a high risk of having low health literacy, as well as among those who chose either “I don’t look for health information” or “I don’t pay attention to written health information”. However, we noticed that those who reported being at risk of having low health literacy used various sources less frequently when compared to those who reported having adequate health literacy. Those who chose either “I don’t look for health information” or “I don’t pay attention to written health information” had the lowest frequency of using various sources among these three groups. ## 3.2.1. Trust Among those who reported their health literacy ($$n = 676$$), our unadjusted linear regression models indicated that students who reported having lower health literacy were more likely to trust COVID-19 information from social media (b = −0.04, $$p \leq 0.006$$) when compared to those who reported having higher health literacy. However, they were less likely to trust coworkers ($b = 0.04$, $$p \leq 0.022$$), (former) President Trump ($b = 0.06$, $$p \leq 0.010$$), and radio and podcasts ($b = 0.04$, $$p \leq 0.022$$). As shown in Table 3, after controlling for demographic characteristics (i.e., gender, age, race, ethnicity, and income), students who reported having lower health literacy were also less likely to trust COVID-19 information from family members ($b = 0.06$, $$p \leq 0.005$$), coworkers ($b = 0.04$, $$p \leq 0.009$$), (former) President Trump ($b = 0.05$, $$p \leq 0.019$$), and radio and podcasts ($b = 0.04$, $$p \leq 0.018$$) when compared to those who reported having higher health literacy. Our independent t-test results indicated that those who chose either “I don’t look for health information” or “I don’t pay attention to written health information” had significantly lower trust in doctors and other health care providers ($$p \leq 0.038$$), official government websites ($$p \leq 0.043$$), and the CDC ($$p \leq 0.006$$) for COVID-19 information when compared to those who reported their levels of difficulty in finding information, understanding oral information, and understanding written information. ## 3.2.2. Use Among those who reported their health literacy ($$n = 676$$), our unadjusted linear regression models indicated that students who reported having lower health literacy more frequently used social media (b = −0.13, $$p \leq 0.001$$) as sources of COVID-19 information than students who reported having higher health literacy. However, when compared to people who reported having higher health literacy, those who reported having lower health literacy less frequently used health care providers ($b = 0.09$, $$p \leq 0.027$$) as sources of health information about COVID-19. As shown in Table 3, after controlling for demographic characteristics (i.e., gender, age, race, ethnicity, income), students who reported having lower health literacy more frequently used social media (b = −0.09, $$p \leq 0.032$$) as sources of COVID-19 information than students who reported having higher health literacy. However, those reporting a lower health literacy capacity were less likely to use health care providers ($b = 0.12$, $$p \leq 0.005$$), and the CDC ($b = 0.11$, $$p \leq 0.016$$) to obtain information about COVID-19 when compared to participants who reported having higher health literacy. Our independent t-test results indicated that those who chose either “I don’t look for health information” or “I don’t pay attention to written health information” had a significantly lower frequency of using magazines and newspapers ($$p \leq 0.034$$), doctors and other health care providers ($$p \leq 0.002$$), official government websites ($$p \leq 0.002$$), the WHO ($$p \leq 0.031$$), the CDC ($$p \leq 0.018$$), and state/county/city health departments ($$p \leq 0.017$$) as sources of COVID-19 information when compared to those who reported their levels of difficulty in finding information, understanding oral information, and understanding written information. ## 4. Discussion In our study of college students during the COVID-19 pandemic, we found that college students who chose either “I don’t look for health information” or “I don’t pay attention to written health information” had significantly lower trust in and use of official health authority sources (e.g., health care providers, official government websites, and the CDC) of COVID-19 information when compared to students who reported their levels of difficulty in finding information, understanding oral information, and understanding written information. Additionally, we also found that those who reported lower health literacy had a lower frequency of using official health authority sources (e.g., health care providers and the CDC) of COVID-19 information when compared to students who reported higher health literacy; however, students who reported lower health literacy used social media for COVID-19 information more frequently. This pattern of information-seeking behavior is similar to a previous study investigating the relationship between health literacy and use of and trust in sources for general health information, which found that lower health literacy was associated with lower odds of trusting in specialist doctors and dentists for health information, as well as using medical websites, but higher odds of using social media, among a U.S. nationally representative adult sample [13]. Similarly, a recent study conducted among a German-speaking adult population of Switzerland also found that those with lower health literacy tended to not trust in, and more rarely use, health professionals and health authorities as sources of COVID-19 information when compared to those with higher health literacy [24]. Several factors might help explain why people with low health literacy neither trust nor use health professionals and health authorities for COVID-19 information gathering. First, people with lower health literacy have difficulty understanding physician instructions and have negative perceptions of their healthcare experience, such as receiving inadequate health information, which contributes to a low trust in health care providers [13,25,26]. In fact, many people with low health literacy work hard to hide the fact that they have difficulty understanding oral or written instructions from health care providers; in addition, people with low health literacy often do not have a regular health care provider [27,28]. These factors create a challenge for health care providers in gaining trust among those with low health literacy and providing health recommendations. The second possible explanation as to why people with low health literacy neither trust nor use health professionals and health authorities for COVID-19 information is that individuals with low health literacy have low trust in scientists, especially during the COVID-19 pandemic [29,30]. For example, people with low health literacy tend to not embrace the COVID-19 vaccine, due to a lack of trust in the government and scientists that stems from the uncertain attitudes towards possible herd immunity, the complexity of the scientific and political discourse surrounding COVID-19 vaccines, as well as questions about the vaccination effectiveness as new variants keep evolving [29]. Additionally, official rules and recommendations for preventive measures (e.g., mask mandates) based on scientific findings and clinical trials keep changing, because of the evolving knowledge about how this virus is behaving [30], which leads to negative feelings, such as being overwhelmed, confused, upset, and scared [8]. In fact, people with lower health literacy are more likely to avoid health information related to COVID-19, in order to reduce the above unwanted emotions [9]. Third, the level of trust in the CDC declined significantly during the COVID-19 pandemic, and many people view the CDC as strongly politized [31]. Another study also found that about 40 percent of adults in the U.S. felt that the CDC was paying too much attention to politics when issuing guidelines and recommendations for the COVID-19 policy [32]. Such distrust in the government stimulates the spread of fake news and mis/disinformation [33,34], which further reduces the trust in COVID-19 health information from government sources. Our current study finding indicates that college students with low health literacy have had an especially low trust in the government (e.g., the CDC) during this pandemic. Several strategies can help to increase trust in health care providers and government agencies among people with low health literacy. First, it is critical to provide easy-to-understand health information, with plain, jargon-free language through various channels, in order to meet people where they are. Second, health care providers should use the teach-back technique with their low health literacy patients, in order to ensure these patients understand the information and instructions they have been given. When applying the teach-back technique, health care providers ask their patients to repeat, in their own words, what they have been told, using a caring tone of voice and attitude to create a “shame-free” environment for patients [35]. Third, collaborating closely with community organizations and social workers to create health messages with no political interference is another effective strategy for government agencies to utilize, in order to build trust in the public [29,36]. Another strategy for government agencies to maintain public trust is to ensure clear information and unambiguous health instructions related to COVID-19 that represent government transparency and effective communication [37]. Lastly, a prior study pointed out that the CDC should create COVID-19 health information with a clearer and more explicit focus on the science, and should provide rationales for any decision made regarding the guidelines and recommendations of COVID-19 policy [32]. Moreover, we found that college students who reported having low health literacy tended to use various sources (including health care providers and public health authorities, such as the CDC, the WHO, state/county/city health departments, and official government websites) less frequently, and trusted them less when compared to those who reported having an adequate health literacy capacity, which is consistent with a recent study [24]. This finding indicates that people with a higher health literacy might compare information among different sources, in order to check for trustworthiness, while those with a lower health literacy do not [24]. Furthermore, in one of our recent studies, we found that college students with lower health literacy were more likely to intentionally avoid information about COVID-19 [9]. Our findings confirm that college students with low health literacy are at risk of COVID-19 knowledge deficits, due to their high information avoidance. They might not learn about the most important preventive behaviors and the value of vaccinations, which could lead to compliance violations and vaccine hesitancy. Our findings also indicate that there is a critical need for higher education institutions to create learning outcomes and to provide training, in order to enhance health literacy skills among college students [38]. We found that health care providers, the CDC, the WHO, state/county/city health departments, and official government websites were frequently used and highly trusted sources of COVID-19 information among college students. This finding aligns with a previous study, which reported that the U.S. adult population used and trusted health care providers the most for general health information [13]. Another study also reported that doctors and government health agencies were the most trusted source, among a U.S. nationally representative adult sample, of general health information [39]. Social media had low trust among our participants, which is consistent with a recent study concluding that people had low trust towards social media as a source of COVID-19 information [24], as well with as a previous study, reporting that social media received low trust among the U.S. adult population as a source of general health information [13]. We also found that (former) President Trump was one of the least used and trusted sources for COVID-19 information. These results were similar to those reported in our recent qualitative study, which recruited participants from the same university, and found that college students identified various pieces COVID-19 misinformation, especially from social media and politicians [8]. However, COVID-19 related information-seeking behavior patterns might be different across countries. For example, a recent study conducted by De Gani’s team among 1012 participants (age mean = 46.2, SD = 16.9) living in the German-speaking part of Switzerland reported that television and the Internet were the most used information sources for COVID-19; health authorities and health professionals were used by less than half of the respondents, but these two sources were reported as being highly trusted [24]. Although we collected our data at the same time as De Gani’s team did, we found that television was not a frequently used source among our college participants, and they used health authority- and health professional-related sources frequently. These two study samples consisted of different age groups, however, which might contribute to such differences, as age is a significant predictor of information-seeking behaviors [40]. Another possible explanation is that COVID-19 information dissemination and mitigation strategies are used differently across countries [41]. Interestingly, after adjusting for gender, age, race, ethnicity, and income, the association between lower health literacy and higher trust in social media for COVID-19 information became non-significant. The 2007 HINTS data indicated that younger adults had a higher level of trust in online health information, regardless of the information quality; one possible explanation is that young adults perceive themselves as being less vulnerable to low-quality health information, due to this age group being generally healthier than older adults [42]. Therefore, our finding might also indicate that age plays a more critical role than health literacy levels in terms of trust in social media. Social media is one of the least trusted sources for COVID-19 information among our participants; however, we found that students who reported having a lower health literacy more frequently used social media as sources of COVID-19 information than students who reported having higher health literacy. Social media can play a positive or negative role in providing health information related to COVID-19. Social media can be used to assist in seeking, understanding, and sharing health information; however, the quality and accuracy of the health information from social media need to be evaluated cautiously [43]. On one hand, social media helps to reduce social isolation and improve mental health, as it provides a connection between people and their peers, friends, and family [44]. On the other hand, COVID-19 misinformation is more likely to be shared on social media than official sources [45], and people experience negative feelings, such as fear and confusion, when they identify various pieces of misinformation related to COVID-19 on social media [8]. ## Limitations Although this study reported how health literacy plays a role in COVID-19 information-seeking behaviors, it has some limitations. First, the cross-sectional survey design of this study limits our ability to infer causal relationships. Second, different types of health literacy measures are associated with people’s patterns of information source usage [46]. There are many different instruments used to measure individuals’ health literacy [47]. Moreover, some studies have developed health literacy measures that are specific to COVID-19 [24,46]. Due to the fact that people have been constantly exposed to COVID-19-related information during this pandemic, they tend to have higher COVID-19-specific health literacy scores than their general health literacy scores [24]. Different measures of health literacy could produce different results, as different measures may assess slightly different skills [47]. Third, our convenience sampling method and relatively small sample size temper our ability to generalize our findings to the entire U.S. or other countries. Lastly, our COVID-19 information source list was not exhaustive, although the sources used here have been shown to be the most frequently used in many other studies. ## 5. Conclusions This study makes an important contribution to our understanding of the patterns in information source preferences, specific to COVID-19, among college students attending a land-grant university located in the South–Central region of the U.S., with variations in self-reported health literacy levels. We found that college students who reported having lower health literacy reported lower levels of using and trusting official sources for COVID-19 information when compared to their peers who reported having higher health literacy. Relying on low-quality information sources and misinformation could create and reinforce people’s misperceptions regarding the virus, and further lead to less compliance with COVID-19-related public health measures. ## References 1. 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--- title: 'Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with Real-World Variability Analysis' authors: - Ahtsham Zafar - Dana M. Lewis - Arsalan Shahid journal: Healthcare year: 2023 pmcid: PMC10048652 doi: 10.3390/healthcare11060779 license: CC BY 4.0 --- # Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with Real-World Variability Analysis ## Abstract Glucose forecasting serves as a backbone for several healthcare applications, including real-time insulin dosing in people with diabetes and physical activity optimization. This paper presents a study on the use of machine learning (ML) and deep learning (DL) methods for predicting glucose variability (GV) in individuals with open-source automated insulin delivery systems (AID). A three-stage experimental framework is employed in this work to systematically implement and evaluate ML/DL methods on a large-scale diabetes dataset collected from individuals with open-source AID. The first stage involves data collection, the second stage involves data preparation and exploratory analysis, and the third stage involves developing, fine-tuning, and evaluating ML/DL models. The performance and resource costs of the models are evaluated alongside relative and proportional errors for 17 GV metrics. Evaluation of fine-tuned ML/DL models shows considerable accuracy in glucose forecasting and variability analysis up to 48 h in advance. The average MAE ranges from 2.50 mg/dL for long short-term memory models (LSTM) to 4.94 mg/dL for autoregressive integrated moving average (ARIMA) models, and the RMSE ranges from 3.7 mg/dL for LSTM to 7.67 mg/dL for ARIMA. Model execution time is proportional to the amount of data used for training, with long short-term memory models having the lowest execution time but the highest memory consumption compared to other models. This work successfully incorporates the use of appropriate programming frameworks, concurrency-enhancing tools, and resource and storage cost estimators to encourage the sustainable use of ML/DL in real-world AID systems. ## 1.1. Overview of Data-Driven Automated Insulin Delivery Systems With an ever-increasing number of diabetes technologies that assist individuals living with insulin-requiring diabetes, large amounts of diabetes-related and user-entered behavioral data are generated. Connected insulin pens or insulin pumps deliver insulin, and real-time blood glucose information is obtained using Bluetooth-enabled glucose meters or continuous glucose monitors (CGM). Insulin pumps and CGM can be combined as part of an automated insulin delivery (AID) system, where data from each device flows through an algorithm to determine insulin-delivery rates and automatically adjust them to keep glucose values in a specific range, requiring less work from people with diabetes and also improving quality of life outcomes [1]. AID systems further generate rich data regarding the conditions (such as sensor glucose values, user-entered information such as targets or carbohydrates, and current and previous insulin delivery) in which it operates [2]. Exploring these rich data sources unveils opportunities for scientific discoveries to understand individual glucose outcomes better and improve diabetes technology. There has been increasing interest in applying machine learning (ML) and deep learning (DL) techniques to improve predictions of glucose levels [3]. Accurate and reliable glucose profile forecasting is essential for a range of data-driven applications and use cases that improve diabetes management (Figure 1). ML models are able to train and automatically capture hidden trends and patterns in large volumes of data with considerable accuracy and efficiency. This enables them to make decisions for various prediction and classification tasks and to learn and improve over time. ## 1.2. Applications of Machine Learning and Deep Learning in AID Systems Several ML techniques, including K-Nearest Neighbour (KNN), Random Forests (RF), Long Short Term Memory (LSTM), Support Vector Regressor (SVR), and Gradient Boost (XGBoost), have been used for regression and classification tasks to predict and identify hypoglycemia and hyperglycemia [4,5,6,7,8,9,10,11,12,13,14]. These methods use invasive and non-invasive techniques to collect data such as continuous glucose monitor data and physiological and demographic features to train the models and achieve high prediction accuracy. Our in-depth review of ML/DL methods applied to glucose forecasting (Section 2.1) yields a list of challenges and limitations to the practical adoption of these methods in open-source AID systems for glucose profile forecasting, including: [1]. limited prediction horizon (30, 60, or 120 min) of trained models, [2]. inconsistency of reported accuracies and employed model evaluation metrics makes it difficult to compare and reproduce the existing work, [3]. unavailability of large-scale and real-world diabetes datasets that encourage the use of artificial and synthetic data for model training and evaluation, [4]. lack of evaluation and reporting on the computing resource costs of building the models, [5]. lack of implementation details and open-source models that are fine-tuned on diabetes datasets, and [6]. lack of assessment of clinically-approved glucose variability metrics (reviewed in Section 2.2) based on predicted glucose profiles. Historically, due to the non-availability of quality diabetes data, many early datasets used to perform ML-related work were considered “large” if they contained several weeks of data from a dozen individuals. However, with the early adoption of open-source AID systems, which predated the availability of commercial AID systems for several years, users donated their anonymized data for diabetes research [15]. The resulting dataset from the OpenAPS Data Commons contains tens of thousands of days of glucose data points [16] and is employed in this paper. One unique aspect of open-source AID systems such as OpenAPS is its inherent design to be understandable to users, including the rationale of every decision it makes. ML can be seen as a black box, and it may be challenging to substitute an ML-based prediction algorithm wholesale into an open-source AID. However, OpenAPS is uniquely designed to generate predictions based on various scenarios, including whether carbohydrates are fully absorbed, or a meal is consumed but not recorded to the system. These predictions are conditionally blended and heuristically used [17], such as to produce estimates of the lowest predicted glucose value to be observed over the timeframe relevant for insulin dosing and separately the blended average glucose level over the approximate period when the activity of any additional insulin would be peaking, in order to limit contributions to hypoglycemia while also seeking to minimize hyperglycemia. Therefore, OpenAPS is one such system where an ML-based prediction algorithm could be introduced and blended into the current set of predictions and used alongside the backstop of safety rules used by the system to achieve the highest possible time in the target glucose range (known as “time in range” or TIR) without much hypoglycemia or hyperglycemia. ## 1.3. Original Contributions As a result of this opportunity for improvement, this paper sought to assess different ML-based prediction methods for glucose profiles, paying particular attention to limitations mentioned above in the existing works [18] and to their performance in terms of accuracy and resource consumption of the implementation (training/inference time and memory consumption) intending to integrate them in open source or future commercial AID solutions. In this paper, 30 and 60 days of glucose data has been employed from a set of individuals having diverse demographic attributes from OpenAPS Data Commons to train a set of ML and DL models, including ARIMA, XGBoost, RF, SVR, and LSTM. The fine-tuned models have been further evaluated based on their performance and resource consumption for glucose profile prediction up to 48 h. Finally, a set of clinically-validated statistical and glucose variability (GV) metrics have been calculated, and a comparative analysis of the predicted and expected outcomes are presented. All models have been implemented with the flexibility to train online, and programming scripts are open-sourced for reproducibility and benchmarking [19]. ## 1.4. Organisation of the Paper The rest of this paper is divided into the following sections. Section 2 presents the literature review of tools and technologies for glucose profile assessment and the latest advances in ML-based glucose forecasting methods. Section 3 provides a summary of the dataset and techniques adopted for diabetes data collection, selection and cleaning; followed by a description of employed ML-based predictive models and the glucose analysis metrics. Section 4 presents the glucose variability assessments and the evaluation results of trained ML models for selected individuals with insulin-requiring diabetes. The section further shows the performance and resource costs of ML-based predictive models and reports the relative and proportional errors as a result of a comparison of GV metrics obtained for predicted and expected glucose profiles. Section 5 presents discussions on the analysed ML model outcomes and assessment of metrics used for glucose analysis, highlights the lessons learned, and criticises the limitations. Finally, Section 6 concludes the paper and provides a roadmap for future considerations. ## 2. Related Work This section first highlights recent research developments towards ML-enabled glucose predictions and highlights the main limitations and challenges; followed by a review of clinically-approved glucose variability metrics. ## 2.1. Review of Machine Learning and Deep Learning Methods and Techniques for Glucose Forecasting Several machine learning and statistical learning techniques have been employed for regression and classification tasks to predict and identify hypoglycemia and hyperglycemia. Mordvanyuk et al. [ 4] employed K-Nearest Neighbour (KNN) algorithm on machine-simulated data and used the meal information along with CGM data to predict out of range glucose with $83.64\%$ accuracy. Dave et al. [ 5] employed 26 features including gender, the hour of the day, etc as multivariate input in logistic regression (LR) and random forest (RF) algorithms to predict glucose up to 60 min with sensitivity and specificity over $90\%$. Another approach is the use of physiological data including heart rate and movement recorded by a smartwatch alongside CGM data of an individual employed in the Gradient Boost algorithm to classify normal blood glucose levels and hypoglycemia with an accuracy of $82.7\%$ [6]. Zhu et al. [ 7] used OhioT1DM dataset [20] to train Long Short Term Memory (LSTM) network to predict up to 30 and 60 min of glucose data and reported root mean square error (RMSE) of 19.10 mg/dL and 32.61 mg/dL, respectively. In [8], simulated data from UVA-Padova [21] (360 simulated days of 10 patients) and OhioT1DM dataset (8 weeks of clinical trials on 6 patients) were employed to train a dilated recurrent neural network (D-RNN) with prediction RMSE of 20.1 mg/dL. Using data from 12 individuals from OhioT1DM, Yang et al. [ 9] developed an autonomous channel model using a combination of multiple LSTM models for glucose prediction for up to next 30 and 60 min with an RMSE of 18.9 mg/dL and 31.79 mg/dL, respectively. Berikov et al. [ 10] used eight CGM-derived metrics including glycemic control and glucose variability from 406 patients in RF, logistic linear regression with lasso regularization, and artificial neural networks (ANN) to predict the next 15 and 30 min of glucose data with considerable accuracy. Duckworth et al. in [11] used explainable ML (trained using CGM data for 153 people with diabetes) to make predictions of hypoglycemia and hyperglycemia up to 60 min. The gradient boost (GB) algorithm yielded a reasonable prediction performance (AUROC) of 0.998 and 0.989 for hypoglycemia and hyperglycemia, respectively, in comparison to standard heuristic and logistic regression models. Van et al. [ 12] employed a portion of the Maastricht Study’s dataset (including CGM and accelerometer) to train multiple ML and DL models (including ARIMA, support vector regressor (SVR), GB, LSTM, and RNN) and predicted the next 15 and 60 min of blood glucose levels with an RMSE of 0.48 mmol/L and 0.9 mmol/L, respectively. In [13], authors trained a personalized LSTM model (using UVA-Padova simulator data for 100 patients with meals, insulin, and past blood glucose) to predict the next 40 min of blood glucose levels with an RMSE of 7.67 mg/dL. Allam et al. [ 14] trained an RNN and SVR using data from 9 individuals to predict blood glucose for 15, 30, and 60 min horizon with an RMSE (in mmol/L) for 0.14, 0.55, 1.32 for RNN and 0.52, 0.89, 1.37 for SVR, respectively. In [22], authors presented an ensemble approach using SVR as a base model and using ARIMA and physiological features (trained on data for 10 individuals with type-1 diabetes) to predict blood glucose levels with RMSE (in mg/dL) of 19.5 and 35.7 for 30 and 60 min prediction horizon, respectively. A jump neural network (JNN) in [23] is trained on data for 20 T1D individuals to predict 30 min of blood glucose with an RMSE (Mean ± Standard deviation) of 16.6 ± 3.1 mg/dL. Pustozerov et al. [ 24] trained a linear regression model using data from 62 individuals (with 48 pregnant women with gestational diabetes mellitus (GDM) and 14 women with normal glucose tolerance) with food intake as an evaluation parameter. Results show that the RMSE of BG levels for 1 h after food intake is 0.87 mmol/L. The use of smartwatches has seen tremendous growth with improvements in sensor technology motivated by the use of Photoplethysmography (PPG) signals to detect volumetric changes in blood in the peripheral circulation [25]. Data from 9 people (3 males and 6 females) was used to train ada-boost and RF models to provide $90\%$ prediction accuracy for glucose levels [25]. Dave et al. [ 5] trained an RF model to predict possible hypoglycemia for 30 and 60 min ahead of time with a sensitivity and specificity of $91\%$ and $90\%$, respectively. Georga et al. [ 26] used multivariate data (including glucose profile, plasma insulin concentration, appeared glucose derived from a meal in the blood circulation, and the energy utilized during other physical activities) from 27 people in free-living conditions in an SVR to predict glucose levels for 15, 30, 60, and 120 min with average prediction errors of 5.21, 6.03, 7.14, and 7.62 mg/dL, respectively. Pérez-Gandía et al. [ 27] trained a neural network using data from 15 individuals to predict glucose in 15, 30 and 45 min horizon with an RMSE of 10, 18, and 27 mg/dL, respectively. ## Limitations and Shortcomings To summarise, multiple ML/DL frameworks and methodologies have been employed to forecast and predict blood glucose for people with diabetes. The limitations and shortcomings of the existing literature are listed below:The primary issue of all the reported methods is the evaluation of trained models for a limited prediction horizon of 30 min and 60 min, with the maximum being 120 min, i.e., the reported predictions for the trained models are in the range of 30, 60, or 120 min. The lack of consistency in the accuracies of the reported models makes it difficult to compare the existing work. This further affects the reliability of the trained models for further evaluation and reproducibility. Another drawback of the existing literature is the previous lack of large-scale and real-world datasets for individuals with diabetes that use automated insulin delivery systems. Therefore, the majority of the aforementioned models in the literature are trained on partial/fully simulated data or limited days of real-world CGM data. Multiple model performances and accuracy metrics have been used (including RMSE, specificity, MAE and F1 score) to evaluate the model predictions. However, to the best of our knowledge, none of the existing works has evaluated and studied the impact of glucose predictions by calculating the clinically validated glucose variability (GV) metrics. There is a lack of implementation details and open-source methods to reproduce the reported results which makes it difficult to independently evaluate them on additional datasets or to be able to evaluate their applicability for different modalities of insulin therapy, such as in sensor-augmented pump therapy as compared to automated insulin delivery therapy. Most of the existing works employed a limited number of machine learning models (one or two) for evaluation which certainly adds inconsistency. However, it is critical to evaluate model results for multiple machine learning and deep learning models along with tuned time series analysis frameworks like ARIMA. Evaluating the results of multiple model types would lay a foundation for benchmarking. ## 2.2. Clinically-Approved Statistical and Variability Metrics for Glucose Analysis Over 25 clinically approved GV metrics have been adopted by the diabetes research community. Table 1 list the acronyms and full forms of the most important and commonly used metrics for GV assessment. To assist in the automated calculation and visualisation of clinically approved GV and statistical metrics, many open-source programming tools and frameworks have been developed. These include cgmquantify [28], CGM-GUIDE [29], CGDA [30], EasyGV [31], cgmanalysis [32], and GlyCulator [33]. ## 3. Materials and Methods This section presents the experimental workflow and adopted processes and procedures for diabetes data collection, anonymisation, cleaning, processing, modeling, and analysis. ## 3.1. Experimental Workflow and ML Development Pipelines The experiments are conducted using a standalone Intel-based Core-i7 CPU processor (2 cores, 2 threads) with 8 GB of main memory. Figure 2 illustrates a tri-staged architecture demonstrating the experimental workflow employed in developing and analyzing ML/DL models. ## 3.2. Highlights of Data Collection, Anonymisation, and Cleaning The OpenAPS Data Commons, collated as a project on the Open Humans platform, is imported as anonymized diabetes dataset with rich CGM data, insulin delivery information from insulin pumps, user-entered information such as carbohydrate entries or temporary target changes, as well as algorithm-derived information about insulin dosing decisions. An individual was randomly chosen to test the ML/DL methods described below. After initial tests of methods and validating how much data was needed for analysis, an additional 18 individuals were chosen from the dataset based on the diversity of demographic variables such as ages, AID system used, geography, etc. Table 2 summarizes the demographics of the resulting $$n = 19$$ individuals employed in the dataset for this paper, alongside their gender and geography distributions. Data cleaning methods has been reproduced for timestamps and glucose entries from previous work on glycemic variability [35], and all programming scripts are open-source at [36]. ## 3.3. Machine Learning and Deep Learning Algorithms Employed for Glucose Forecasting Selected ML and DL timeseries forecasting models for glucose include ARIMA [37], XGBoost [38], RF [39], LSTM [40], and SVR [41]. Table 3 provide model descriptions, their fine-tuned hyperparameters for glucose data, and Python implementation library. Although SVR was initially employed to forecast glucose profiles, due to excessive training and execution time and resource consumption, it was dropped and was not considered for further experiments on our dataset. Model evaluation metrics for performance and resource cost are described in Appendix A. It is important to note that a three-stage process is utilized for ARIMA model building [37]. The first step involves the identification of the order of differencing (d), the order of autoregression (p), and the order of moving average (q) required to model the data. This step involves analyzing the autocorrelation and partial autocorrelation functions of the time series data to determine the values of p and q and analyzing the time series data to determine the value of d. In the second step, parameters have been estimated using maximum likelihood estimation. Lastly, the adequacy of the ARIMA model is checked. This involves analyzing the residuals of the model, which are the differences between the actual data and the model predictions. When it comes to predicting time series data there are several DL algorithms, however, LSTMs are often considered a reasonable choice for univariate time series prediction due to its ability to handle long-term dependencies and capture temporal patterns in the data. LSTM is a type of recurrent neural network (RNN) that is capable of retaining long-term dependencies in the data, which is particularly useful for time series prediction, where past values can have a strong influence on future values. Unlike traditional RNNs, which can suffer from vanishing or exploding gradients when dealing with long-term dependencies, LSTM has a mechanism to selectively forget or remember information from previous time steps. Some other conventional DL algorithms were less suitable for our task due to a number of reasons including the inefficiency of univariate time series prediction tasks, computational complexity, and complex hyperparameter tuning. For example, Convolutional Neural Networks (CNNs) are often used for image classification, they can also be applied to time series prediction by treating the time series as a 1D image. However, CNNs may not be suitable for all time series problems, especially if the time series has complex temporal dependencies that cannot be captured by convolutional filters. Similarly, Deep Belief Networks (DBNs) are generative models that consist of multiple layers of Restricted Boltzmann Machines (RBMs) and can be used for unsupervised feature learning. However, they can be computationally expensive to train and may require more data to learn meaningful representations. ## 3.4. Statistical and Variability Metrics for Glucose Analysis Descriptive statistic metrics are computed for glucose profiles to analyse the spread, variation, and distributions. These metrics include mean, standard deviation (SD), coefficient of variation (CV), skewness score, and quantile statistics (Table 4). Q1, Q2, and Q3 represent the first, second, and third quartiles that evaluate the overall data distribution, respectively. CV indicates the variability in data concerning the mean; the higher the CV is, the more dispersed the data will be. The skewness score is the measure of asymmetric distribution. A number of clinically approved GV metrics are computed using EasyGV tool [31] and compared for measured (using CGM sensors) and predicted (using ML/DL models) glucose profiles. Relative and proportional errors were calculated and the rationale behind using two error metrics is given in Appendix B. ## 4. Results This section presents the results of in-depth statistical and GV analysis followed by evaluation and analysis of trained ML/DL models. ## 4.1. Descriptive Statistics and Glucose Variability Metrics for Selected AID Users Statistical methods are applied to complete glucose profiles for $$n = 19$$ individuals to evaluate timeseries data in terms of their characteristics. Stationarity analysis was applied using the augmented Dickey-Fuller (ADF) and Kwiatkowski Phillips Schmidt Shin (KPSS) test. A glucose profile is labeled stationary if both tests conclude that the series is stationary. It is labeled as difference stationary in case only the ADF test is positive and trend stationary if only the KPSS test is positive. It was observed that all the glucose profiles are stationary, with both ADF and KPSS tests being positive. Further analyse was done to evaluate if the time series is seasonal using auto-correlation, and if seasonality is detected, the best period would be found. If the autocorrelation is over 0.9, the data was labelled as seasonal. However, no evident seasonality and periods are detected for selected individuals. Table 4 reports the descriptive statistics for complete glucose profiles for $$n = 19$$ individuals. AID19 had the minimum number of data points (equal to 96 days worth of glucose data), whereas AID3 has the maximum count (constituting 1688 days worth of glucose data). The glucose profile variation is an essential factor in hypoglycemia/hyperglycemia assessment. The minimum and maximum mean values for glucose profiles are 98.42 mg/dL and 158.42 mg/dL, respectively, and the overall average of glucose profiles is 137.56 mg/dL. The minimum, maximum, and average SD for glucose profiles are {30.68, 60.27, 50.15} mg/dL. The average CV for all glucose profiles is 36.36 mg/dL, while the maximum and minimum are 44.37 mg/dL and 26.18 mg/dL, respectively. Quantiles Q1, Q2, and Q3 determine how many values in a distribution are above or below $25\%$, $50\%$, and $75\%$ limits. The minimum, average, and maximum of Q1, Q2, and Q3 are {76, 101.05, 115} mg/dL, {91, 127.94, 153} mg/dL, and {114, 164.78, 195} mg/dL, respectively. The skewness value greater than ±1 indicates highly skewed distributions. These include AID3, AID7, AID9, AID10, AID15, and AID18. The skewness score between −0.5 and 0.5 (including AID1, AID5, AID8, AID11, AID17, and AID19) indicates symmetrical distributions. The rest of the glucose profiles have skewness scores between 0.5 and 1 or −0.5 and −1, demonstrating that they are moderately skewed. Table 5 reports the GV metrics. The average SD ROC recorded amidst all glucose profiles is 1.47 mg/dL, whereas the minimum and maximum are 0.79 mg/dL and 2.05 mg/dL, respectively. The minimum and maximum TBR, TIR, and TAR are {$0.78\%$, $16.97\%$}, {$63.6\%$, $93.9\%$}, and {$2.6\%$, $32.43\%$}, respectively. The overall averages for TBR, TIR, and TAR among all glucose profiles are {$4.78\%$, $76.85\%$, $18.36\%$}. The recorded average (min–max) for LBGI, HBGI, GMI, and J-Index among selected AID users is 1.23 (0.41–3.82), 4.16 (0.74–6.84), 6.59 (5.66–7.1), and 35.68 (17.35–46.66), respectively. ## 4.2. Performance and Resource Cost Evaluation and Analysis of Trained ML/DL Algorithms The ML/DL models are trained by employing 30 and 60 days of data and tested individually for their performance and resource costs to predict glucose up to 48 h. Resource costs are evaluated by measuring execution time and memory consumption, whereas RMSE and MAE are calculated to assess the model’s prediction performance. Figure 3 shows the MAE, RMSE, and execution time for models trained on 30 days of glucose data. The results for models trained on 60 days of glucose data are given in Appendix E. The maximum value of MAE of 8.07 is observed for ARIMA, whereas the lowest MAE is 1.295 reported for the random forest model (Figure 3a). Overall, the ARIMA model yields the highest MAE indicating the least prediction performance. The maximum and minimum recorded RMSE is 10.42 for AID9 and 2.16 for AID11, respectively, both in the case of XGBoost (Figure 3b). No noticeable trend was observed between the RMSE values of reported models trained on 30 days of glucose data when compared with the ones trained on 60 days of glucose data. ARIMA yields a maximum execution time equal to 780 s. In comparison, LSTM performs best in terms of execution time with a minimum of 162 s (Figure 3c. However, LSTMs are recorded as memory-hungry, with consumption peaking at 1993 MBs (Appendix D). ## 4.3. Comparative Analysis of Glucose Variability for Predicted and Expected Glucose Profiles GV metrics have been calculated from the predicted and expected profiles up to 48 h for $$n = 19$$ individuals and evaluate error scores between each GV metric using relative and proportional errors (defined in Appendix B). Table 6 reports the mean of minimum, average, and maximum relative and proportional errors for GV metrics among selected individuals; obtained by comparing ground truths with the ones calculated using the glucose profiles predicted by ARIMA, XGBoost, LSTM, and RF, respectively. The models trained on 30 days of data are denoted by ARIMA30, XGBoost30, LSTM30, and RF30, respectively. Additional results for the models trained on 60 days of data (ARIMA60, XGBoost60, LSTM60, and RF60) are provided in Appendix F. Errors have been represented in sets of minimum, average, and maximum. The highest score in the case of ARIMA30 for relative and proportional errors is obtained for TBR with {$0\%$, $11.78\%$, $54.55\%$} and {1, 1.12, 1.55}, respectively. The noticeable problem with the relative error is the inconsistency in the maximum error because it considers equal relative proportions for expected and predicted values. Therefore, the proportional error can be considered a comparatively more gaugeable parameter. The relative and proportional errors obtained by XGBoost30 is the highest for MVALUE equal to {$1.67\%$, $12.18\%$, $64.69\%$} and {1.02, 1.12, 1.65}, respectively. For LSTM30, MAG has the highest reported relative and proportional errors equal to {$12.54\%$, $37\%$, $110\%$} and {1.14, 1.63, 2.57}, respectively. The relative errors obtained by RF30 are the highest for MAGE equal to {$0\%$, $18.2\%$, $182\%$}. However, the highest proportional errors are obtained for TBR equal to {1, 1.22, 3.5}, respectively. ## 5. Discussion Large-scale diabetes datasets, such as the OpenAPS Data Commons, provide opportunities for researchers to develop innovative ML/DL tools and technologies and improve the functionality of future automated insulin delivery (AID) systems. This work addresses the limitations of existing ML/DL methods (Section 2.1.1) for predicting glucose profiles by developing models using a dataset of diverse individuals with insulin-requiring diabetes who use open-source AID systems. ML/DL solutions for diabetes require computing resources, so practical solutions that are fine-tuned and optimized to reduce energy consumption without degrading performance are necessary. This includes using appropriate programming frameworks and tools that enhance concurrency, as well as resource and storage cost estimators and minimizers. Incorporating these strategies ensures the sustainable use of ML technologies and minimizes the environmental impact. In addition to evaluating the accuracy of predictions, it is important to assess the feasibility and sustainability of ML/DL models for use in real-world AID solutions. The min and max mean values for glucose are likely below average (137.56 mg/dL) due to the use of open-source AID (Table 4). This is confirmed by studies, including a recent RCT [42], which show that open-source AID users typically achieve above-goal glucose metrics. This work also uniquely evaluates data from three open-source AID systems (OpenAPS, AndroidAPS, and Loop). It is worth reflecting that with a decrease in time below range (TBR) and as it is approaching to 0 (which is ideal), the relative error will increase accordingly. Although AID systems significantly improve glucose management, one should also consider infrequent but significant events such as severe hypoglycemia (a “bad low”) and its long-lasting effects on glucose variability. However, current literature on ML/DL-based glucose forecasting only considers prediction horizons of up to 120 min, hindering the understanding of the relationship between glucose variability and such events. These ML/DL models fine-tuned using the OpenAPS Data Commons accurately forecast glucose profiles up to 48 h (see Appendix C for example profiles). The average MAE range for all trained models is 2.50 mg/dL (for LSTM) to 4.94 mg/dL (for ARIMA). LSTMs have the lowest overall MAE (0.99 mg/dL for AID14) when trained with 60 days of glucose data. The average RSME is 3.7 mg/dL for LSTM to 7.67 mg/dL for ARIMA (Figure 3b). ML/DL models developed in this work have been evaluated for their computing resource costs. This analysis shows that the execution time of a model is proportional to the amount of data used to train it. For example, models trained on 30 days of data have almost half the execution time of models trained with 60 days of data. LSTMs have the least execution time and the highest memory consumption compared to other models. However, since CPU/GPU time contributes the most to energy-consumption costs, LSTMs are the most resource-efficient in our case. LSTMs could run daily during non-critical times to generate daily predictions, similar to how Autotune, a non-ML-based algorithm for recommending setting changes, runs overnight in OpenAPS [43]. Future work should also consider evaluating cloud computing and the tradeoff costs, including both computing power and the safety risk of off-device calculations in the context of AID. ## 6. Conclusions Our study comparing GV metrics calculated using predicted and original glucose profiles show the improved accuracy and reliability of extended horizon forecasts in real-world applications. GV metrics are widely used to understand diabetes management outcomes, above and beyond standard glucose outcome metrics, and should continue to be used to evaluate ML/DL-based glucose forecasting methods. The lower error scores in Table 6 show that fine-tuned ML/DL models can accurately estimate glucose variability outcomes for up to 48 h in the future, which is a much longer horizon than has previously been studied with ML/DL methods. Future work should evaluate these methods on different, non-AID diabetes datasets to assess whether ML/DL is “learning” that an AID system will be able to successfully correct according to the forecast; additional work should then also extend this work to assess the utility of such extended forecasts for non-AID users living with diabetes. The applications of ML/DL described in this paper have the potential to form the basis for intelligent recommender systems in future-generation AIDs and other diabetes applications. 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--- title: Antioxidant Activity and Inhibition of Liver Cancer Cells’ Growth of Extracts from 14 Marine Macroalgae Species of the Mediterranean Sea authors: - Nikolaos Goutzourelas - Dimitrios Phaedon Kevrekidis - Sofia Barda - Paraskevi Malea - Varvara Trachana - Stavroula Savvidi - Alkistis Kevrekidou - Andreana N. Assimopoulou - Andreas Goutas - Ming Liu - Xiukun Lin - Nikolaos Kollatos - Grigorios D. Amoutzias - Dimitrios Stagos journal: Foods year: 2023 pmcid: PMC10048654 doi: 10.3390/foods12061310 license: CC BY 4.0 --- # Antioxidant Activity and Inhibition of Liver Cancer Cells’ Growth of Extracts from 14 Marine Macroalgae Species of the Mediterranean Sea ## Abstract Macroalgae exhibit beneficial bioactivities for human health. Thus, the aim of the present study was to examine the antioxidant and anticancer potential of 14 macroalgae species’ extracts, namely, Gigartina pistillata, Gigartina teedei, Gracilaria gracilis, Gracilaria sp., Gracilaria bursa pastoris, Colpomenia sinuosa, Cystoseira amentacea, Cystoseira barbata, Cystoseira compressa, Sargassum vulgare, Padina pavonica, Codium fragile, Ulva intestinalis, and Ulva rigida, from the Aegean Sea, Greece. The antioxidant activity was assessed using DPPH, ABTS•+, •OH, and O2•− radicals’ scavenging assays, reducing power (RP), and protection from ROO•-induced DNA plasmid damage assays. Moreover, macroalgae extracts’ total polyphenol contents (TPCs) were assessed. Extracts’ inhibition against liver HepG2 cancer cell growth was assessed using the XTT assay. The results showed that G. teedei extract’s IC50 was the lowest in DPPH (0.31 ± 0.006 mg/mL), ABTS•+ (0.02 ± 0.001 mg/mL), •OH (0.10 ± 0.007 mg/mL), O2•− (0.05 ± 0.003 mg/mL), and DNA plasmid breakage (0.038 ± 0.002 mg/mL) and exhibited the highest RP (RP0.5AU 0.24 ± 0.019 mg/mL) and TPC (12.53 ± 0.88 mg GAE/g dw). There was also a significant correlation between antioxidant activity and TPC. P. pavonica (IC50 0.93 ± 0.006 mg/mL) exhibited the highest inhibition against HepG2 cell growth. Conclusively, some of the tested extracts exhibited significant chemopreventive properties, and so they may be used for food products. ## 1. Introduction Chemoprevention is currently considered among the most important strategies for fighting cancer [1]. Specifically, chemoprevention is defined as the use of natural or synthetic compounds as drugs or through the diet for the prevention or even the reversal of carcinogenesis [2]. One of the cancer types suggested as suitable for the application of chemoprevention is liver cancer [1]. Liver cancer is the fifth most frequent tumor type worldwide and the third in terms of mortality [1]. So far, there has not been an effective conventional therapy for liver cancer. Surgical resection, local ablation, and liver transplantation are the most common treatments for a small proportion of patients with early-stage hepatocellular carcinoma [1,3]. The main drug used for the treatment of advanced liver cancer is sorafenib [3]. Sorafenib inhibits cancer cell proliferation and tumor angiogenesis through the inhibition of several serine/threonine kinases such as Raf-1, and receptor tyrosine kinases such as vascular endothelial growth factor receptors (VEGFRs) and platelet-derived growth factor (PDGF) [3]. However, sorafenib provides modest gains in survival [1,3]. Thus, there is a great need for alternative treatments for use as chemopreventive agents [1,3]. Epidemiological studies have shown that populations of many countries with high consumption of fish and seafood have low prevalence of particular type of cancers (e.g., lung, breast, colorectal, and prostate cancers) [4]. This observation has led to extensive investigations of the benefits of compounds present in edible marine organisms, including marine macroalgae, as cancer chemopreventive agents [4]. Numerous studies have demonstrated the association between oxidative stress (i.e., the overproduction of free radicals) and cancer in humans [5]. Thus, the uptake of antioxidants through the diet or as food supplements has been suggested for cancer prevention [1]. Interestingly, compounds such as polyphenols and bromophenols isolated from marine macroalgae have been shown by our research group and others in in vitro and in vivo studies to possess antioxidant and anticancer activities [6,7,8]. For example, macroalgae’s compounds have been shown to act as reactive oxygen species’ (ROS) scavengers, and consequently they may provide protection from ROS-induced DNA damage in non-cancerous cells, a major cause for the initiation of carcinogenesis [6,8]. Although marine macroalgae exhibit great interest because of their bioactive properties for human health, they are considered as an underexploited resource [9,10]. Macroalgae (also known as seaweeds) along with seagrasses are the main primary producers and have an essential role in the structure and function of coastal and estuarine environments [11,12]. They are characterized by the formation of productive communities with great biodiversity [12]. Macroalgae are used in various applications such as environmental indicators of water quality, feasible alternatives to fossil fuels, and fertilizers [13,14]. In addition, there is currently great research interest for macroalgae as an important source for human nutrition [15]. Microalgae contain a variety of cellular components such as proteins, cellulose, polysaccharides, minerals, and phenolic compounds, which exhibit beneficial properties for human health such as antioxidant, anticancer, antibacterial, antimicrobial, antifungal, and antihypertensive [13]. The interspecific variation in macroalgal biochemical composition is expected, as macroalgae belong to different phylogenetic groups (i.e., Phaeophyceae, Rhodopyta, Chlorophyta), as well as functional form groups (i.e., filamentous, coarsely-branch, sheet, thick-leathery), which determine their physiological processes [13,16,17,18]. Additionally, as macroalgae grow worldwide, they are exposed to various abiotic and biotic environmental stresses that stimulate the production of bioactive components such as polyphenols, fatty acids, sterols, and carbohydrates [13,17,18]. In the Mediterranean Sea, which is characterized by high biodiversity, 1351 taxa of benthic macroalgae have been recorded, corresponding to $16.2\%$ of all macroalgae worldwide [19]. The Greek coasts, a major part of the Eastern Mediterranean Sea, are inhabited by flora species belonging to different geographic affinities (e.g., endemic, eastern Atlantic temperate, Indopacific tropical) [20]. In addition, the Greek coasts along with Turkish coasts present the highest macroalgal biodiversity in the Eastern Mediterranean Sea due mainly to different oceanographic or geomorphological characteristics of coastal waters [21]. The taxonomical group of red algae dominates in terms of diversity in the macroalgae found in Greek coasts [22]. The Greek macroalgae include in total about 550 taxa [23,24]. Especially, in Greek coasts, benthic macroalgal species are more frequently found in the North and South Aegean Sea and in shallow sheltered body types (10–45 species/0.04 m2) [23,24]. The Aegean Sea is an elongated embayment of the Mediterranean Sea and covers an area of about 215,000 km2. Since the Aegean Sea has great biodiversity of marine organisms including endemic and rare species, it is considered an important area to study marine resources [25]. For example, Montalvao et al. [ 25] examined 72 macroalgae species collected from the Turkish coast of the Aegean Sea for their inhibitory activity against growth of prostate and breast cancer cells. They found that the most potent species were Cystoseira barbata, Cystoseira crinita, Cystoseira stricta, Dictyopteris membranacea, Hypnea musciformis, Laurencia papilossa, and Sargassum vulgare [25]. In another study [26], the antioxidant activity of five brown macroalgae species from the Aegean Sea (Izmir coast, Turkey) was examined. Moreover, Guner et al. [ 27] examined the antioxidant activity and cytotoxicity against liver cancer cells of Cystoseira compressa collected from the Turkish Coast of Urla in the Aegean Sea. In these studies, the main metabolites found in macroalgae were polyphenols, phenols, terpenes, hydrocarbons, and aldehydes [26,27]. However, since only a few studies on the antioxidant and anticancer compounds of macroalgae from the Aegean Sea have been conducted so far, more research is needed. Thus, the aim of the present study was to investigate the chemopreventive potential (i.e., antioxidant activity and inhibition of cancer cell growth) of extracts of fourteen marine macroalgae species collected from the Northern Aegean Sea, Greece. Specifically, the most abundant and dominant seaweed species in the collection area at the sampling period were examined. The collection of abundant species was also necessary, since significant extracts’ amounts were required to perform all the assays. The collected species belonged, for comparison reasons, to all three phylogenetic (i.e., Chlorophyta, Phaeophyceae, and Rhodophyta) and functional form groups of macroalgae. It should be noted that most of these species (e.g., Ulva rigida, Ulva intestinalis, Codium fragile, Gracilaria gracilis, G. bursa pastoris, Gracilaria sp., Cystoseira barbata, and Padina pavonia) were also recorded during other sampling periods from our research group in the collection area [16,17,28,29]. The antioxidant activity was assessed in vitro using free radical scavenging, reducing power (RP), and protection from ROS-induced DNA plasmid breakage assays. In addition, macroalgae extracts’ inhibitory activity against liver cancer cell growth was assessed. Moreover, macroalgae extracts’ total polyphenolic content (TPC) values were evaluated. Correlation analysis was also performed between the different bioactivities and TPC values. Most of the tested macroalgae species collected from the Aegean Sea have never been investigated previously for their chemopreventive activities. ## 2.1. Marine Macroalgae Species Collection Samples of fourteen dominant marine macroalgae species were collected from June to September 2020 from the Thermaikos Gulf (Thessaloniki, Greece) and the Monolimni lagoon (Evros River Delta, Greece), Northern Aegean Sea, Mediterranean Sea (Figure 1, Table 1). More specifically, fourteen seaweed species (entire thalli) were collected from four stations, namely, St1 (48°58′97.37″ Ν 22°94′41.42″ Ε, with two substations St1.1 and St1.2), St2 (40°40′64.46″ N, 22°89′34.38″ E), St3 (40°30′12.5″ N 22°51′25.1″ E) of the Gulf of Thessaloniki (Figure 1, Table 1), and St4 (40°45′ N, 26°01′ E) at the outer part of the lagoon of Evros River Delta (Figure 1, Table 1). Rhodophyta (i.e., red macroalgae) were represented by five species, namely, Gigartina pistillata, Gigartina teedei, Gracilaria gracilis, Gracilaria sp., and Gracilaria bursa pastoris (S.G.Gmelin) P.C. Silva; Phaeophyceae (i.e., brown macroalgae) by six species, namely, Colpomenia sinuosa, Cystoseira amentacea, Cystoseira barbata, Cystoseira compressa, Sargassum vulgare, and Padina pavonica; and Chlorophyta (i.e., green macroalgae) by three taxa, namely, Codium fragile, Ulva intestinalis, and Ulva rigida (Table 1). The nomenclature and classification of organisms were based on the following floral catalogs and studies [23,24,30,31,32,33,34,35,36,37,38,39,40]. Two of these species belonged to the sheet functional group, five species to the coarsely branched group, and seven species to the thick–leathery group (Table 1) [16,17,23,24,30,31,32,33,34,35,36,37,38,39,40,41]. At the sampling stations, the marine macroalgae species were randomly collected by hand, wearing gloves, directly from the substrate, using a spatula, from 50–70 cm of depth. Samples of the same species collected from a common station were pooled, having a total biomass ranging from 300 to 15,000 g wet wt. Then, they were rinsed in seawater and transported to the laboratory in large containers (50 L) with seawater from the collection area. In the laboratory, the macrophyte species were identified to the lowest possible taxon [23,24,30,31,32,33,34,35,36,37,38,39,40]. Subsequently, they were washed with double distilled water, and any epiphyte, dead thalli part, and sediment were carefully removed with nylon brushes. They were dried at 50 °C for 48 h in the oven (Friocell, MMM Medcenter Einrichtungen GmbH; Munich, Germany) to constant weight and ground using an agate mill (MixerMill MM200, Retsh; Haan, Germany). ## 2.2. Extract Preparation The isolation of the extracts from the marine macroalgae was made according to Farasat et al. [ 42] with modifications. After grinding, macroalgae were soaked for extraction in $80\%$ methanol solution (1:30 dried weight sample to solvent volume), elaborated with a UP400S Hielscher sonicator (Teltow, Germany) at 20 cycles and $70\%$ amplitude for 20 min, and left in a shaker incubator (Innova® 40, New Brunswick Scientific; St Albans, UK) at 25 °C and 150 rpm for 48 h. Afterwards, the extract solutions were filtered using Whatman filter paper (0.45 μm). The solvent was removed under reduced pressure by a rotary evaporator (IKA, Werke RV-06-ML; Staufen, Germany) at 30 °C and 150 rpm, followed by freeze drying (CoolsafeTM, Scanvac; Allerod, Denmark) for 24 h, so as to produce an extract in the form of a powder. The dried powder was weighed to evaluate the percentage yield of the extraction process using the following equation:Extraction yield (%) = [dry extract (g)/dry seaweed (g)] × 100[1] The extracts were kept at −20 °C until further use. ## 2.3. Assessment of Macroalgae Extracts’ Polyphenolic Contents Macroalgae extracts’ TPC values were evaluated spectrophotometrically at 765 nm by using the Folin–Ciocalteu reagent as described previously [43]. TPC was determined by a standard curve of absorbance values in correlation with standard concentrations (50–1500 μg/mL) of gallic acid. The TPC was expressed as mg of gallic acid equivalents (GAE) per g of dry weight (dw) of extract. Moreover, HPLC-DAD analysis was performed to identify individual polyphenols and simple phenols in macroalgae extracts. Analysis by HPLC was performed on an ECOM analytical HPLC instrument, model ECS05 (Prague, Czech Republic), consisting of a quaternary gradient pump (ECP2010H) and a gradient box with a degasser (ECB2004) coupled with a diode array detector (ECDA2800 UV-Vis PDA Detector). Chromatographic separation of the samples was carried out on a Fortis SpeedCore column (C18, 2.6 um, 100 × 4.6 mm) (Cheshire, United Kingdom). Millipore water acidified with $0.1\%$ formic acid (A) and methanol (B) was utilized as the elution system, with a total flow rate of 1 mL/min. The elution gradient started with $90\%$ A, which remained constant for 5 min, and at 8.5 min it was set to $72\%$ A and at 30 min to $40\%$ A; this remained constant for 3 min. After each injection, the system was equilibrated for 3 min at the initial conditions. The column temperature was set at 25 °C, and the injection volume was 10 μL. The detection of the peaks was performed at 280, 270, 328, and 318 nm. Data were processed by using Clarity Chromatography Software v8.2 (DataApex Ltd., Thessaloniki, Greece) For identifying phenolic compounds in macroalgae samples and to later proceed with the quantification, the following mixture of standards was used: caftaric acid, caffeic acid, epigallocatechine gallate, p-coumaric acid, chicoric acid, trans-ferulic acid, quercetin, sinapic acid, rutin, and trans-cinnamic acid (Merck, Darmstadt, Germany). Standards were diluted in methanol and analyzed at 280, 270, 328, and 318 nm. The mixture of standards at a concentration range from 0.78 to 200 ppm was used for the construction of each calibration curve. Analyses of the phenolic contents were carried out in the macroalgae extracts at 7000 ppm concentration in methanol and were identified by the standards. ## 2.4. DPPH Radical Scavenging Assay The 2,2-diphenyl-picrylhydrazyl (DPPH•) assay was performed as described previously [43]. In brief, different concentrations of macroalgae extract in aqueous solution were added to 1.0 mL of methanolic solution of DPPH• radical (100 μM). Specifically, each macroalgae extract was dissolved in double distilled water to make stock solutions (300 mg/mL). These stocks were used for achieving different extract concentrations by making serial dilutions. One hundred μL was added from each extract concentration to the reaction mixture, having a total volume of 1 mL. After mixed by vortexing, the samples were incubated at room temperature in the dark for 20 min, and the absorbance was measured at 517 nm. The measurement was conducted on a Perkin Elmer Lambda 25 UV/VIS spectrophotometer (Waltham, MA, USA). In each experiment, the tested sample alone in methanol was used as a negative control. These negative controls were used to avoid the possible interference of the extract’s absorbance by itself, with the absorbance measured by the assay. The absorbance of these negative controls was subtracted by the absorbance of the corresponding samples. DPPH• alone in methanol was used as a control. Ascorbic acid was used as a positive control for the antioxidant activity. The percentage of radical scavenging capacity (RSC) of the tested extracts was calculated according to the following equation: RSC (%) = [(Acontrol − Asample)/Acontrol] × 100[2] where Acontrol and Asample are the absorbance values of the control and the sample, respectively. The IC50 value showing the concentration that caused $50\%$ scavenging of the DPPH• and ABTS•+ radical was calculated from the graph, plotted as RSC percentage against the extract concentration. All experiments were carried out in triplicate and at least on three separate occasions. ## 2.5. ABTS•+ Radical Scavenging Assay The 2,2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS•+) radical scavenging assay was carried out as described previously [43]. In brief, the ABTS•+ radical was generated by mixing 2 mM ABTS with 30 μM H2O2 and 6 μM horseradish peroxidase (HRP) enzyme in 1 mL of distilled water. The reagents were mixed and incubated at room temperature in the dark for 45 min. Each macroalgae extract was dissolved in double distilled water to make stock solutions (300 mg/mL). These stocks were used for achieving different extract concentrations by making serial dilutions. Then, 10 μL of different extract concentrations in aqueous solution were added in the reaction mixture, and the absorbance at 730 nm was read. In each experiment, the tested sample in distilled water containing ABTS and H2O2 was used as a negative control. These negative controls were used to avoid the possible interference of the extract’s absorbance by itself, with the absorbance measured by the assay. The absorbance of these negative controls was subtracted by the absorbance of the corresponding samples. The ABTS•+ radical solution with 10 μL H2O was used as control. Ascorbic acid was used as a positive control for the antioxidant activity. The percentage of RSC of the tested extracts was calculated as described above for the DPPH assay. At least three independent experiments were performed for each tested compound. ## 2.6. Hydroxyl Radical Scavenging Assay Hydroxyl radical (•OH) scavenging activity was determined as described previously [44]. In brief, 75 μL of extract dissolved in distilled water at different concentrations was added to 450 μL sodium phosphate buffer (0.2 M, pH 7.4), 150 μL 2-deoxyribose (10 mM), 150 μL FeSO4-EDTA (10 mM), 525 μL H2O, and 150 μL H2O2 (10 mM), and the samples were incubated at 37 °C for 4 h. After incubation, 750 μL trichloroacetic acid (TCA) ($2.8\%$) and 750 μL 2-thiobarbituric acid ($1\%$) were added, and the samples were incubated at 95 °C for 10 min. The samples were cooled on ice for 5 min and centrifuged at 3000 rpm for 10 min at 25 °C. The absorbance was measured at 520 nm. In each experiment, the samples without H2O2 were used as negative controls. These negative controls were used to avoid the possible interference of the extract’s absorbance by itself with the absorbance measured by the assay. The absorbance of these negative controls was subtracted by the absorbance of the corresponding samples. The samples without extract were used as controls. Ascorbic acid was used as a positive control for the antioxidant activity. The OH• radical scavenging activity was calculated according to the following equation:•OH radical scavenging activity (%) = [(Abscontrol – Abssample)/Abscontrol] × 100[3] where Abscontroland Abssample are the absorbance values of the control and the tested sample, respectively. At least three independent experiments were performed for each tested compound. ## 2.7. Superoxide Radical Scavenging Assay The superoxide anion radical (O2•−)-scavenging activity of the extracts was evaluated as described previously [45] with minor modifications. Specifically, in this method, O2•− radicals are produced by the phenazine methosulfate and reduced nicotinamide adenine dinucleotide (PMS-NADH) system by NADH oxidation, and then they reduce nitroblue tetrazolium (NBT) to formazan, which is measured spectrophotometrically at 560 nm. Antioxidants may scavenge O2•−, consequently reducing absorbance. For this assay, the macroalgae extracts were dissolved at different concentrations in Tris-HCl of 16 mM (pH 8.0), which was the buffer. More specifically, 125 μL of NBT2+ (300 μΜ), 125 μL of NADH (468 μΜ), and 10 μL of extracts (diluted in the buffer) were added into 615 μL of Tris-HCl (16 mM; pH 8.0). The reaction was initiated by the addition of 125 μL of PMS (60 μΜ) to the mixture. The samples were incubated for 5 min in the dark, and the absorbance was monitored at 560 nm on a Perkin Elmer Lambda 25 UV/VIS spectrophotometer (Waltham, MA, USA). In each measurement, a blank containing 750 μL of Tri-HCl buffer, 125 μL of NBT, and 125 μL of NADH, and a control containing 625 μL of Tri-HCl buffer, 125 μL of NBT, 125 μL of NADH, and 125 μL of PMS were used. Moreover, in each experiment, negative controls were used containing 740 μL of Tri-HCl buffer, 125 μL of NBT, 125 μL of NADH, and 10 μL of extract diluted in buffer. These negative controls were used to avoid the possible interference of the extract’s absorbance by itself with the absorbance measured by the assay. The absorbance of these negative controls was subtracted by the absorbance of the corresponding samples. The RSC and the IC50 values for O2•− were evaluated as mentioned above for the DPPH• radical. At least three independent experiments were performed for each tested compound. ## 2.8. RP Assay Reducing power was determined spectrophotometrically as described previously [44] with minor modifications. In this assay, the macroalgae extracts were dissolved in phosphate buffer (0.2 M, pH 6.6) at different concentrations. Two hundred and fifty microliters of the extract solution was added to 250 μL of potassium ferricyanide ($1\%$ w/v in dH2O) and incubated at 50 °C for 20 min. After incubation, the samples were cooled on ice for 5 min. Then, 250 μL of TCA (10 w/v) was added, and the samples were centrifuged (1700 g, 10 min, 25 °C). Subsequently, 250 μL of distilled H2O and 50 μL of ferric chloride ($0.1\%$ w/v) were added to the supernatant, and the samples were incubated at room temperature (RT) for 10 min. The absorbance was monitored at 700 nm on a Perkin Elmer Lambda 25 UV/VIS spectrophotometer (Waltham, MA, USA). In each measurement, a blank containing 500 μL of phosphate buffer, 250 μL of TCA, 250 μL of dH2O, and 50 μL of ferric chloride, and a control containing 250 μL of buffer, 250 μL of potassium ferricyanide, 250 μL of TCA, 250 μL of dH2O, and 50 μL of ferric chloride were used. Moreover, in each experiment, negative controls were used containing 250 μL of buffer, 250 μL of TCA, 250 μL of dH2O, and 50 μL of ferric chloride and 250 μL of extract diluted in buffer. These negative controls were used to avoid the possible interference of the extract’s absorbance by itself with the absorbance measured by the assay. Ascorbic acid was used as a positive control for the RP activity. The absorbance of these negative controls was subtracted by the absorbance of the corresponding samples. The RP0.5AU value showing that the extract concentration caused an absorbance of 0.5 at 700 nm was calculated from the graph plotting absorbance against extract concentration. At least three independent experiments were performed for each tested compound. ## 2.9. ROS-Induced DNA Plasmid Strand Cleavage Assay The ROS-induced DNA plasmid strand cleavage assay was performed as described previously [18]. At least three independent experiments were performed for each tested compound. ## 2.10. Evaluation of Relative Antioxidant Capacity Index (RACI) The assessment of the order of the antioxidant potency of macroalgae extracts, taking into account their activity in all antioxidant assays, was based on the evaluation of the RACI for each extract, as described previously [46]. Since RACI estimation was based on IC50 values and RP0.5AU values, the lower the RACI value was, the higher the antioxidant capacity was. ## 2.11. Cell Culture Conditions The human liver HepG2 cancer cell line was obtained from Dr. Anna-Maria Psarra (University of Thessaly, Larissa, Greece). The cells were cultured in normal Dulbecco’s modified Eagle’s medium (DMEM; Gibco, Horsham and Loughborough, UK) containing $10\%$ (v/v) fetal bovine serum, 2 mM L-glutamine (Gibco, Horsham and Loughborough, UK), 100 units/mL of penicillin, and 100 units/mL of streptomycin (Gibco, Horsham and Loughborough, UK) in plastic disposable tissue culture flasks at 37 °C in $5\%$ CO2. ## 2.12. XTT Assay for Inhibition of Cell Proliferation The inhibition of cell proliferation was assessed using the XTT assay kit (Roche, Germany), as described previously [43]. Briefly, 1 × 104 cells were subcultured into a 96-well plate in DMEM medium. After 24 h incubation, the cells were treated with different concentrations of macroalgae extracts in serum-free DMEM medium for 24 h. Then 50 μL of XTT test solution, which was prepared by mixing 50 μL of XTT-labeling reagent with 1 μL of electron coupling reagent, was then added to each well. After 4 h of incubation, absorbance was measured at 450 nm and also at 690 nm as a reference wavelength on a Perkin Elmer EnSpire Model 2300 Multilabel microplate reader (Waltham, MA, USA). Cells cultured in DMEM serum-free medium were used as a negative control. Additionally, the absorbance of each extract concentration alone in DMEM serum-free medium and XTT test solution was tested at 450 nm. The absorbance values shown by the extracts alone were subtracted from those derived from cancer cell treatment with extracts. Data were calculated as percentage of inhibition by the following formula:Inhibition (%) = [(O.D.control − O.D.sample)/O.D.control] × 100[4] where O.D.control and O.D.sample indicated the optical density of the negative control and the tested substances, respectively. The concentration of macroalgae extracts causing $50\%$ cellular proliferation inhibition (IC50) of cancer cells was calculated thereafter from the graph plotted percentage inhibition against extract concentration. All experiments were carried out at least on three separate occasions in triplicate. ## 2.13. Statistical Analysis All results were expressed as mean ± SD. For statistical analysis, one-way ANOVA was applied followed by Dunnett’s test for multiple pair-wise comparisons. Dose–response relationships were examined by Spearman’s correlation analysis. Spearman’s correlation was also used to determine the correlation between the values of different bioactivity assays and TPC values. Correlation coefficients whose magnitudes were less than 0.49, from 0.5 to 0.69, and from 0.7 to 1.0 were considered as having low, medium, and high correlations, respectively. Differences were considered significant at $p \leq 0.05.$ All statistical analyses were performed with the SPSS software (version 14.0; SPSS). In order to identify clusters of closely related macroalgae species, in terms of their overall bioactivities, dendrograms and principal component analysis (PCA) were performed. Clustering was based on seven measures (i.e., DPPH•, ABTS•+, OH•, O2•−, RP, DNA plasmid breakage, and XTT assay for HepG2 cells). Dendrograms were generated using the Euclidian Distance metric and the WPGMA algorithm, with the Scipy python package [47]. PCA was conducted with the scikit-learn package [48] for two components using default parameters. Dendrograms and PCA plots were generated with the plotly Python package (Plotly Technologies Inc., Collaborative data science, Montréal, QC, Canada, 2015. https://plot.ly; accessed on 30 January 2023), for raw as well as normalized data, based on the Z-score transformation. All the above clustering analyses were initially conducted for all fourteen macroalgae. However, after one outlier sample was detected (i.e., C. fragile), all the above analyses were also repeated for the remaining thirteen samples. ## 3.1. Extraction Yield and Assessment of Polyphenolic Content The extraction yields ranged from $18.0\%$ (U. rigida) to $46.7\%$ (Codium fragile) (Table 2). The average of extraction yield was 24.9 ± $7.8\%$. Only two species exhibited significant deviation from the mean value, that is, the green macroalgae C. fragile ($46.7\%$) and the brown macroalgae C. amentacea ($34.1\%$). Moreover, on average, we did not observe significant differences in the yields between red, brown, and green macroalgae. Since the same extraction method was used for all macroalgae, differences in extraction yield may be due to differences in the macroalgae’s chemical composition [16,49]. Although the comparison of extraction yield between different studies is difficult due to various methods and solvents used, our yield values were comparable with those of other studies. For example, G. gracilis’ yield of extraction using hot water was $24.63\%$ [50], being close to our yield of $25.4\%$ after $80\%$ v/v methanol extraction. In addition, our C. amentacea’s extraction yield of $34.1\%$ was similar to the yield of $31\%$ of macroalgae after $50\%$ v/v ethanol extraction [51]. Our C. barbata’s yield of $22.4\%$ was also close to the yield of $24.31\%$ of extract obtained in $70\%$ v/v acetone [52]. However, intriguingly, $100\%$ v/v methanol was used for extraction, C. barbata’s yield was too low ($3.8\%$) [52]. Since algal polyphenols are known for their antioxidant and/or anticancer activities [7], macroalgae extracts’ TPC values were assessed. The results showed that the extracts had low TPC values and ranged about 23-fold, from 0.55 to 12.53 mg GAE/g dw of extract (Table 2). The G. teedei extract exhibited the highest TPC value (12.53 mg GAE/gr dw) followed by C. barbata (5.76 mg GAE/gr dw) and U. rigida (4.15 mg GAE/gr dw) (Table 2). Two extracts had TPC values below 1 mg GAE/g dw of extract, six extracts from 2 to 3 mg GAE/g dw, three extracts from 3 to 4 mg GAE/g dw, and three extracts above 4 mg GAE/g dw (Table 2). The TPC values of several macroalgae were similar to those found in other studies. For example, Francavilla et al. [ 53] reported that the TPC of a G. gracilis methanolic extract was 2.3 mg GAE/dw, that is, it was very close to our methanolic extract (3.01 mg GAE/dw). Interestingly, Francavilla et al. [ 53] collected G. gracilis from the Mediterranean Sea (Southern Adriatic Sea, Lesina, Italy) like us. However, in the aforementioned study, TPC values varied significantly between different solvents used for extraction. For example, their ethyl acetate extract had a TPC of ~65 mg GAE/dw [53]. Moreover, Sapatinha et al. [ 50] demonstrated that G. gracilis extracts had TPCs from 28.2 to 50.73 mg GAE/dw, but they used different solvents than we did. Furthermore, the TPC of methanolic P. pavonica extract was 0.96 GAE/dw [54], a value close to our result (2.77 mg GAE/gr dw). However, P. pavonica was also reported to contain a higher TPC (27 mg GAE/gr dw) than our value, but acetone was used for the extraction instead of methanol [55]. In addition, S. vulgare extract was demonstrated to have a TPC of 6.60 mg GAE/g dw, being about 3-fold higher than that of our extract, but dichloromethane along with methanol was used for extraction [56]. Additionally, De La Fuente et al. [ 51] reported a TPC of 20.3 mg GAE/g dw for C. amentacea methanolic extract, while our extract had a much lower value (2.54 mg GAE/g dw). Moreover, methanolic C. compressa extract contained 0.161 mg GAE/g dw [27], which was much lower than that of our extract (2.93 mg GAE/g dw). Interestingly, Guner [27] also collected C. compressa from the Aegean Sea (Coast of Urla, Izmir, Turkey), but from a different region than ours. In addition, another study showed that the TPC of C. compressa extracts varied according to season from 48.2 to 83.4 mg GAE/g [57], that is, the values were higher than our finding (2.93 mg GAE/g dw). In that case, although the same solvent as ours was used, microwave extraction was carried out [57], a method completely different than that used in our study. In general, apart from the extraction method [50,53], several other factors may affect macroalgae’s TPC values, such as seasonality [57], local environmental conditions (e.g., salinity, nutrient availability, UV irradiation, and light), and geographical location [50,58,59,60]. Neto et al. [ 61] showed U. rigida extracts’ TPCs to range from 1.6 to 5.3 mg GAE/g dw, depending on the extraction method. These values were close to that of U. rigida in our study (4.15 mg GAE/g dw). Additionally, Megzhani et al. [ 62] reported U. rigida extract’s TPCs (3.29 mg GAE/g dw) to be similar to this study, although their ethanolic extract exhibited a higher value (8.09 mg GAE/g dw). In addition, Farasat et al. [ 42] used exactly the same extraction method as us for U. intestinalis and found a similar TPC value (i.e., 1.98 vs. 2.11 mg GAE/g dw). A U. intestinalis ethanolic extract was demonstrated to contain a TPC of 1.15 mg GAE/g dw [63], similar to our value. However, the same researchers showed a higher TPC (11.27 mg GAE/g dw) of U. intestinalis than this study when samples were treated by ultrasonication [63]. In another study, the accelerated solvent method was used for U. intestinalis extraction, and the TPC value was 5 mg GAE/g dw [64]. The green macroalgae C. fragile was also shown previously to have a TPC of 0.99 mg GAE/g dw [65], similar to this study (0.55 mg GAE/g dw). For identifying individual compounds accounting for the observed bioactivities of the macroalgae extracts, the presence of ten polyphenols or simple phenols was investigated with HPLC-DAD analysis. These phenols were caftaric acid, caffeic acid, epigallocatechine gallate, p-coumaric acid, chicoric acid, trans-ferulic acid, quercetin, sinapic acid, rutin, and trans-cinnamic acid. These polyphenols were examined, since all of them have been found in macroalgae species, including those tested in the present study [66,67,68,69,70]. The results showed that none of the macroalgae extracts’ chromatograms showed detectable peak areas of the standard phenols (Supplementary Figure S1). Thus, the polyphenols or phenols under investigation were not contained in any of the tested macroalgae extracts. In another study, the results of HPLC-MS/MS carried out for identifying compounds of C. amentacea extract showed as main components meroditerpene-like structures [51]. Moreover, Caf et al. [ 54], in agreement with our study, did not identify rutin using HPLC in P. pavonica collected from the Eastern Mediterranean Sea (Lara coast, Antalya, Turkey) like our sample. However, unlike our study, they detected quercetin, but its amount was low (0.013 μg/g dw extract) [54]. Other phenols identified by Caf et al. [ 54] in P. pavonica were myrisetin (0.034 μg/g dw), morin (0.011 μg/g dw), naringenin (0.065 μg/g dw), and resveratrol (0.11 μg/g dw), while kaempferol and naringin were not found. In another study on P. pavonica collected from the Mediterranean Sea (coast of Ciovo Island, Croatia), unlike our extract, p-coumaric and trans-ferulic acids were identified in extracts derived from different methods, but at low amounts ranging from 0.02 to 0.88 mg/L and from 0.07 to 1.22 mg/L extract, respectively [71]. These extracts were obtained using a method (ultrasound-assisted extraction in ethanol or water) [71] different to what was used in our study. Other polyphenols identified in this study were protocatechuic acid (from 1.05 to 1.70 mg/L extract) and p-hydroxybenzoic acid (from 0.51 to 0.76 mg/L extract) [71]. Furthermore, an interesting study used quantitative 1H NMR (qNMR), a very sensitive method compared to HPLC-DAD, for polyphenols’ identification in U. intestinalis and, similar to our results, did not find chicoric acid but identified small amounts of sinapic acid, ferulic acid, p-coumaric acid, quercetin, caffeic acid, gallic acid, luteolin, apigenin, and diosmetin [64]. They also concluded that polyphenols’ identification from marine macroalgae presents many difficulties due to there being complex samples and polyphenols’ presence at low concentrations [64]. The differences in polyphenols’ determination between our study and other studies may be attributed to different factors such as geographical location and season of collection, as well as extraction and chemical analysis methods [50,51,56]. ## 3.2. Free Radical Scavenging Activity Since oxidative stress has been shown to be a causative factor for cancer [72], macroalgae extracts’ scavenging abilities against four different free radicals (i.e., DPPH, ABTS•+, •OH, and O2•−) were determined. The IC50 values of all assays are shown in Table 2. The lower the IC50 value, the higher the antioxidant activity. In DPPH. and ABTS•+, as expected, ascorbic acid as a pure compound had lower IC50 values than the extracts. Interestingly, in the •OH assay, G. pistillata, G. teedei, and C. amentacea exhibited better scavenging activity than ascorbic acid. In the O2•− assay, ascorbic acid could not be tested because it can reduce NBT [73]. In DPPH assay, the macroalgae species’ IC50 values ranged from 0.31 to 79.00 mg/mL (Table 2). The three most potent macroalgae species against DPPH• scavenging were G. teedei (IC50: 0.31 mg/mL), C. barbata (IC50: 1.40 mg/mL), and G. pistillata (IC50: 2.10 mg/mL) (Table 2). Our results were partly similar to those of other studies, but different findings from ours have also been reported. For example, Francavilla et al. [ 53] demonstrated that the G. gracilis’ IC50 value against DPPH. varied according to the solvent used for the extraction and season of macroalgae collection. When methanol was used for extraction, IC50 values ranged from 2.94 to 9.72 mg/mL [53], similar to our study (13.5 mg/mL). However, Sapatinha et al. [ 50] and Zubia et al. [ 74] reported G. gracilis’ IC50 values of ~80 and 42.27 mg/mL, respectively, in the DPPH. assay, which were significantly higher than our IC50 values. Moreover, C. amentacea extracts isolated with $50\%$ v/v ethanol and dimethyl sulfoxide (DMSO) exhibited in the DPPH assay IC50 values of 205.1 and 0.34 μg/mL, respectively [51], which were much lower than in this study (2.5 mg/mL). Kosanic et al. [ 75] reported an DPPH. IC50 value of 409.81 μg/mL of the C. amentacea extract, which was also lower than this study, but they used a different solvent and method (i.e., acetone in a Soxhlet extractor) for extract isolation than us. Additionally, C. barbata’s IC50 values in DPPH. assay varied according to solvent used for extraction from 0.088 to 0.564 mg/mL [52,75], being lower than in this study (1.4 mg/mL). Guner et al. [ 27] demonstrated DPPH. IC50 values of 15.94, 5.00, and 7.46 mg/mL of C. compressa extracts isolated using methanol, hexane, and chloroform, respectively, which were higher than our IC50 (2.9 mg/mL). Interestingly, Guner et al. [ 27] collected C. compressa from the Aegean Sea like us. However, Mhadhebi et al. [ 76] documented a DPPH. IC50 value of 0.012 mg/mL for C. compressa collected from the Tunisian coastline in the Mediterranean Sea. Kosanic et al. [ 75] also reported C. compressa’s DPPH. IC50 value of 812.22 μg/mL. De La Fuente et al. [ 56] attributed C. compressa’s antioxidant activity, at least in part, to a sulphated polysaccharide extract with a DPPH. IC50 of 142.5 μg/mL. P. pavonica isolated in $95\%$ v/v ethanol was shown in the DPPH. assay to possess an IC50 of 5.59 μg/mL [77], a value much lower than our result (6.5 mg/mL). Chouh et al. [ 78] demonstrated a DPPH. IC50 value of 97.41 μg/mL of S. vulgare extract, which was also lower than our value (8.2 mg/mL). However, they used $70\%$ v/v acetone for extraction [50] instead of $80\%$ v/v methanol, which we used. Interestingly, De La Fuente et al. [ 56] showed an extract of sulphated polysaccharides from S. vulgare from the Mediterranean Sea to exhibit an IC50 of 695.5 μg/mL. Mezghani et al. [ 62] reported DPPH. IC50 values of U. rigida ranging from 204.08 to 500 μg/mL, depending on the extraction solvent, while our value was 5.5 mg/mL. A methanolic extract of U. intestinalis was demonstrated to have a DPPH. IC50 value of 1.88 mg/mL [42], while our value was 10 mg/mL. Macroalgae extracts’ IC50 values against ABTS•+ scavenging were from 0.02 to 15.00 mg/mL (Table 2). Among tested algae extracts, G. teedei (IC50: 0.024 mg/mL), G. pistillata (IC50: 0.16 mg/mL), and P. pavonica (IC50: 0.38 mg/mL) exhibited the lowest IC50 values. Trifan et al. [ 52] documented C. barbata extracts’ ABTS•+ IC50 values to range from 13.9 to 22.1 μg/mL, while our value was higher (0.43 mg/mL). G. gracilis’ IC50 values in the ABTS assay ranged from ~15 to 30 mg/mL depending on the solvent and method used for extraction [50], while our value was lower (1.45 mg/mL). Chouh et al. [ 78] demonstrated S. vulgare extract’s ABTS IC50 value of 72.9 μg/mL, which was lower than ours (1.4 mg/mL). Moreover, all macroalgae extracts scavenged the •OH radical with IC50 values ranging from 0.10 to 10.00 mg/mL (Table 2). In this assay, the macroalgae species exhibiting the highest activity were G. teedei (IC50: 0.10 mg/mL), G. pistillata (IC50: 0.14 mg/mL), and C. amentacea (IC50: 0.16 mg/mL). De La Fuente et al. [ 51] documented, in an •OH assay, IC50 values of 0.29 and 0.45 mg/mL for C. amentacea extracts isolated with $50\%$ v/v ethanol and DMSO, respectively, which were close to our value (0.16 mg/mL). In addition, in the O2•− radical scavenging assay, macroalgae extracts’ IC50 values ranged from 0.05 to 6.40 mg/mL (Table 2). In this assay, the three most potent extracts were G. teedei (IC50: 0.05 mg/mL), G. pistillata (IC50: 0.07 mg/mL), and G. bursa pastoris (IC50: 0.14 mg/mL) (Table 2). Unlike all the other scavenging assays, two macroalgae species, that is, U. rigida and U. intestinalis, could not achieve IC50 values at the tested concentrations. Actually, it was not possible to determine IC50 values for these two species. The reason was that at concentrations higher than 0.2 mg/mL, their extracts formed a precipitate, probably due to a reaction of one of their compounds with the reaction mixture, which impeded absorbance measurement. Thus, at 0.2 mg/mL, U. rigida scavenged O2•− by $32\%$, while the value for U. intestinalis was $43.20\%$. Chouh et al. [ 78] demonstrated, in an O2•− radical assay, IC50 value of >800 μg/mL of S. vulgare extract, while our value was 600 μg/mL. Kosanic et al. [ 75] reported, in an O2•− radical assay, for C. barbata, C. amentacea, and C. compressa extracts isolated with acetone in a Soxhlet extractor, IC50 values of 675.93, 521.45, and 976.62 μg/mL, respectively, while our values were 1.2, 1.4, and 1.1 mg/mL, respectively. It was remarkable that all extracts were less potent in DPPH. assays compared to the other three free radical scavenging assays. The solvent of the DPPH. assay is methanol, while the solvent of the other three assays is water. Thus, lipophilic compounds are mainly active in the DPPH. assay, while hydrophilic compounds are more potent in ABTS•+, •OH, and O2•− assays. Consequently, it may be concluded that the antioxidant compounds of the tested macroalgae extracts are mainly hydrophilic. Both DPPH. and ABTS•+ assays are based on synthetic radicals, but they consist of the most frequent methods used to determine the antioxidant activity of a compound [79]. On the contrary, •OH and O2•− radicals are formed naturally in the human organism [80]. The overproduction of O2•− within cells results in reactions with biological macromolecules, causing damage to cellular components and dysfunction of cell metabolism [72]. Moreover, intracellular superoxide dismutase (SOD) can catalyze O2•− to hydrogen peroxide (H2O2) reacting through the Fenton reaction with Fe2+, leading to formation of •OH that may cause DNA damage and cancer [72]. Thus, the identification of compounds being able to scavenge both •OH and O2•− radicals is of great importance for cancer prevention. Finally, it should be noted that in all free radical scavenging assays, there was a great variation in potency among the tested macroalgae extracts. However, it was obvious from the IC50 values in all assays that the two Gigartina species, G. teedei and G. pistillata, had higher free radical scavenging activity than the other extracts. ## 3.3. RP Activity Macroalgae extracts’ RP values were determined, since the ability of bioactive compounds to act as electron donors is considered as an indication of their capacity to neutralize free radicals [79]. In the RP assay, tested extracts’ RP0.5AU values ranged from 0.24 to 15 mg/mL (Figure 2). It should be noted that similar to IC50 values, the lower the RP0.5AU value, the higher the RP activity. The three species demonstrating the highest reducing activity were G. teedei (RP0.5AU: 0.24 mg/mL), C. barbata (RP0.5AU: 0.56 mg/mL), and C. compressa (RP0.5AU: 0.58 mg/mL) (Figure 2). Since ascorbic acid is a pure compound, it exhibited an RP0.5AU of 3.4 μg/mL (data not shown), being much lower compared to extracts. Like free radical scavenging assays, macroalgae extracts’ RP exhibited great variation. For example, G. teedei, the most potent extract, exhibited a 62.5 times greater reducing activity than C. fragile, the least potent extract. In general, the two Gigartina species along with the three Cystoseira species had RP0.5AU values below or equal to 1 mg/mL, the two Ulva species together with P. pavonica and S. vulgare had RP0.5AU from 1 to 2 mg/mL, while the three Gracilaria species, C. sinuosa and C. fragile, exhibited RP0.5AU values higher than 2 mg/mL. The fact that the G. teedei extract, like in all free radical scavenging assays, was the most potent in the RP assay confirmed the association between reducing activity and free radical neutralization. Therefore, the results suggested that the G. teedei extract’s antioxidant compounds may also be effective electron donors. In other studies, De La Fuente et al. [ 51] documented for C. amentacea extracts isolated with DMSO or $50\%$ v/v ethanol RP0.5AU values of 0.11 and 0.64 mg/mL, respectively. The latter value was comparable to our value (0.77 mg/mL). Chouh et al. [ 78] demonstrated an RP0.5AU value of >200 μg/mL of S. vulgare extract, while our value was 1.8 mg/mL. ## 3.4. Protection from ROS-Induced DNA Damage The evidence of macroalgae extracts’ antioxidant potential was further supported by their ability to protect from ROO•-induced DNA damage (Figure 3 and Figure 4). The IC50 values in this assay ranged from 0.038 to 1.8 mg/mL (Figure 4). The most potent extract, such as free radical scavenging assays, was G. teedei (IC50: 0.038 mg/mL) followed by G. pistillata (IC50: 0.25 mg/mL) and C. barbata (IC50: 0.32 mg/mL) (Figure 4). Interestingly, IC50 values of the DNA plasmid breakage assay were on average lower than IC50 values of all free radical scavenging assays and RP0.5AU values. The ROO• radicals used for DNA damage are usually produced in cells by the reaction of oxygen with radicals containing carbon atoms [81]. Then, after their entrance to the nucleus, they may cause DNA damage and diseases such as cancer [81]. To the best of our knowledge, this is the first study to demonstrate for the most of the tested macroalgae species’ extracts protection from ROS-induced DNA damage. Only for C. barbata was a sulphated polysaccharide extract reported to inhibit DNA damage caused by (•OH) at a concentration of 0.125 mg/mL [82], which was close to our IC50 value (0.32 mg/mL). Moreover, the U. rigida ethanolic extract was shown to protect bone marrow cells from genotoxicity [83]. Since it is well established that DNA damage is a crucial factor for cancer manifestation and progression [84], the identification of compounds protecting from ROS-induced DNA damage is of great importance. ## 3.5. Estimation of RACI Values Since for the assessment of macroalgae extracts’ antioxidant capacity six different antioxidant assays (i.e., DPPH, ABTS•+, •OH, O2•−, RP, and DNA plasmid strand cleavage) were used and in each assay the extracts’ potency order was different, it was difficult to find out which extract was the most potent. Thus, for estimating the macroalgae extracts’ potency order by combining the values of all the above assays, the RACI was estimated for each macroalgae species (Figure 5). The RACI estimation showed that its values ranged from −0.77 to 2.28. As mentioned, the lower the RACI value, the higher the antioxidant capacity. Thus, the most potent antioxidant extract was G. teedei (−0.77) followed by G. pistillata (−0.63), Cystoseira barbata (−0.35), and U. rigida (−0.34) (Figure 5). Moreover, these four species exhibited higher TPCs (Table 2), and so their polyphenolic amounts may account for their high antioxidant activity. Other studies have also shown that macroalgae’s antioxidant activity is attributed to their polyphenols [7,51,85]. For example, G. gracilis [53], C. amentacea, C. barbata, and C. compressa [75] extracts’ polyphenolic contents accounted for their antioxidant activity. Specifically, phlorotannins (e.g., phloroglucinol) and flavonoids have been demonstrated to be strong antioxidants in several macroalgae species such as S. vulgare, P. pavonica, and C. barbata [7,52,71,78]. Additionally, Trifan et al. [ 52] identified 18 phlorotannins in C. barbata extracts exhibiting antioxidant activity. These phlorotannins were mainly fucophlorethol and eckol derivatives, containing between three and seven phloroglucinol units [52]. Moreover, P. pavonica extracts contained polyphenols such as quercetin, resveratrol, trans-ferulic acid, and p-hydroxybenzoic acid, known for their antioxidant activity [86]. Chouh et al. [ 78] identified in S. vulgare 21 phlorotannins such as dibenzodioxine1,3,6,8-tetraol, fuhalol, pentaphlorethol, fucopentaphlorethol, and dihydroxypentafuhalol with antioxidant properties. G. pistillata has also been reported to contain antioxidant polyphenols such as (–)–epicatechin, protocatechuic acid, oleuropein, p-aminobenzoic acid, and tyrosol [87]. U. intestinalis extracts have been reported to contain antioxidant polyphenols such as sinapic acid, ferulic acid, p-coumaric acid, quercetin, caffeic acid, gallic acid, luteolin, apigenin, and diosmetin [64,86]. Further evidence of our results for polyphenols’ roles in the tested macroalgae’s antioxidant potency was that C. fragile and C. sinuosa extracts exhibiting the least antioxidant activity had also the lowest TPC values. Apart from polyphenols, other algal compounds have also been shown to possess antioxidant activity. Specifically, the most important macroalgae’s metabolites accounting for their antioxidant activity are phenols (e.g., phlorotannins, flavonoids, phenolic acids, and bromophenols), polysaccharides (e.g., carrageenans, sulfated polysaccharides, agar, fucoidan), fatty acids, phytosterols, proteins (e.g., phycobiliproteins), terpenoids (e.g., carotenoids, zeaxanthin), pigments (e.g., chlorophylls), and iodine [87]. For instance, sulfated polysaccharides (e.g., fucoidan and alginate) from C. sinuosa, C. barbata, and U. rigida exhibited antioxidant activity [82,88,89,90,91,92]. Red macroalgae such as Gigartina and Gracilaria species are also rich in carrageenans and sulfated polysaccharides, demonstrating antioxidant properties [93]. Specifically, G. pistillata has been found to contain carrageenans such as hybrid kappa-iota and xi-lambda carrageenans [94]. Moreover, sulfated polysaccharides with free radical scavenging activity have been identified in G. gracilis [95]. These polysaccharides consisted mainly of galactose, ribose, arabinose, and glucose [95]. In addition, the antioxidant activity of C. compressa’s extracts was attributed to polysaccharides such as fucoidan and monosaccharides such as fucose, galactose, and mannose [96]. In C. fragile, sulfated polysaccharides, which are mainly linear homopolymers comprising ß-1.4-linked D-mannose residues, were shown to possess antioxidant activity by promoting survival while decreasing ROS, cell mortality, and lipid peroxidation in a zebrafish experimental model [97]. Furthermore, C. compressa’s extracts exhibiting antioxidant activity were reported to contain a series of fatty acids such as oleic acid, palmitoleic acid (C16:1n-7), palmitic acid (C16:0), and ω-3 eicosapentaenoic acid (EPA) [57]. Cystoseira amentacea extracts’ antioxidant activity has been attributed to terpenoids such as meroditerpenes and linear diterpenes [51]. Moreover, P. pavonica, C. barbata, and other brown macroalgae contain phytosterols such as fucosterol, exhibiting antioxidant activity [52,55]. In addition, red macroalgae (e.g., G. gracilis) have been reported to contain phycobiliproteins having antioxidant properties [98], while in green macroalgae (e.g., U. intestinalis), antioxidant pigments such as astaxanthin have been found [99]. ## 3.6. Inhibition of Cancer Cell Growth Macroalgae extracts have been reported to possess anticancer activity [88]. Macroalgae’s main group of metabolites exhibiting anticancer properties are polysaccharides (e.g., sulfated polysaccharides and carrageenans), halogenated metabolites, phenols (e.g., bromophenols, polyphenols, and phlorotannins), pigments (e.g., pheophorbide A), iodine, lipids (e.g., sulfolipids), proteins (e.g., lectins), and terpenes (e.g., brominated terpenes and elatol) [88]. Thus, macroalgae extracts, apart from their antioxidant capacities, inhibited growth of liver HepG2 cancer cells (Figure 6). The macroalgae species’ IC50 values against HepG2 cell growth ranged from 0.93 to 9.70 mg/mL (Figure 6). The three macroalgae extracts that exhibited the highest inhibition against liver cancer cell proliferation were P. pavonica (IC50: 0.93 mg/mL), U. rigida (IC50: 1.40 mg/mL), and G. bursa pastoris (IC50: 1.40 mg/mL) (Figure 6). It should be noted that macroalgae species exhibiting high anticancer potential belong to all taxonomic groups, that is, Chlorophyta (e.g., U. rigida and U. intestinalis), Phaeophyceae (e.g., P. pavonica), and Rhodophyta (e.g., G. bursa pastoris and G. teedei). Importantly, G. teedei extract, demonstrating the highest antioxidant activity, was also included among the extracts having the greatest inhibition against the growth of cancer cells. This result indicated that the same compounds of G. teedei extract may account for both antioxidant and cancer cell growth inhibitory activities. Furthermore, according to our results, the macroalgae polyphenols’ roles in cancer cell growth inhibition were not clear, since there were extracts (e.g., C. barbata) having high TPCs and low inhibitory activity against cancer cell growth or vice versa (e.g., G. bursa pastoris) (Table 2, Figure 6). On the other hand, some extracts (e.g., G. teedei and U. rigida) with high TPC values exhibited also high inhibition against cancer cell growth (Table 2, Figure 6). Thus, it seems that for some macroalgae species, the total polyphenolic amount affects their anticancer potency, while there are also macroalgae species in which specific polyphenols may account for their anticancer activity and not their TPCs. Other studies have also reported, in agreement with us, that macroalgae extracts having high polyphenolic content exhibited low anticancer activity [100]. Furthermore, in accordance with our results, P. pavonica, a Phaeophyceae alga and the most potent extract, has been reported by others to inhibit HepG2 cells with an IC50 value (613 μg/mL) close to our value [101]. Moreover, P. pavonica extract was demonstrated to inhibit HCT-116 colon cancer cells [77] as well as osteosarcoma [55], lung, cervical, intestinal, larynx, and breast cancer cells [77] through molecular mechanisms such as apoptosis mediated by p53 protein [55]. In addition, El-Sheekh et al. [ 101] showed that P. pavonica decreased in vivo Ehrlich ascites carcinoma due to apoptosis. These P. pavonica activities were attributed mainly to its polysaccharides, sterols (e.g., fucosterol), terpenes (e.g., phytol), and fatty acids (e.g., palmitic acid) [55,101]. Some of the tested macroalgae species were reported previously to inhibit cancer cell growth. For example, extracts from the Phaeophyceae alga C. barbata, rich in phlorotannins, have been demonstrated to inhibit lung A549 [52,75], colon HT-29, breast MCF-7 [52], melanoma Fem-x, and chronic myelogenous leukemia K562 [75] cancer cells through increases in ROS, arrest at the subG1 phase, and apoptosis [52]. Furthermore, Kosanic et al. [ 75] showed C. amentacea to inhibit colon LS174 cancer cells. Like our findings, Kosanic et al. [ 75] reported that C. amentacea had better anticancer activity than C. compressa and C. barbata. C. amentacea has also been demonstrated to inhibit lung, melanoma, and myelogenous leukemia cancer cells [75]. Unlike our results, in two studies, C. compressa extracts did not inhibit liver Hep3B [27] and colon LS174 cancer cell growth [75], but the extracts have been used at lower concentrations (up to 50 and 200 μg/mL, respectively) than our extract. Interestingly, Guner et al. [ 27] collected C. compressa from the Aegean Sea (i.e., coast of Urla, Izmir, Turkey), but from a different region than ours. In addition, extracts from C. sinuosa, another Phaeophyceae alga, have been reported to inhibit HCT-116 colon cancer cell growth with IC50 values depending on the extraction method [100]. This activity was attributed mainly to the polysaccharides fucoidan and alginate and mediated through cell cycle arrest at the G1 phase, ROS increase, and apoptosis [89,100]. Additionally, sulfated polysaccharides such as fucan composed of fucose, galactose, xylose, glucuronic acid, and mannose from the Phaeophyceae alga S. vulgare have been demonstrated to inhibit cervical HeLa cancer cells [102]. Moreover, C. fragile, belonging to Chlorophyta, has been reported to possess compounds such as sulfated polysaccharides [103] and clerosterol [104], which inhibited in vitro and in vivo melanoma growth through cell cycle arrest at the G1 phase and apoptosis [104] as well as in vivo carcinoma metastasis [105]. In another study, sulfated polysaccharides from C. fragile, which were mainly linear homopolymers comprising ß-1.4-linked D-mannose residues, mediated anticancer immune responses through activation of NK cells, leading to an increase in cytotoxic mediators such as IFN-γ, IL-12, and CD69 overexpression [106]. Additionally, like us, Nazarudin et al. [ 107] reported that the Chlorophyte U. intestinalis inhibited growth of liver HepG2 cancer cells. In addition, U. intestinalis extract inhibited cervical cancer cells by autophagy induction through increases of p53, Bax, atg12, and p62 proteins [108]. Furthermore, lipid extracts from U. rigida exhibited inhibition of breast MDA-MB-231 cancer cells [109]. Finally, in agreement with our finding that the Rhodophyte G. pistillata inhibited colon cancer cell growth, carrageenans ι-, κ-, and λ- (i.e., sulphated polysaccharides) isolated from this species decreased cancer stem cell-enriched tumorspheres derived from colon SW620, SW480, and HCT116 cancer cell lines [110]. ## 3.7. Correlation Analysis Spearman’s correlation analysis was performed to find out if there was any association between macroalgae extracts’ activities as assessed in DPPH., ABTS•+, •OH, O2•−, RP, DNA plasmid strand cleavage, and XTT assays (Table 3). The results showed that there were high and significant correlations between IC50 values of the DPPH. assay and IC50 values of ABTS•+ ($r = 0.825$; $p \leq 0.01$), and RP0.5AU values ($r = 0.964$; $p \leq 0.01$) (Table 3). Moreover, the IC50 values of the ABTS•+ assay were significantly and highly correlated with RP0.5AU values ($r = 0.789$; $p \leq 0.01$) (Table 3). The significantly high correlation between values of DPPH., ABTS•+, and RP assays suggested that the same macroalgae extracts’ antioxidant compounds may account simultaneously for these two radicals’ scavenging and reducing activity. In addition, DPPH. and ABTS•+ assays are based on both hydrogen atom transfer (HAT) and single electron transfer (SET) mechanisms, while RP is a SET-based method [79]. Thus, the significantly high correlation between DPPH. and ABTS•+ values with RP also indicated that most of the tested macroalgae species’ antioxidants acted mainly as SETs. Furthermore, the significantly moderate correlation between extracts’ values of the DNA plasmid breakage assay with values of DPPH., ABTS•+, and RP assays (Table 3) suggested that some of the macroalgae extracts’ antioxidant compounds may account simultaneously for radical scavenging, reducing activity and preventing ROS-induced DNA damage. However, the absence of a high correlation between •OH and O2•− assays’ values with those of DPPH., ABTS•+, and RP assays indicated that macroalgae’s compounds scavenging the former radicals were different from those scavenging the latter. There was also a significantly high anticorrelation between TPC values and IC50 values of DPPH (−0.737; $p \leq 0.01$), ABTS•+ (−0.789; $p \leq 0.01$), and DNA plasmid strand cleavage assays (−0.768; $p \leq 0.01$) (Table 3). Importantly, the significantly high anticorrelation between TPC and IC50 values of DPPH, ABTS•+, and DNA plasmid breakage suggested that polyphenols may play significant roles in the tested macroalgae extracts’ antioxidant activity, although macroalgae extracts’ TPC values were low. As mentioned above, several studies have demonstrated polyphenols to account for macroalgae’s antioxidant activity [50,52,71,75]. However, the absence of significant correlation between •OH and O2•− assays’ values and TPCs suggested that especially for these two radicals’ scavenging, either macroalgae’s polyphenols might not be important, or specific polyphenols might be important instead of TPC. Namely, although specific polyphenols with high antioxidant potency exist at low amounts in macroalgae extracts, they may be able to scavenge •OH and O2•− radicals. Finally, the absence of significantly high correlation between XTT assay values and those of antioxidant assays indicated that different macroalgae’s compounds accounted for anticancer and antioxidant activity (Table 3). Furthermore, the absence of a significant correlation between TPC and XTT assay’s IC50 values (Table 3) indicated that in most tested macroalgae extracts, polyphenols were not important for macroalgae’s anticancer activity. As mentioned above, according to our findings, the association between macroalgae’s polyphenols and anticancer activity was not clear. ## 3.8. Clustering of Macroalgae Extracts Based on their Activities with Dendrogram and PCA In order to detect similarities and differences among the tested macroalgae species (Supplementary Figure S2) in terms of their overall measured activities, dendrogram and PCA analysis were performed using the data from all the bioactivity assays (i.e., DPPH., ABTS•+, •OH, O2•−, RP, DNA plasmid strand cleavage, and XTT assay in HepG2 cells). The results of the dendrogram and PCA analysis are shown in Figure 7 and Figure 8. It is evident from both dendrogram and PCA analysis that the C. fragile extract was very different from the other thirteen samples and appeared as an outlier. This difference of C. fragile was due to its weak activity in all assays, especially the antioxidants. Reassuringly, other studies have also shown that C. fragile extracts had weak antioxidant activity compared to other macroalgae species [65]. As mentioned, C. fragile extract had also the least TPC value, probably accounting for its low antioxidant activity. Once the outlier was removed from the analyses, it was evident that the other 13 samples formed two major subclusters. The first one was composed of G. gracilis and C. sinuosa, whereas the second subcluster was composed of the other 11 species (Figure 7D). The clustering of G. gracilis and C. sinuosa was mainly attributed to their close potency order in DPPH., ABTS•+, DNA plasmid strand cleavage, and RP assays. Moreover, G. teedei and G. pistillata extracts clustered together as sister groups (Figure 7D and Figure 8D). Indeed, these two Gigartina species were included among the most potent extracts in almost all assays, although the former had higher activity than the latter. All these suggested that G. teedei may contain similar bioactive compounds with G. pistillata, but in higher amounts. This conclusion was supported by the higher G. teedei’s polyphenolic amount compared to G. pistillata. Among Cystoseira species, C. amentacea and C. barbata clustered more closely compared to C. compressa (Figure 7D and Figure 8D). According to RACI values, C. barbata exhibited the best antioxidant activity, while it had also about a 2-fold higher TPC than the other two species. However, C. amentacea exhibited better anticancer activity than the other species. Furthermore, the three Gracilaria species’ extracts did not cluster together. Specifically, G. bursa pastoris and G. sp. clustered separately from G. gracilis (Figure 7D and Figure 8D). In antioxidant assays, the main differences between G. gracilis and G. bursa pastoris were exhibited in scavenging of •OH and O2•− radicals. Moreover, G. bursa pastoris was more potent in anticancer assay than G. gracilis. The two extracts of U. rigida and U. intestinalis also did not cluster too closely (Figure 7D). The two Ulva species exhibited similar activity in most antioxidant assays, but U. rigida was more potent in DPPH and DNA plasmid strand cleavage assays compared to U. intestinalis. However, U. intestinalis had higher inhibitory activity against colon cancer cell growth than U. rigida. Overall, the clustering of the tested macroalgae species suggests that between species of the same genus there are common bioactive compounds accounting for their similarities in some assays, but they also contain compounds characteristic of each species, which differentiate their activity in other assays. ## 4. Conclusions The results showed that the extract from the red macroalgae G. teedei was the most potent in all antioxidant assays, while it had also the highest TPC. Interestingly, another member of the Gigartina genus, G. pistillata, was the second most potent species in antioxidant activity, followed by C. barbata. Moreover, the results suggested that extracts’ polyphenols might play important roles for their antioxidant activity. In addition, extracts’ chemopreventive potential was also supported by their ability to inhibit liver HepG2 cancer cell growth. P. pavonica, G. bursa pastoris, and G. teedei extracts exhibited the three most potent inhibitory activities against liver cancer cells. To the best of our knowledge, the present study is the first demonstrating the antioxidant activity of G. teedei; the anticancer potential of G. teedei, G. gracilis, and G. bursa pastoris; the inhibitory activity of G. pistillata, C. amentacea, C. compressa, and C. barbata against liver cancer cells; protection from ROS-induced DNA damage of G. teedei, G. pistillata, S. vulgare, G. gracilis, C. amentacea, C. sinuosa, C. fragile, C. compressa, and P. pavonica extracts; and TPCs of C. sinuosa, C. fragile, C. barbata, and G. bursa pastoris extracts. Moreover, it is the first time to the best of our knowledge that the macroalgae species C. amentacea, G. pistillata, G. gracilis, U. intestinalis, U. rigida, C. barbata, C. sinuosa, C. fragile, and C. compressa collected from the Aegean Sea were examined for their antioxidant and/or anticancer activities. 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--- title: Screening and Evaluation of Active Compounds in Polyphenol Mixtures by a Novel AAPH Offline HPLC Method and Its Application authors: - Zhaoyang Wu - Guanglei Zuo - Soo-Kyeong Lee - Sung-Mo Kang - Sang-Youn Lee - Saba Noreen - Soon-Sung Lim journal: Foods year: 2023 pmcid: PMC10048677 doi: 10.3390/foods12061258 license: CC BY 4.0 --- # Screening and Evaluation of Active Compounds in Polyphenol Mixtures by a Novel AAPH Offline HPLC Method and Its Application ## Abstract In this study, we developed a novel offline high-performance liquid chromatography (HPLC) method based on 2,2′-azobis(2-amidinopropane) dihydrochloride (AAPH) radicals for antioxidant screening in 20 polyphenolic compounds and used the Trolox equivalent antioxidant capacity assay to evaluate their antioxidant activity. Compared to the existing offline HPLC methods based on 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH), the offline HPLC method based on the AAPH radical is more sensitive. Additionally, we applied this method to Lepechinia meyenii (Walp.) Epling extract and screened out seven antioxidants, caffeic acid, hesperidin, rosmarinic acid, diosmin, methyl rosmarinate, diosmetin, and n-butyl rosmarinate, which are known antioxidants. Therefore, this study provides new insights into the screening of antioxidants in natural extracts. ## 1. Introduction In recent years, the incidence of chronic diseases such as diabetes, obesity, cardiovascular and cerebrovascular diseases, and cancer has increased annually, resulting in a serious disease burden [1]. Growing evidence supports the crucial role that oxidative stress plays in the development of tissue injury, leading to a range of pathologies, all of which are characterized by a change in oxidative status [2]. Oxidative stress refers to a condition where cell membrane components, such as proteins, nucleic acids, and lipids, are damaged by oxidants through non-enzymatic means [3]. Therefore, antioxidants can act as key factors against oxidative stress to reduce the incidence of chronic diseases. Indeed, multiple preclinical and clinical studies indicate that lipid peroxidation products play a role in several pathological conditions, including inflammation, atherosclerosis, diabetes, aging, neurodegenerative diseases, and cancer [4]. 2,2′-Azobis(2-amidinopropane) dihydrochloride (AAPH) is well known to strongly induce lipid peroxidation in members, resulting in viability reduction and upregulation of reactive oxygen species generation [5,6]. Currently, the most widely used offline screening method for antioxidants is based on 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals, and at present, there is no offline screening method based on lipid peroxidation-related radicals. The electron transfer mode of DPPH and ABTS radicals is single-electron transfer, while the electron transfer mode of AAPH radicals is hydrogen atom transfer [7]. Based on the different electron transfer patterns, the compounds screened from natural products are also different. Therefore, it is necessary to develop an offline method based on AAPH radicals to screen antioxidants to delay the cell damage caused by lipid peroxidation products. Phenolic compounds are a relatively important class of compounds among the many secondary metabolites of plants. It refers to a class of compounds formed by replacing the hydrogen atoms on the benzene rings in aromatic hydrocarbons with hydroxyl groups. More than 8000 polyphenols have been identified, which can be further categorized into three main groups [8,9,10]. Flavonoids are a group of polyphenolic secondary metabolites occurring in plants, with C6-C3-C6 as the basic structure, and include quercetin, catechin, taxifolin, and apigenin [11]. Phenolic acids are a group of compounds that consist of a phenolic ring and an organic carboxylic acid function (C6-C1 skeleton), including hydroxybenzoic acids (gallic acid) and hydroxycinnamic acids (caffeic acid). Stilbenes are a group of natural compounds that are characterized by the presence of a central trans-stilbene core structure, which consists of two phenyl rings linked by a double bond, and the typical compound is resveratrol [12]. Natural products are an important source of antioxidants, especially herbal medicines. Lepechinia meyenii (Walp.) Epling (L. meyenii), a member of the Lamiaceae family, is indigenous to Argentina, Bolivia, and Peru, and its herbal infusion is commonly used in Peru as a traditional medicine to treat a range of conditions, including diabetes, cough, inflammation, diarrhea, spasms, stomach discomfort, and joint and stomach pain [13]. In our previous study, L. meyenii extract was found to have good antioxidant activity, and seven main components were isolated and identified from its extract; however, their anti-lipid peroxidation activity was unknown, which prompted us to use offline HPLC to further clarify the anti-lipid peroxidation activity and composition of L. meyenii [14]. In this study, we developed a novel offline high-performance liquid chromatography (HPLC) method based on AAPH radicals for the antioxidant screening of 20 polyphenolic compounds and used the Trolox equivalent antioxidant capacity assay to evaluate their antioxidant activity. The developed method was then applied to L. meyenii, and the results obtained were compared with those obtained by the existing DPPH offline HPLC and ABTS offline HPLC methods. ## 2.1. Reagents and Plants 2,2′-Azobis (2-amidinopropane) dihydrochloride (AAPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), apigenin, caffeic acid, (+)-catechin, p-coumaric acid, 3,4-dihydroxy-L-phenylalanin, 2,5-dihydroxybenzoic acid, 3,4-dihydroxybenzoic acid, 2,4-dihydroxycinnamic acid, 3,4-dihydroxyhydrocinnamic acid, formic acid, gallic acid, 4-hydroxyphenylacetic acid, 2-hydroxycinnamic acid, 4,4′-methylenediphenol, potassium chloride, potassium persulfate, potassium phosphate monobasic, quercetin, resveratrol, rosmarinic acid, sinapic acid, syringic acid, sodium hydrogen phosphate, sodium chloride, taxifolin, and Trolox were purchased from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA). Methanol was purchased from J. T. Baker Co. (Phillipsburg, NJ, USA) for sample preparation and analysis. Ultrapure water used in this study was produced using a Milli-Q water purification system (Millipore Co., Bedford, MA, USA). The aerial parts of L. meyenii were gathered from Lima in 2015 and verified by Paul H. Gonzales Arce (P.H.G.A.). The dried samples (L-2015-A30), extract (L-2015-A30E), and separated compounds (L-2015-A30C1-7) were placed at the Center for Efficacy Assessment and Development of Functional Foods and Drugs at Hallym University. ## 2.2. Preparation of Single Standard, Mixed Standards and L. meyenii Extract First, 20 kinds of 50 mmol methanol solutions of polyphenol standards were prepared and stored at 4 °C in the dark for future experiments. Then, using the same volume, the 20 standard solutions were mixed to prepare a 2.5 mmol mixed standard solution, which was diluted to 320 µmol in methanol and stored at −20 °C in the dark for future use. The L. meyenii extract and active compounds isolated from it used in this study were obtained from previous research, prepared as 1 mg/mL methanol, and stored at −20 °C in the dark for future use [14]. ## 2.3. Optimization of AAPH Radical Generation Condition AAPH radicals were generated under heating conditions. In this study, we investigated the effects of water bath temperature (65, 70, 75, 80, 85 °C), heating time (20, 30, 40, 50, 60 min), and concentration of AAPH (500, 750, 1000, 1250, and 1500 µmol in PBS; pH = 7.4) on the production of AAPH radicals using a single factor experiment, and the consumption of Trolox was used as a quantitative standard. While investigating other factors, this study used 75 °C, 30 min, and 500 µmol to control for variables. Trolox content was quantified using a standard curve (50–500 µmol) and detected at 291 nm using a UV spectrophotometer (UV1601, SHIMADZU, Kyoto, Japan) [15]. ## 2.4. HPLC Analysis The mixed standard was analyzed using equipment from Agilent Technologies (Santa Clara, CA, USA). The setup consisted of a G1311A pump, a G1329A automated sample injector, a G1316A column oven kept at 30 °C, and a G1314D detector. The HPLC mobile phases used were acidic water ($0.1\%$ formic acid; A) and methanol (B). The mixed standards were analyzed at 254 nm and separated with a flow rate of 0.7 mL/min using an Eclipse XDB-C18 column (250 × 4.6 mm, 5 µm). The separation process was as follows: $10\%$ B from 0–5 min, 10–$50\%$ B from 5–40 min, 50–$100\%$ B from 40–55 min, and $100\%$ B from 55–60 min. The L. meyenii samples were also analyzed at 254 nm and separated with a flow rate of 0.7 mL/min using an Eclipse XDB-C18 column (250 × 4.6 mm, 5 µm), with the separation process being 10–$100\%$ B at 0–20 min, and $100\%$ B at 20–26 min [13]. ## 2.5. Screening Antioxidants from Mixed Standards and Extract Using AAPH Offline HPLC Briefly, a mixture of 300 µL of the AAPH solution (16 mmol in PBS with a pH of 7.4) and 300 µL of the mixed standard solution (320 µmol in methanol) or extract (1 mg/mL in methanol) was incubated for 50 min at 65, 75, and 85 °C. Therefore, the reaction solution with an injection volume of 10 µL was analyzed to HPLC. As a blank, the AAPH solution was replaced with PBS when incubating with the mixed standards or extract to form an AAPH free group. Compared to the AAPH free group, compounds with reduced peak areas in the AAPH group were assigned as having potential antioxidant activity. In addition, in order to study the effect of heating temperature on polyphenols, we set up a group, a mixture of 300 µL of PBS and 300 µL of the mixed standard solution or extract without heating, as a control group. Peak area reduction by heating and AAPH radicals was calculated as the following Formulas [1] and [2], respectively:Peak reduction by heating = (A control − A AAPH free group)/A control × $100\%$[1] Peak reduction by AAPH radicals = (A AAPH free group − A AAPH group)/A AAPH free group × $100\%$[2] ## 2.6. Screening Antioxidants from Mixed Standards and Extract Using ABTS Offline HPLC The ABTS radical (ABTS+) solution was prepared by mixing 11 mg ABTS, 9.5 mg potassium persulfate, and 300 mL distilled water. ABTS working solution was prepared in the dark by incubation for 16 h at 25 °C. Briefly, 200 µL of ABTS working solution prepared before and 20 µL of the mixed standard solution (320 µmol in methanol) or extract (1 mg/mL in methanol) were mixed and incubated for 6 min at 25 °C in the dark. Therefore, the reaction solution, with an injection volume of 10 µL, was analyzed by HPLC. As a blank, the ABTS solution was replaced with water when incubating with the mixed standards or extract to form an ABTS+ free group. Compared to the ABTS+ free group, compounds with reduced peak areas in the ABTS+ group were assigned as having potential antioxidant activity [16]. Peak area reduction by ABTS radicals was calculated using the following Formula [3]:Peak reduction by ABTS radicals = (A ABTS free group − A ABTS group)/ A ABTS free group × $100\%$ [3] ## 2.7. Screening Antioxidants from Mixed Standards and Extract Using DPPH Offline HPLC Briefly, 150 µL of DPPH solution (2.5 mg/mL in methanol) and 50 µL of the mixed standard solution (320 µmol in methanol) or extract (1 mg/mL in methanol) were mixed and incubated for 30 min at 37 °C. Therefore, the reaction solution, with an injection volume of 10 µL, was analyzed by HPLC. As a control, the DPPH solution was replaced with methanol when incubating with the mixed standards or extract to form a DPPH free group. Compared to the DPPH free group, compounds with reduced peak areas in the DPPH group were assigned as having potential antioxidant activity [16]. Peak area reduction by DPPH radicals was calculated using the following Formula [4]:Peak reduction DPPH radicals = (A DPPH free group − A DPPH group)/A DPPH free group × $100\%$[4] ## 2.8. The Trolox Equivalent Antioxidant Capacity (TEAC) Assay ABTS+ solution was prepared as part 2.6. Ten microliters of sample (0.5 mmol in methanol) was added to a 96-well plate with 290 μL of ABTS+ solution and incubated in the dark for 10 min at 25 °C, which was detected by an EL800 microplate reader (Bio-Tek Instruments, Winooski, VT, USA) at the absorbance of 750 nm. The standard results are expressed as µmol Trolox equivalents [17]. ## 2.9. Statistical Analysis The TEAC assay was performed in triplicate, and the results are presented as the means ± standard deviations (SDs). One-way ANOVA with Tukey’s multiple comparison test was used to compare the differences in Trolox consumption using Pearson’s correlation coefficients with SPSS software (Version 25; IBM, New York, NY, USA). ## 3.1. Optimization of AAPH Radical Generation Condition As shown in Figure 1, the water bath temperature, heating time, and AAPH concentration were key factors affecting the generation of AAPH radicals. With the increase in the temperature of the water bath, the prolongation of the heating time, the increase in the concentration of AAPH, and the consumption of Trolox increased, which represented an increase in the generation of AAPH radicals. Therefore, a heating time of 50 min was suitable. When the Trolox concentration was 500 µmol, AAPH at a concentration of 1250 µmol was sufficient. As the concentration of each standard in the mixed polyphenols was 320 µmol, according to the scale conversion, 16 mmol AAPH was used to screen antioxidants from the mixed polyphenols for future experiments. Phenolic compounds are unstable, and they easily decompose during heating, which was also confirmed by previous research results [18]. Therefore, in this study, we selected 20 phenolic compounds that were common in plant extracts. Then we investigated the thermal stabilities of phenolic compounds at different temperatures (Figure 2a,c,e), and the generation of AAPH radicals (Figure 2b,d,f). The results are shown in Figure 2 and Table 1. In addition, to verify the accuracy of the AAPH offline HPLC method, the TEAC assay was used to evaluate the antioxidant activity of phenolic compounds, and the results are shown in Table 2. As shown in Table 1, at 65 °C, the percentage of peak area reduction of polyphenol compounds due to heating was between 3.03 and $23.92\%$; at 75 °C, the percentage of peak area reduction of polyphenols was between 4.32 and $34.80\%$; and when the temperature was increased to 85 °C, the percentage decrease in the peak area of polyphenols was between 4.65 and $39.20\%$. At 65 °C, we detected 16 compounds whose peak area reduction was due to the reaction with AAPH radicals; the percentage of peak area reduction was between 1.84 and $100\%$, and most of the peak area reduction rates were around $10\%$; at 75 °C, we detected a reduction in the peak area of 19 compounds. Due to the reaction with AAPH radical, the percentage of peak area reduction was between 1.46 and $100\%$, and most of the peak area decrease rate was approximately $50\%$; at 85 °C, we detected peak area reduction for 17 compounds, due to the reaction with AAPH radical, the percentage of peak area reduction was between 0.3 and $100\%$. The degradation rate of polyphenols and scavenging rate of AAPH radicals increased with increasing temperature. Affected by the decomposition products, 3,4-Dihydroxyhydrocinnamic acid and 4,4′-methylenediphenol, which have AAPH free radical scavenging activity, could not be analyzed when heated at 85 °C. Therefore, to ensure a lower polyphenol degradation rate and a more accurate analysis of compounds with AAPH radical scavenging activity, 75 °C is a suitable temperature. ## 3.2. Screening Antioxidations from Mixed Polyphenols Using AAPH, DPPH and ABTS Offline HPLC As shown in Table 2 and Figure 2f,g,h, based on ABTS, DPPH, and AAPH free radicals, we used offline HPLC to screen antioxidants from six types and twenty polyphenol standards. Nineteen active compounds were screened using the AAPH offline HPLC method, and the peak area reduction rate was between 1.46 and $100\%$; 17 active compounds were screened by the DPPH offline HPLC method, and the peak area reduction rate was between 1.07 and $100\%$; and 18 active compounds were screened by the ABTS offline HPLC method, and the peak area reduction rate was between 6.74 and $27.92\%$. The results obtained by the AAPH offline HPLC method were consistent with those of TEAC and were more sensitive than the existing ABTS and DPPH offline HPLC methods. This is due to the particularity of AAPH radicals, which have shorter carbon chains, active double bonds, and are easier to react with polyphenols; moreover, this may be due to the relatively high temperature of the AAPH reaction system, which accelerates the production of AAPH radicals as well as increases the activity of phenolic compounds to attack radicals [19,20,21]. The antioxidant properties of phenolic acids are greatly influenced by molecular structural features, such as the presence of double bonds and the number and positioning of hydroxyl groups relative to the carboxyl functional group and carbon chain length [22,23]. The radical addition reaction to the double bond has a very important effect on antioxidant capacity [24]. Among the five polyphenols in the group of hydroxybenzoic acid and its derivatives, gallic acid, 2,5-dihydroxybenzoic acid, 4-hydroxyphenylacetic acid, and syringic acid were screened by AAPH offline HPLC, gallic acid, 2,5-dihydroxybenzoic acid, 3,4-dihydroxybenzoic acid, and syringic acid were screened by DPPH offline HPLC, and all the standards were screened by ABTS offline HPLC with antioxidant activity. In the AAPH offline HPLC method, 3,4-dihydroxybenzoic acid did not show antioxidant activity, but 2,5-dihydroxybenzoic acid with the same number of hydroxyl groups showed strong antioxidant activity because hydrogen bonds were formed between hydroxyl groups in 3,4-dihydroxybenzoic acid, and the antioxidant activity decreased. 4-Hydroxyphenylacetic acid has a Trolox equivalent of 0.74 ± 0.01 µM and cannot be screened out by the DPPH offline HPLC method, probably because only one hydroxyl group exists in its structure. Some studies have shown that 4-hydroxyphenylacetic acid has only a weak DPPH radical scavenging activity. In the hydroxycinnamic acid group and its derivatives, the Trolox equivalent of the six polyphenol standards was between 0.57 and $4.98\%$ [25]. All standards were demonstrated to have antioxidant activity using AAPH and ABTS offline HPLC methods. However, 3,4-dihydroxyhydrocinnamic acid showed no antioxidant activity in the DPPH offline HPLC method. The number of hydroxyl groups and the number of double bonds in 3,4-dihydroxyhydrocinnamic acid and caffeic acid were the same, and the antioxidant activities obtained in Trolox equivalent, AAPH, and ABTS offline HPLC results were similar, but there was no antioxidant activity in DPPH offline HPLC, which may be related to free radicals related to the type. By reviewing the literature, we found no studies that have screened 3,4-dihydroxyhydrocinnamic acid for antioxidant activity using DPPH offline HPLC. Compared with p-coumaric acid, 2,4-dihydroxycinnamic acid showed stronger antioxidant activity in the results of Trolox equivalent and AAPH offline HPLC, which is due to one more hydroxyl group in 2,4-dihydroxycinnamic acid. The Trolox equivalent weight of the four polyphenol standards in the flavonoid group ranged from 0.71–$8.16\%$. All standards were demonstrated to have antioxidant activity using AAPH and DPPH offline HPLC methods. However, (+)-catechin showed no antioxidant activity in the ABTS offline HPLC method. After a literature search, it was found that the ABTS free radical scavenging activity of (+)-catechin is low; hence, it is not easy to be screened out by the ABTS offline HPLC method [26,27]. The number of hydroxyl groups in ginseng was the lowest; therefore, apigenin had the weakest antioxidant activity in the results of Trolox equivalent, AAPH, and DPPH offline HPLC. In stilbenes, lignans, and other groups, all standards were demonstrated to have antioxidant activity using the AAPH offline HPLC method. 4,4′-Methylenediphenol had no antioxidant activity in the DPPH offline HPLC method, and 3,4-dihydroxy-L-phenylalanine had no antioxidant activity in the ABTS offline HPLC method. A literature search found that 4,4′-methylenediphenol has no DPPH free radical scavenging activity, and 3,4-dihydroxy-L-phenylalanine has no ABTS free radical scavenging activity. In conclusion, compared with the existing offline HPLC methods of ABTS and DPPH, the offline HPLC method based on the AAPH radical is more sensitive. ## 3.3. Screening Antioxidations from L. meyenii Extract Using AAPH, DPPH, and ABTS Offline HPLC In this study, we selected methanol extract (MeOH. E.), methanol extract chloroform fraction (MeOH-CH2Cl2. Fr.), and $50\%$ methanol extract ethyl acetate fraction ($50\%$ MeOH-EA.Fr.), which contained all the compounds in L. meyenii extract, to investigate the antioxidations. In our previous studies, the components in L. meyenii extracts have been isolated and identified [14]. Therefore, in this study, we determined the components in L. meyenii extracts by retention time. As shown in Figure 3 and Table 3, we applied the AAPH offline HPLC method to L. meyenii extracts, compared the DPPH and ABTS offline HPLC methods, and used the TEAC method to calculate the Trolox equivalent to verify the accuracy of the AAPH offline HPLC method. Through the TEAC assay, we used the standard curve method to calculate the Trolox equivalents of the seven main compounds in L. meyenii between 1.37 and 2.17 µmol, which have good antioxidant activity. In this study, the methanol extract of L. meyenii, chloroform fraction of the methanol extract, $50\%$ methanol extract, and ethyl acetate fraction of the $50\%$ methanol extract, which contains seven main compounds, were used as samples for antioxidant screening. In the AAPH offline HPLC method, the peak area reduction percentage of these seven compounds was between 17.56 and $100\%$ because of the scavenging of AAPH radicals, and in the DPPH offline HPLC method, the peak area reduction percentage was between 12.26 and $100\%$. In the ABTS offline HPLC method, the peak area reduction percentages of these seven compounds ranged from 1.59 to $20.76\%$. Therefore, we believe that the results obtained by the AAPH offline HPLC method were consistent with those of the DPPH offline HPLC method and were more sensitive than those of the ABTS offline HPLC method. This is consistent with the Trolox equivalent calculated by the TEAC assay, which shows that the AAPH offline HPLC method developed based on AAPH free radicals is reliable and accurate and can be used for the screening of antioxidants in natural extracts. ## 4. Conclusions In this study, we developed a novel offline HPLC method based on AAPH radicals for the antioxidant screening of 20 polyphenolic compounds and used the Trolox equivalent antioxidant capacity assay to evaluate their antioxidant activity. Compared with the existing offline HPLC methods for ABTS and DPPH, the offline HPLC method based on the AAPH radical is more sensitive. In addition, we applied this method to the L. meyenii extract and screened seven antioxidants, caffeic acid, hesperidin, rosmarinic acid, diosmin, methyl rosmarinate, diosmetin, and n-butyl rosmarinate, which are known antioxidants. Therefore, this study provides new insights into the screening of antioxidants in natural extracts. ## References 1. 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--- title: Development and Evaluation of Essential Oil-Based Nanoemulgel Formulation for the Treatment of Oral Bacterial Infections authors: - Niamat Ullah - Adnan Amin - Arshad Farid - Samy Selim - Sheikh Abdur Rashid - Muhammad Imran Aziz - Sairah Hafeez Kamran - Muzammil Ahmad Khan - Nauman Rahim Khan - Saima Mashal - Muhammad Mohtasheemul Hasan journal: Gels year: 2023 pmcid: PMC10048686 doi: 10.3390/gels9030252 license: CC BY 4.0 --- # Development and Evaluation of Essential Oil-Based Nanoemulgel Formulation for the Treatment of Oral Bacterial Infections ## Abstract Prevalence of oral infections in diabetic patients is a health challenge due to persistent hyperglycemia. However, despite great concerns, limited treatment options are available. We therefore aimed to develop nanoemulsion gel (NEG) for oral bacterial infections based on essential oils. Clove and cinnamon essential oils based nanoemulgel were prepared and characterized. Various physicochemical parameters of optimized formulation including viscosity (65311 mPa·S), spreadability (36 g·cm/s), and mucoadhesive strength 42.87 N/cm2) were within prescribed limits. The drug contents of the NEG were 94.38 ± $1.12\%$ (cinnamaldehyde) and 92.96 ± $2.08\%$ (clove oil). A significant concentration of clove ($73.9\%$) and cinnamon essential oil (71.2 %) was released from a polymer matrix of the NEG till 24 h. The ex vivo goat buccal mucosa permeation profile revealed a significant (52.7–$54.2\%$) permeation of major constituents which occurred after 24 h. When subjected to antimicrobial testing, significant inhibition was observed for several clinical strains, namely Staphylococcus aureus (19 mm), Staphylococcus epidermidis (19 mm), and Pseudomonas aeruginosa (4 mm), as well as against Bacillus chungangensis (2 mm), whereas no inhibition was detected for Bacillus paramycoides and Paenibacillus dendritiformis when NEG was utilized. Likewise promising antifungal (Candida albicans) and antiquorum sensing activities were observed. It was therefore concluded that cinnamon and clove oil-based NEG formulation presented significant antibacterial-, antifungal, and antiquorum sensing activities. ## 1. Introduction Oral microbiome is quiet diverse and is comprised of over 700 different “core” and “Transient” microbial species including Gram-positive, Gram-negative and fungal species [1], which significantly contribute towards gingivitis, periodontitis, dental caries, stomatitis, and candidiasis [2]. This greater microbial load is influenced by several factors including host, signaling systems, and environmental factors [3]. Several bacteria, fungi, viruses, or protozoa are responsible for such infections that become more severe due development of biofilms [4,5]. Tooth decay (dental caries) and periodontal disease are prevalent oral infections that result from bacteria accumulating on the surface of teeth. Among the most caries-causing bacterial species are Mutans streptococci, including S. mutans and Streptococcus sobrinus. These bacteria consume sugars in the diet and produce acid, which lowers the pH of the mouth and leads to the demineralization of tooth enamel, ultimately causing tooth decay [6]. Gingivitis is a gum infection that is the first stage of periodontal disease. It is characterized by gingival bleeding and swollen gums and mostly it is reversible. If not treated properly and swiftly, it can lead to periodontitis. Periodontitis is a chronic inflammatory illness in which the supporting components of the teeth, such as the periodontal ligament and alveolar bone, are destroyed. In the general population, severe periodontitis affects $10\%$ to $15\%$ of the population [7]. The complex structure of bacterial colonies in the oral cavity makes it difficult to treat bacterial infections in dentistry [8]. Oral infections are usually treated with antibiotics, antiamoebic drugs, and quaternary ammonium compounds. However, the use of these drugs may lead to the development of resistance and toxicity due to the selective pressure induced by their prolonged use [9]. Thus, there exists a great potential for alternative therapies that are safe, effective, and easy to use, such as essential oils. The potential for using essential oils against microbial infections is gaining attention [10]. Most well-known essential oils for maintaining good dental health include lavender, eucalyptus, peppermint, clove, and cinnamon essential oils [11], which are also supported with scientific evidence [12]. Cinnamon oil has been investigated for its antibacterial action against both Gram-positive and Gram-negative pathogens. It is mainly collected from Cinnamomum zeylanicum (Family Lauraceae) [13]. The cinnamon essential oil is traditionally used in various herbal remedies due to diverse biological activities including anti-inflammatory, antidiabetic, carminative, antiviral, and antihypertensive properties [14]. Major constituents of cinnamon essential oil include cinnamaldehyde, Linalool, β-caryophyllene, eucalyptol, camphor, and cinnamyl acetate [15]. Several investigations have shown its effectiveness towards Streptococcus mutans and Lactobacillus casei (dental cavities), Staphylococcus aureus, C. albicans, C. glabrata, and Enterococcus faecalis [16]. A strong antimicrobial action of cinnamon essential oil is due to disruption of the cell membrane, which promotes leakage of intracellular components [17]. Clove essential oil is mainly obtained from Syzygium aromaticum (family Myrtaceae) [18] and the FDA has classified the clove essential oil safe as “generally regarded as safe” (GRAS) and this essential oil possess several properties such as antioxidant, antioxidant, antifungal, as well as antibacterial [19]. Furthermore, clove oil has also been proven to effectively inactivate bacterial strains such as E. coli O157:H7, Salmonella typhimurium, Listeria monocytogenes, and S. aureus [19]. Nanoemulsion is a drug delivery system that is designed with enhanced stability and solubility [20]. The globules size in the nanoemulsion is in the nano range and are stabilized by mixture of the surfactants and co-surfactants [21]. The globules of a nano metric size in the oil phase normally facilitate efficient drug delivery; and therefore, the formulation of nanoemulsion is promising approach against oral infections [22]. In order to improve retention in the oral cavity and even enable sustained release of a medication, nanoemulsion gels (NEGs) are prepared by adding a thickening or gelling agent (Carbopol) to a nanoemulsion [23,24]. NEGs are valuable formulations as they offer a sustained release of drugs and their mucoadhesive nature facilitates a prolonged contact time compared to nano emulsion [21,24]. NEGs, which are either W/O or O/W emulsions, are combined with a gel base to create a more stable, viscous, and non-greasy formulation [25]. This is a preferred dosage form for hydrophobic drugs. These formulations possess an improved bioavailability, as they reduce surface tension and shield incorporated drug from enzymatic degradation and hydrolysis [26]. Furthermore, an improved infusibility, greater drug loading capacity, and permeability make NEGs the most acceptable delivery system for dental drug delivery [27]. Carbopols are mainly comprised of acrylic acid polymers that are cross-linked with polyalkenyl ethers or divinyl glycol [28]. The non-irritant and non-toxic nature of carbapols make them widely acceptable for topical applications [29]. A high molecular weight limits their penetration into the skin, and they thus are considered as good substitutes for oil-based vehicles [30]. Based on the widespread prevalence of oral microbial infections and importance of NEGs, this project was designed to develop a muco-adhesive nanoemulgel for the treatment of oral bacterial infections. ## 2.1. HLB Values of Different Smix Ratios The HLB values of different Smix ratios (surfactant: co-surfactant) (1:1, 1:2, 1:3, 1:4, 2:1, 3:1, and 4:1) were determined and it was observed that the HLB values increased with an increase in the concentration of the surfactant whereas a significant decrease in HLB values was noticed with a decrease in the concentration of the co-surfactant (Table 1). The Smix ratio 4:1 was chosen due to its high HLB value (12.86). It is advantageous for oil in water nanoemulsions to have higher HLB values [24]. The HBL values and particle size in o/w emulsion has inverse relationship higher the HLB value lower is the globule size of the emulsion. Furthermore, the higher HLB values are helpful for producing a uniform and fine nanoparticle [24]. As the oil concentration rises, the nanoemulsion particle size expands, and the concentration of the surfactants is forced to keep up with the oil droplets’ rising concentration [30]. ( The GC-MS Profile of clove essential oil as shown in Figure S1.) ## 2.2. Optimization of Nanoemulsion The optimization of the nanoemulsion was performed by using the pseudo ternary diagram. In this case, Smix (Span 80 and Tween 80) was combined with olive oil at the dispersed phase, and distilled water was used as the dispersing medium. The region of stable oil in water nanoemulsion is represented by the shadowed area in the built pseudo-ternary phase (Figure 1). The phase diagram’s each corner represents $100\%$ concentration of constituent. ## 2.3. Globule Size, Polydispersity Index, and Zeta Potential Four formulations of the nanoemulsion were prepared with different concentrations of the oily phase. The concentration of the surfactants was also minimized for the nanoemulsion. The N1 formulation showed the least globule size of (152 nm) compared to all formulations (Table 2). Due it being the least size, N1 formulation was selected as an optimum formulation. ## 2.4. Kinetic Stability The optimized formulation was centrifuged to check the kinetic stability. The formulation was rendered as kinetically stable since no phase separation, creaming, and flocculation occurred after centrifugation at 1000, 2000, and 3000 rpm. ## 2.5. Thermodynamic Stability To assess the thermodynamic stability, a heating–cooling cycle was performed on the test sample. It was observed that nanoemulsion creaming and phases separating occurred after three consecutive heating–cooling cycles, which was a clear indication of thermodynamic instability. Nanoemulsions are thermodynamically unstable while being kinetically stable, and therefore creaming and phase separation occurs upon long-term storage. The centrifugation process is typically used to execute stress conditions to freshly formed nanoemulsions in order to determine their kinetic stability. This is due to fact that the colloidal dispersion’s free energy in a nanoemulsion is higher compared to the free energy of the individual phase indicates that nanoemulsion [31]. ## 2.6. Optimization of Nanoemulgel The nanoemulgel formulations were optimized based on viscosity and spreadability and it was observed that the spreadability of the nanoemulgel decreased with an increase in the polymer concentration (Table 3, Figure 2), whereas a significant increase in viscosity of the nanoemulgel was seen with an increase in polymer concentration (Table 3, Figure 3). The formulation F1 was less viscous (62,035 ± 10 mPa·S) with a high spreadability value of 38 ± 1, whereas formulation F3 and F4 both were highly viscous, having viscosity 91,306 ± 15 and 96,432 ± 10 mPa·S, respectively, and very low spreadability. Formulation F2 was considered as optimized due to its optimal viscosity of 65,311 ± 7 mPa·S and spreadability of 36 ± 0.5. The polymers (gelling agents) are employed to keep the nanoemulgel consistency constant. Physical homogeneity, consistency, bio-adhesive qualities, swelling index, rheological studies, drug release kinetics, extrudability, and spreadability are among the parameters that play a significant role in determining the consistency of nanoemulgel formulations, with gelling agents being a key factor in achieving these parameters [32]. High viscosity (30,000–50,000 centipoises) of synthetic polymer Carbopol 940 is typically utilized for topical or transdermal formulations as it forms a transparent gel with water or hydro alcoholic frameworks. Likewise, spreading is a crucial aspect for preparations intended for topical treatment [33]. Our findings demonstrated that the improved formulation is suitable for topical application and has acceptable spreadability properties. An increase in the concentration of the gelling agent (Carbopol 940) showed a positive effect on the viscosity of the nanoemulgel formulation; consequently, the spreadability of the nanoemulgel formulation decreased, as there is inverse relationship between the viscosity and spreadability of the formulation (Figure 2 and Figure 3) [34]. ## 2.7. FTIR FTIR analysis is utilized to ascertain the drug-excipient compatibility in nanoemulgel formulations. The characteristic peaks of essential oils and other excipients, both alone and in the optimized nanoemulgel formulation, were found to match the previously reported functional groups of these constituents. For instance, in the FTIR spectrum of olive oil, peaks at 2923 cm−1 and 2845 cm−1 correspond to OH and fatty acid stretching, while a peak at 1744 cm−1 represents the ester C=O group (Figure 4). In clove oil, a peak at 3452 cm−1 represents OH stretching, and peaks at 1512 cm−1, 1425 cm−1, and 1266 cm−1 correspond to the aromatic C=C and phenolic group. In cinnamon oil, a peak at 3452 cm−1 represents OH stretching, a peak at 1670 cm−1 corresponds to the C=O group, a peak at 1624 cm−1 represents the C=C group, a peak at 745 cm−1 corresponds to the aromatic C-H group, and a peak at 970 cm−1 indicates C-H bending. In the FTIR spectrum of Carbopol 940 the peak at 2927 cm−1 represents CH2 stretching, a 1767 cm−1 peak is associated with COOH group, whereas 1451 cm−1 and 1246 cm−1 peaks show the presence of the acrylate back bone (Figure 4). Based on FTIR spectral data of essential oils as well as excipients, it is confirmed that the characteristic peaks of essential oils are preserved in the nanoemulgel formulations, showing the absence of any type of interaction among formulation constituents. ## 2.8. Drug Content Analysis Drug content analysis studies revealed that essential oil contents of the optimized nanoemulgel formulation for cinnamon oil and clove oil were 94.38 ± $1.12\%$ and 92.96 ± $2.08\%$, respectively. ## 2.9. Entrapment Efficiency of the Nanoemulsion and Nanoemulgel The average entrapment efficiency of the cinnamaldehyde in the nanoemulgel was $95.78\%$, whereas in case of the clove oil, it was $96.45\%$. Entrapment efficiency and system homogeneity are influenced by the drug’s solubility in the required oily phase, as well as by its compatibility with other ingredients. An important element that greatly effects drug encapsulation is the insolubility of the investigated chemical compounds, which can be stabilized by employing surfactants and co-surfactants. This might be due to a higher surfactant content in nanoemulsion and nanoemulgel, which can lead to smaller particle size and reduced drug entrapment [35]. Furthermore, in our case, drug partitioning improved the solubility of active ingredients from the oily phase to the aqueous phase, which reduced formulation viscosity and enhanced the diffusion phase during self-assembling. This provided a clear explanation for the formulation’s earlier record of lower active ingredient entrapment efficiency [36]. ## 2.10. Drug Release Profile of Nanoemulgel In vitro drug release profiling of nanoemulgel was performed using a Franz diffusion cell. Drug release as percentage was plotted against the experimental time (Figure 5). The essential oil-loaded nanoemulgel presented a sustained release of the drug since the essential oil was encapsulated in lipid part of the nanoemulgel. The clove essential oil-based formulation presented a significant release ($73.9\%$) within 24 h, whereas a nearly similar release profile was recorded in case of cinnamon oil-based formulation ($71.2\%$) (Figure 5). In the case of pure essential oils $100\%$, the release was observed within 2 h. Therapeutic efficacy of any drug is dependent on the release of the drug from pharmaceutical formulation [37]. The release of the drug from the topical pharmaceutical formulation depends upon many factors such as viscosity, surfactants concentration, polymer, and drug concentration [38]. A delayed release of the essential oil from the optimized nanoemulgel formulation could possibly be due to the fact that a polymer was incorporated in the already prepared nanoemulsion. This increases the viscosity, and the structure becomes more complex. The high molecular weight of synthetic polymer Carbopol 940 results in the formation of a viscous formulation, causing a delay in the release profile of drugs from topical preparations such as nanoemulgel [32]. Moreover, it has been reported earlier that emulgel acts as a reservoir for the drug, which releases the drug initially from the internal phase to the external phase, and then into the site of the skin [39]. In nanoemulgel, the oil droplets are initially released into the gel matrix and then penetrate directly into the subcutaneous layer of the skin, bypassing the need to pass through a hydrophilic phase transfer in the nanoemulsion [40]. ## 2.11. Mechanism of Drug Release from the Essential Oil-Loaded Nanoemulgel Different kinetic models, such as Zero order, First order, Higuchi, Hixson–Crowell, and Korsmeyer–Peppas models were used to establish drug release mechanism from nanoemulgel. It was observed that our formulation followed Korsmeyer–Peppas model with an R2 value of 0.99. It was concluded that the drug release from the produced formulation was diffusion-controlled (Table 4). ## 2.12. Buccal Mucosa Permeation of the Prepared Nanoemulgel A moderate permeation of cinnamon ($54.21\%$) and clove ($52.75\%$) essential oil was observed in the case of nanoemulsion. Overall, a delayed permeation was observed for the essential oil in nanoemulgel, which was contrary to nanoemulsion. Pure drug ($100\%$) permeation was achieved in 4 h (Figure 6). ## 2.13. Mucoadhesive Test Hydrophilic polymer Carbopol 940 (used as a gelling agent) also possesses significant mucoadehession strength. The mucoadhesive strength of the nanoemulgel was calculated by using a modified balance method. It was observed that the prepared nanoemulgel formulation has a very good mucoadhesive strength of 42.87 N/cm2. Mucoadehession is essential to deliver the required drug concentration to the diseased mucosa as the prepared formulation stays intact to the affected area for longer time due to adhesive properties. This therefore provides a maximum time of contact to the formulation release the drug. The hydrophilic polymers possess strong mucoadehession characteristics due to the fact that they can form hydrogen bonding interaction with the mucin [41]. Mucoadehession is a key property of the hydrophilic polymers [42]. ## 2.14. Skin Irritation Test An essential oil-loaded nanoemulgel formulation and formalin (positive control) were applied on rats. The rats were observed for any sign of skin irritation such as skin erythema and edema formation. The Draize skin irritation scoring system was used to rate skin irritation, and as reported earlier, if score was one or less than one, then no or negligible signs of irritation were demonstrated. The skin irritation score of the nanoemulgel loaded with essential oil was <1, and there were no significant signs of skin irritation. The lack of irritancy in the tested formulation may be attributed to the fact that the essential oils were encapsulated in olive oil and the non-irritant polymer Carbopol 940 [43]. Likewise, the prepared nanoemulgel formulation was also applied on the goat buccal mucosa and no signs of mucosal irritation were observed (Table 5). ## 2.15.1. Antimicrobial Activity The optimized nanoemulgel formulation (loaded with $3\%$ and $1.5\%$ essential oil) was tested for antibacterial efficacy against isolated oral bacterial strains. The essential oil-loaded nanoemulgel formulation was found to have a very distinct zone of inhibition when used against the isolated bacterial strains (Table 6). The antibacterial activity of NEG loaded with essential oil was also checked against other bacterial strains P. aeruginosa (4 ± 1 mm) and Bacillus chungangensis (2 ± 1 mm), whereas no activity was seen against Bacillus paramycoides and Paenibacillus dendritiformis. Moreover, it was found that the antibacterial activity of the essential oil-loaded nanoemulgel against the oral bacterial strains was increased as the concentration of the EO increased from 1.5–$3\%$ w/w, and the zone of inhibition increased from 6–19 mm in both the oral bacterial strains (Supplementary Information (Figures S2 and S3). Likewise, significant inhibition of S. aureus and S. epidermidis was also recorded (Table 6). The cinnamon and clove essential oils were previously used against E. coli and K. pneumoniae with different concentrations. It was found that these essential oils were potentially active, and the zone of inhibition increased with an increase in the essential oil concentration [44]. The essential oil loaded nanoemulgel also inhibited Candida albicans (6 ± 1 mm). It has been reported earlier that the hydrophobic nature of the essential oils may lead to the deterioration of the membrane and increase the permeability of the membrane in bacteria [45]. ## 2.15.2. Antiquorum Sensing Activity Antiquorum sensing of the prepared nanoemulgel was performed against Chromobacterium voilaceum (biomarker strain), and significant results were recorded (20 ± 1 mm) (Table 7, Supplementary Materials Figure S5). ## 2.16. Scanning Electron Microscopy of the Essential-Loaded Nanoemulgel Scanning electron microscopic analysis provides the morphological information about the formulation. The SEM images confirmed that globules of the nanoemulsions are evenly distributed in the polymeric gel base of the emulgel and exhibited a well-defined spherical shape. Moreover, the porous nature of the Carbopol gel embedded the nanoemulsion globules forming a gel layer barrier that facilitates a delayed release of the drug. The spherical shape of the globules is beneficial due to their ability to squeeze easily to penetrate through the microscopic pores of the skin (Figure 7). ## 2.17. Kinetic and Thermodynamic Stability of Nanoemulgel The optimized nanoemulgel formulation underwent characterization to determine its pH, homogeneity, and color. No significant changes were observed in the organoleptic properties of the optimized nanoemulgel formulation, and it remained homogeneous for 3 months at 25 °C as shown in Table 8. Furthermore, at temperature 8 °C and 25 °C, no change in color of the formulation was noticed (Table 8 and Table 9). Likewise, no change in the pH of the optimized formulation stored at diverse temperatures was recorded. The nanoemulgel formulation remained homogeneous at 8 °C, and a slight change in the spreadability and viscosity of the formulation was recorded. The nanoemulgel formulation was kinetically stable (Table 9) m and remained stable at 48 °C; however, the spreadability of the nanoemulgel increased and viscosity decreased followed by change in color of the formulation (from white to dark brown) (Table 10). The viscosity of pharmaceutical formulations is a crucial factor as the release of drugs and the consistency of semisolid dosage forms rely on it [46]. We observed that viscosity of the nanoemulgel was decreased with increase in temperature from 8 °C to 48 °C. Additionally, an increase in temperature led to an increase in the spreadability of the formulation, as the viscosity decreased. This is due to the inverse relationship between viscosity and spreadability of the nanoemulgel. Viscosity of the semisolid formulation decreased with an increase in temperature [47]. Thus, room temperature (25 °C) was considered the most suitable storage of nanoemulsion gel. Increasing the temperature was found to increase the spreadability of the prepared formulation, which could be advantageous for the patient, particularly in the buccal cavity where the temperature is higher than room temperature. This improvement in spreadability can facilitate application of the formulation. As far as physical stability is concerned, the formulation stability last 3 months and no phase separation occurred after centrifugation at 1000 rpm, 2500 rpm and 3000 rpm for 15 (Table 8, Table 9 and Table 10). ## 3. Conclusions In recent years, there has been a growing interest in developing effective methods for delivering essential oils before their incorporation into different forms of medication. In particular, the use of essential oils as potential sources of antimicrobial agents for applications in treatments, food preservation, and packaging has garnered significant attention. Commercial applications of essential oils were limited as a result of issues such as poor solubility, solvent toxicity, volatility, and a strong organoleptic taste. For the development of essential-oil-based antimicrobial nano-systems, nanoemulsion are a good option due to their biocompatibility, biodegradability, nontoxicity, and target selectivity. As a result of encapsulation and delayed release from the optimized oil in water nanoemulsion formulation, cinnamon and clove essential oil-based products demonstrated promising antibacterial and antiquorum sensing activities. Due to its mucoadhesive properties and delayed release profile, it was concluded that that converting nanoemulsion into nanoemulgel would be more advantageous due to sustained release and an enhanced time of contact due to mucoadhesive nature. ## 4.1. Chemicals and Bacterial Strains Cinnamon and clove were purchased from a local market and authenticated by a taxonomist named D.I. Khan at the Institute of Biological Sciences (IBS), Gomal University. The isolation of essential oil was achieved using Clevenger apparatus. Olive oil, Carbopol 940, and triethanolamine were purchased from Sigma Aldrich, St. Louis, MI, USA. The bacterial growth media used included Tryptic Soy Broth (Hi Media, Mumbai, India), nutrient agar (Oxoid, Hampshire, UK), and Luria-Bertani Broth (LB) (Oxoid, Hampshire, UK). The bacterial strains were isolated from dental plaques of female diabetic patients with informed consent. The ethical approval for study was obtained from the Ethical Review Board, Gomal University, Pakistan, (Approval No. 335/ERB/GU) in accordance with the WMA declaration of Helsinki and ARRIVE guidelines for animals. The bacterial strains were isolated from dental plaques of female diabetic patients with informed consent and standard microbiological methods were used to isolate and purify bacteria. The Congo red agar method was employed to verify the existence of biofilm production. Genomic DNA extraction from bacteria, agarose gel electrophoresis, and PCR was performed followed by 16S rRNA gene sequencing, which was performed in the National Culture Collection of Pakistan (NCCP). The genes alignment was performed for an exact match with the NCBI nucleotide database using nBLAST. The strains were identified as Staphylococcus epidermidis, Staphylococcus aureus, Pseudomonas aeruginosa, Bacillus chungangensis, Bacillus paramycoides and Paenibacillus dendritiformis (Specimen deposited in Pakistan culture bank). The Chromobacterium violaceum (DSM 30191) was purchased from DSMZ, Braunschweig, Germany. ## 4.2. Essential Oil Extraction Essential oils (cinnamon and clove) were extracted in lab using a hydrodistillation method. Detailed component analysis information was recorded (Supplementary Materials). ## 4.3. Determination of HLB values The Hydrophilic Lipophilic Balance (HLB) was employed to calculate the amount of surfactants required for an oil to remain in a solution. The HLB value indicates the optimal ratio of surfactants necessary to form a stable emulsion, whether it is an oil in water (o/w) or water in oil (w/o) type of emulsion. The HLB required for a stable emulsion can be calculated using Equation [48], as shown in Table 11. [ 1]HLB=Wa×HLBa+(Wb×HLBb)/Wa+Wb where, Wa represents the amount (weight) of the first emulsifier, Wb shows amount (weight) of the 2nd emulsifier, and HLBa and HLBb are the representing the hydrophilic lipophilic balance of emulsifier a and b, respectively. ## 4.4. Pseudo Ternary Phase Diagram Using a standard protocol [49], a pseudo ternary phase diagram was constructed to analyze the phase behavior of the several formulation components. In a short, 100 mL of a 4:1 surfactant (Tween 80) and co-surfactant (Span 80) mixture (Smix) were prepared, and 16 various combinations of oil (olive oil) and Smix were made in unique volume ratios ranging from 1:9 to 9:1 (1:9, 1:8, 1:7, 1:6, 1:5, 1:4, 1:3, 1:2, 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1). The mixture of the Smix and olive oil was titrated against distilled water. Visual inspection was performed after each $5\%$ addition of aqueous phase to the oil and Smix mixture, and the results were recorded. Finally, data were recorded after the fractions of water, oil, and Smix were selected and designed on a phase diagram with one axis characterizing the aqueous phase, the other representing the oil phase, and the third representing the Smix. On the basis of succeeding points, distinct formulations were selected from each phase diagram produced for various Smix ratios. The oily phase was selected in a such concentration to easily dissolve the $3\%$ essential oil which was used as the active ingredient. The oil concentration from the phase diagram was chosen as the numeral of five ($5\%$, $10\%$, $15\%$ and $20\%$) and lowest concentration of surfactant was taken for nanoemulsion preparation. ## 4.5. Preparation of Nanoemulsion The preparation of essential oil-loaded nanoemulsions involved blending $3\%$ w/w of the essential oil with $5\%$, $10\%$, $15\%$, and $20\%$ of olive oil using a vortex mixer and the appropriate Smix ratios. Water was then added and mixed until a stable emulsion was obtained [50]. ## 4.6. Globules Size Zetasizer 1000 HS (Malvern instrument, Worcestershire, UK) was used to determine the globule size of the prepared nanoemulsion formulation [51]. The light scattering was seen at 25 °C at a scattering angle of 90°. ## 4.7.1. Kinetic Stability The kinetic stability of formulation was determined using centrifugation method [52]. The centrifugation of samples was performed at 1000, 2000, and 3000 rpm for 15 min at room temperature (25 °C). The appearance of the emulsion and phase separation before and after the centrifugation cycle was recorded to measure the formulation’s kinetic stability. ## 4.7.2. Thermodynamic Stability Thermodynamic stability of the nanoemulsion was determined by using cooling-heating cycle [53]. Briefly samples were held at 4 °C for 48 h and then transferred to 48 °C for 48 h. The nanoemulsion was then examined for any changes, such as phase separation. ## 4.8. Preparation of Nanoemulgel Following major steps were used during preparation of nanoemulgel [29]. ## 4.8.1. Preparation of the Gel To create the gel base, varying concentrations ($0.5\%$, $1\%$, $1.5\%$, and $2\%$) of the gelling agent Carbopol 940 were mixed with distilled water and continuously stirred until the gelling agent fully dissolved at room temperature (25 °C). The use of different concentrations of the gelling agent was explored to investigate their influence on the viscosity and spreadability. ## 4.8.2. Incorporation of the Prepared Gel into Already Prepared Nanoemulsion Prepared nanoemulsion was added dropwise into the prepared gel and stirred for approximately 20 min at room temperature (25 °C) to convert it into emulgel. Finally, the pH of the emulgel was adjusted as the pH of the buccal cavity. ## 4.9. Optimization of the Nanoemulgel Formulation Nanoemulgel formulation was optimized based on viscosity, spreadability and concentration of gelling agent. In order to optimize the formulation, four different formulations were prepared with varying concentration of gelling agent (0.5, 1, 1.5 and $2\%$) (Table 12). ## 4.10.1. FTIR Analysis FTIR analysis was performed with aim to see any incompatibility between the excipients and active components of the formulation as described earlier [30]. The FTIR analysis of olive oil, cinnamon essential oil and Carbopol 940 performed in both loaded and unloaded formulations of nanoemulgel. ## 4.10.2. Viscosity The viscosity of all formulations was determined using standard procedure [53]. The Brookfield viscometer spindle no. 4 was used to determine the viscosity of nanoemulgel at 25 ± 2 °C and 6 rpm (Digital, Labtronics, Panchkula, India). ## 4.10.3. Physical Appearance All the prepared nanoemulgel formulations were observed visually for their color, homogeneity, consistency, and phase separation [54] ## 4.10.4. pH Determination The pH of nanoemulgel formulations was determined using previously described method [54]. The pH was determined by using pH-meter by putting the tip of the electrode into the emulgel without dilution till 2 min and the result was recorded. ## 4.10.5. Spreadability A modified method was adopted to determine the spreadability of the formulations [55]. Briefly, two glass slides (7.5 cm length and 2.5 cm width) were employed, one was fixed to the wooden board and the other was movable, with a thread running through a pulley and holding a weight. Afterwards, the formulation was sandwiched between the two plates. A weight of 100 g was placed on the upper slide for 1–2 min to release trapped air between the slides and create a homogeneous layer of the formulation. Later, the weight (100 g) was removed, and a 30 g weight was placed over the pulley to apply a pull to the top slide. The results were recorded in seconds and expressed as the time it took a moving slide to move a predetermined distance (6.5 cm). Spreadability was calculated using the following formula:[2]S=M×L/T where M is the weight attached to the upper slide, L is the length of the glass slides, and T is the time it takes to separate the slides. ## 4.10.6. Scanning Electron Microscopy of Nanoemulgel The size and surface morphology of the nanoemulgel inner oil phase was determined using field emission scanning electron microscopy (FE-SEM LEO 1525 ZEISS) with electron high tensions of 5 and 15 kV. To capture the images, the material was placed on stubs with double-sided carbon tape and coated with an 8 nm layer of chromium. The SEM images were taken at magnifications of 1.00 KX using secondary electron (SE) and in-lens detectors. The Image J software was utilized to calculate the average size distribution of droplets based on the SEM images. [ 56]. ## 4.10.7. Muco-Adhesion Test Mucoadhesive properties of nanoemulgel were determined according to a previously described method [57]. Briefly, goat buccal mucosa was obtained from the local slaughterhouse, placed in phosphate buffer (pH 6.8), and immediately transferred to the lab. After that, one pan of the balance was removed, and a vial was attached so one piece of the buccal mucosa was attached on the vial, whereas the other piece of the buccal mucosa was attached to another vial fixed on the table surface. The nanoemulgel was sandwiched between the two vials and pressed to remove any air in between the vials. Afterwards, a weight was added to the other pan of the balance until the two vials became detached from one another. The weight, on which detachment of the vials occurred, was recorded, and the muco-adhesive strength of the nanoemulgel was determined by the following formula [58]. [ 3]Mucoadhesive strength=weight grams×Gravitaional acceleration (ms2)Area of the vial opening (cm2) Weight was load applied on pan to detach the pan; gravitational acceleration is 9.8 m/s2. ## 4.10.8. Drug Contents Analysis A spectrophotometric approach (UV-Vis) [55] was used to calculate the essential oil contents. After being diluted with ethanol (1:1000), homogenized in an ultrasonic bath, and measured at 230 nm for clove essential oil (eugenol) [59] and 290 nm for cinnamon essential oil, nanoemulgel formulations were used (cinnamaldehyde) [60]. From the standard curve, the essential oil content was calculated. ## 4.10.9. Encapsulation Efficiency of Nanoemulsion The effectiveness of the nanoemulgel encapsulation was assessed using the previously reported approach with a minor modification [61]. It was calculated by comparing the quantifiable free essential oil to the original amount of the oil that was added to the formulation. The encapsulation efficiency was calculated by using the following:[4]EE(%)=[(initial conc. of EO−free EO)/initial conc. of EO]×100 where EE = encapsulation efficiency, EO = essential oil. ## 4.10.10. Drug Release Profile Release profile was determined using a Franz diffusion cell (Permegear, model 4G01-00-05-05) [62] equipped with a 7 mL acceptor compartment capacity and a diffusion area of 0.2 cm2 was used for the release investigation. Initially, cellulose membranes (14 kDa, dialysis tubing cellulose membrane (Sigma-Aldrich, St. Louis, MI, USA) were soaked in receptor fluid for 24 h at 37 ± 1 °C, followed by placement in donor and receptor compartments. To mimic physiological conditions, a buffer phosphate solution (pH 6.8) was used, and samples were obtained at regular intervals (0.5, 1, 1.5, 2, 4, 8, 12, 16 and 24 h). Collected samples were analyzed for cinnamaldehyde using a UV spectroscope set to 230 nm for eugenol [59] and 290 nm for cinnamaldehyde [60]. The cumulative amount of essential oil released vs. time was plotted to quantify clove oil and cinnamon oil release across the membrane. A similar procedure was repeated with pure essential oils. ## 4.10.11. Ex vivo Permeation An ex vivo permeation study was performed by using the goat buccal mucosa [63]. The buccal mucosa was obtained from goat and placed in a phosphate buffer pH 6.8. The buccal mucosal membrane was separated from the underlying tissues through sharp surgical blades by a registered veterinarian doctor. The buccal membrane was placed between the donor and acceptor compartments of the Franz diffusion cell. A total of 0.5 g of the emulgel was placed in the donor compartment and the Franz diffusion cell was maintained at 37 ± 1 °C. The acceptor compartment was filled with phosphate buffer pH 6.8, and sampling was performed at different time intervals (0.5, 1, 1.5, 2, 4, 8, 12, 16 and 24 h). The permeated essential oil concentration was analyzed by using spectrophotometer at 290 nm for cinnamon and 230 nm for clove oil. ## 4.10.12. Mucosal and Skin Irritation Test A skin irritation test was performed using previously described protocol [64]. Briefly, Wister rats (200–250 g) were divided into three groups. A $0.8\%$ v/v aqueous solution of formalin was used as the positive control, whereas blank nanoemulgel formulation was used as negative control. Group 1 was the positive control (formalin group), group 2 was negative control (blank nanoemulgel formulation group), whereas group 3 was the test group (the essential oil-loaded nanoemulgel formulation group). Wister rats were subjected to a skin irritation test using the Draize scoring method (Table 13) [65,66]. Furthermore, the essential oil-loaded nanoemulgel formulation was applied to goat buccal mucosa to check the mucosal irritation. Animals were checked for evidence of erythema and edema after the standard irritant and test formulations were removed, and responses were graded at 0 h, 24 h, and 48 h. The above-mentioned test was performed to check any sort of irritation on the skin in general if the same formulation would apply to skin and soft tissue infections. ## 4.10.13. Stability Studies Stability of the semisolid the nanoemulgel preparation was determined [67] for 3 months and the optimized formulation was kept at different temperatures: 8 °C, 48 °C + $75\%$ relative humidity (RH), and at room temperature (RT). Samples were characterized for organoleptic changes, physical stability, viscosity, and spreadability after each 15th day. ## 4.11.1. Antimicrobial Property A modified disc diffusion method [68] was used to determine the diameter of the zone of inhibition on petri plates (90 mm) using Mueller–Hinton and Sabouraud agar media. After dying for some time, the 24 h old strain of the bacteria fungal strain was spread on the media. After that, filter paper discs with a diameter of 6 mm were cut and sterilized before being carefully dropped in the center. Afterwards, 12 µL of essential oil for antibacterial testing was put on these discs and allowed to stay for 30 min. Then, the plates were incubated in an oven at 37 °C for 24 h to 48 h. Afterwards, the inhibition diameters were measured around the disc, and results were recorded. ## 4.11.2. Antiquorum Sensing Activity A standard method [69] was used to assess the ability of isolated drugs to disrupt quorum sensing. In petri dishes, 24 h old strain of the C. violaceum ($\frac{1}{100}$ ratio) was streaked onto the LB agar. 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--- title: Influence of Dietary Protein Source and Level on Histological Properties of Muscle and Adipose Tissue of Lambs authors: - Davide De Marzo - Caterina Losacco - Vito Laudadio - Vincenzo Tufarelli - Youling L. Xiong journal: Foods year: 2023 pmcid: PMC10048687 doi: 10.3390/foods12061284 license: CC BY 4.0 --- # Influence of Dietary Protein Source and Level on Histological Properties of Muscle and Adipose Tissue of Lambs ## Abstract The muscle and adipose tissue histological properties in wether and ewe lambs of Gentile di Puglia breed, fed diets including two protein sources [soybean meal (SB) and SB plus distillers dried grain with solubles (DD)] and three protein levels (12.5, 15.7, and $18.9\%$) were evaluated. Muscle samples were collected from the longissimus/rump, cut, and stained (reciprocal aerobic and anaerobic stains) for muscle fiber typing and fat cell characterization. Fibers were classified as α-red, β-red, and α-white. Lambs fed SB had larger α-white ($p \leq 0.10$) and smaller-diameter β-red and α-red fibers ($p \leq 0.05$). Among dietary protein levels, lambs fed $12.5\%$ protein exhibited the highest percentage of α-red and the greatest diameter of α-white fibers, whereas wethers had a higher percentage of α-red ($p \leq 0.05$), and ewes had a higher percentage of α-white fibers ($p \leq 0.05$). Intramuscular fat cells were larger ($p \leq 0.10$) in ewes than in wethers. Lambs in the group fed $12.5\%$ protein had larger subcutaneous fat cells at the sacral vertebrae location. Overall, both sources and levels of dietary protein had significant effects on lamb muscle and fat histological features, suggesting the potential of modulating muscle or fiber types through dietary protein strategies. ## 1. Introduction Lamb is an important source of meat that is widely consumed worldwide, and it is considered essential in many countries for cultural and ethnic reasons. According to the OECD-FAO [1], approximately 15 M tons of lamb meat was consumed, and the level is expected to increase to 16 M tons by the end of 2023. Small ruminants are the prevalent species of domestic livestock animals in the Mediterranean region. In many areas of the region, sheep are commonly reared under semi extensive or extensive conditions according to pasture characteristics. In these areas, an ovine rearing system is conducted traditionally by using local sheep breeds due to their exceptional adaptation to environmental conditions and utilization of existing feed resources. The number and size of muscle cells present in the body of animals are important for meat production. It has been recognized that diversity of muscle fibers (i.e., number, diameter, and type) have a significant effect the qualitative traits of meat [2]. Metabolic and contractile characteristics are important manifestations and predictive factors for the heterogeneity of muscle fibers. In fact, skeletal muscle fibers are normally classified based on selective cellular components that are directly involved in the specific contractile and metabolic activities. In the sheep fetus, muscle fiber numbers are complete at approximately 80 days of gestation [2,3]. These numbers are determined genetically and have heritability estimates of 0.17–0.38. In fresh muscle immediately post-mortem, adenosine triphosphate (commonly referred to as ATP) is produced through the anaerobic glycolytic pathway from glucose and stored glycogen, and this is a part of the post-exsanguination biochemical conversion of muscle to meat. By a natural process, lactic acid, the final product of glycolysis, is accumulated in muscle due to the cessation of blood circulation [2]. If the glycolytic fibers are dominantly distributed in an individual muscle, rapid post-mortem glycolysis occurs. Conversely, if a muscle is predominantly comprised of oxidative fibers, less lactic acid is produced due to the deficiency of substrates (glucose and glycogen). The accumulation of lactic acid results in a rapid muscle pH decrease reaching an ultimate level of 5.4–5.8, depending on the relative preponderance of different fiber types in the muscle tissue. Therefore, the composition of muscle fibers is rather crucial in regards to the post-mortem metabolism pattern and glycolytic products during muscle to meat conversion, which ultimately affects the qualitative traits of meat. The importance of muscle fiber types in studying meat characteristics is mainly related to two aspects: firstly, muscle fiber growth and development reflect the general growth curve pattern of livestock animals and therefore the ultimate body size; secondly, the final product (meat) to be served to consumers is of great interest since it determines meat acceptability based on organoleptic evaluations [4]. It was reported that a significant correlation between βR fiber size and carcass juiciness and tenderness scores existed in lambs. Moreover, it is well established that muscles with different physiological functions generally differ in metabolism. Being tonically active, red-type skeletal muscle generally exhibits a higher rate of oxidative metabolism than white-type skeletal muscle. However, most skeletal muscles are a mixture of red and white fibers; the relative percentages vary with anatomic locations on the carcass as well as with rearing conditions, genetics, feeding management, and amino acids composition of feeds. A deeper understanding of production factors that influence the physiology of muscle growth and development is important not only from the perspective of carcass yield but also in terms of meat quality and sensory properties. Many authors have reported that the size of fibers in sheep increased with age, sex, exercise, and improved nutrition [5]. Moreover, different sources of energy in lamb rations appeared to cause a physiological shift from intermediate muscle fibers to white muscle fibers [6]. These authors suggested that the ATPase activity may not be fixed at birth. In skeletal muscles, there are two basic fiber types: alpha (α) and beta (β) [7]. The beta fibers are “red” (β-red) and generally do not change, whereas alpha fibers are initially “red” but may be transformed from an alpha red (α-red) to an alpha white (α-white). Ashmore et al. [ 7] suggested that the increase in muscle size is due in part to the conversion of smaller α-red fibers to larger α-white fibers. Johnston et al. [ 8] reported that the percentages of intermediate and white muscle fibers decreased as the protein content in the ration increased, and Facciolongo et al. [ 9] indicated that the protein sources did not influence the physical characteristics of the meat. Furthermore, it appears that various types of dietary restrictions may have a selective effect on one muscle type but not another. An increased fat deposition is due both to hyperplasia and hypertrophy of adipocytes [9,10,11,12]. In sheep, subcutaneous fat depots increased in response to hyperplasic growth between 8 and 14 months of age [13]. Numerous researchers have reported that adipocytes in porcine subcutaneous fat tissue do not develop uniformly; instead, they accumulate lipid at different stages of growth [14,15,16,17]. For example, in bovine muscle, intramuscular fat cells have been found to differentiate as clusters of varying sizes [18]. Despite the general knowledge of the role of dietary proteins in muscle cell development and growth, few studies had specifically compared different source and level proteins on muscle fiber characteristics in lambs. Therefore, the present study was conducted to evaluate the effects of different dietary protein sources and levels on the histological properties of lamb muscle and subcutaneous fat. ## 2.1. Lamb Production and Harvest Thirty-six lambs of Gentile di Puglia breed were weaned (40 ± 2.0 days old) at approximately 13.5 ± 0.45 kg of body weight and allocated in equal numbers to six dry lot feeding regimes (Table 1). Lamb groups 1, 2, and 3 received a corn-soybean meal (SB) diet, which contained 12.5, 15.5, or $18.9\%$ crude protein, respectively, until the slaughter weight of approximately 20 kg (at 70 days of age). Groups 4, 5, and 6 received the same three levels of protein, but with distillers dried grain soluble (DD) replacing part of the SB. The total mixed rations (TMR) as pellets were formulated to meet the nutrient requirements for lambs and to be isocaloric according to Laudadio and Tufarelli [19]. Diets were formulated to contain a mean of 9.21 MJ/kg of dry matter (DM) of metabolizable energy (ME) utilizing feed analysis in each treatment. For forages, the rations (oat hay) were re-chopped by grinding at 25 mm and subsequently mixed and pelleted by the team (ca., 8 mm in diameter) to maintain the integrity of fibrous elements. This process was done to reduce differences in physical form and prevent the feed selection bias of the experimental lambs. The TMR were provided to animals in two equal meals. Feed rationing was applied to avoid unnecessary feed ingestion by subjects, which in previous trials had led to issues of lambs’ death. Clean drinking water was available ad libitum. The body weight (BW) of each lamb was recorded weekly prior to feeding at 07:00 h. The feed conversion ratio (FCR) was assessed as the ratio of BW gain to DM intake. Refusals of feed were sampled daily, weighed, and individually analyzed. The samples of collected refusals along with the feed offered to each animal were dried at 105 °C for 24 h to determine DM intake. Before the study was started, all lambs were inoculated against clostridial infections and treated for internal parasites. During the course of the feeding trial, lambs were regularly observed for health and well-being by a veterinarian. At the end of the feeding trial, lambs were humanely harvested according to the University Institutional Review Board protocol. Carcasses were chilled in a 3 °C walk-in cooler for approximately 48 h and then evaluated and sampled. ## 2.2. Muscle Sample Lamb carcasses were evaluated and then fabricated into wholesale cuts. The measurements included the depth of fat over the spinous process, the depth of fat over the tail (rump), and the depth of fat over the leg (30 cm from the shank end). Samples for histological examination were obtained from the center of the longissimus (left side at the 13th rib) and the base of the tail (rump) over the sacral vertebrae. Duplicate 1 cm3 samples were immediately immersed in liquid nitrogen. Frozen muscle samples were mounted on a Cryostat chuck so that fibers were perpendicularly oriented to the blade. After a 20 min equilibration at −20 °C, the samples were sectioned to 16 µm thickness using a Damon freezing microtome-cryostat (Damon/IEC Division, Damon Corp., Needham Heights, MA, USA). Serial sections were mounted on glass microscope slides, allowed to air dry, and then stained with NADH-TR. Samples were myofibrillar ATPase reacted and treated with Oil-Red-O and Hematoxylin [20] at alkaline pH [21]. Once the tissue section was stained, a microscope cover slip was placed over the tissue section and fixed in place with glycerol jelly. The sample slides were observed under a Zeiss photomicroscope (Carl Zeiss, Inc., New York, NY, USA). Several fields of each stained section were photographed at 25× magnification with the bright field setting on the light microscope. A stage micrometer with 0.01 mm graduations was also photographed for size definition and scaling. The photomicrographs were enlarged to a 12.7 × 17.8 cm dimension to facilitate the analysis and differentiation of fat cells and muscle cell types. Muscle cells were typed on the basis of staining reaction into red (α-red), intermediate (β-red), and white (α-white) types. All fibers inside a field size (6 × 4 cm) were counted and then measured using a Zeiss particle size analyzer (Carl Zeiss, Oberkochen, Germany). An enlarged photograph of the micrometer scale was also measured. This micrograph was used in the conversion of the instrument values to maximum round diameter (MRD) of the cell (µm) [5]. The MRD for each fiber type was calculated based on the following equation: fiber diameter (micrometers) = actual value for micrometer scale/instrument value for micrometer scale X instrument value for fiber × 1000. In addition to fiber diameter, the percentage of cells for each of the three fiber types was calculated by dividing the number of each type by the total number of counted cells. For the measurement of fat cells, the photomicrographs of Oil-Red-O slides were used. The MDR of fat cells were measured on each sample, and the values were converted to µm by the same procedure used for muscle fibers as described above. ## 2.3. Statistical Analysis Data were analyzed by the least-squares procedure assuming a mathematical model that included the fixed effect of protein source, protein level, and the protein source × protein level interaction. The pen within protein source × protein level was included as a random effect. Differences among means were tested for significance using the protected least significant difference procedure [22]. ## 3. Results and Discussion Meat consists of countless tissues, predominantly muscular tissues, which are composed of muscular fibers. These fibers are the basic constituents of skeletal muscle and could be divided into different types. The relative distribution and changes in the types of muscle fibers during sheep production have a direct impact on the quality of lamb meat. For example, red fibers are of a higher myoglobin content (color), generally higher pH, and more taste-impactful nucleotides than white fibers [4]. To the best of our knowledge, very limited literature has been published in recent years on the effects of different dietary protein sources and levels on the histological properties of lamb muscle and subcutaneous fat. The consumer demand for ovine meat is on a steady rise, which underscores the need for new and further investigations. Soybean (SB) meal is an important protein source for animal nutrition; however, the use of this conventional ingredient increases feed costs [23]. Thus, using agricultural by-products, such as DD, as a protein source to replace SB can reduce costs as well as ruminal protein degradation [24]. However, it seems that the feed value of DD may vary according to the inclusion level [25]. Generally, DD have mostly been fed to beef and dairy cattle, swine, and poultry [26]. Even though DD should be appropriate for lambs, the feeding value of DD in finishing diets fed to lambs is not well-defined because only a limited amount of research evaluated the use of DD in lamb rations [27]. However, given the physicochemical properties of DD, it is being used in finishing diets for feedlot lambs to partially replace corn and SB. Accordingly, the present study was conducted to test the efficacy of dietary DD at different protein levels in comparison with SB for the modification of muscle fiber types in lambs. The least-square means for size and population of muscle fibers in the longissimus muscle of lambs according to dietary protein source and level are presented in Table 2. For comparing the levels of protein, larger fibers were noted in lambs fed lower percentages of protein, but the differences were significant ($p \leq 0.05$) only for the white fibers. This was in agreement with Wang et al. [ 28] who reported that the diameter of muscle fibers in the medium protein group ($12.1\%$) was significantly larger than that in the low protein group ($10.1\%$), whereas the density of muscle fibers showed the opposite trend. The protein source obviously affected α-white fibers with fibers of the SB lambs being larger ($p \leq 0.10$). Although no significant difference ($p \leq 0.05$) was found between sexes, the mean values for α-red (36.6 µm) and α-white (42.3 µm) were the largest for wethers, whereas the mean value for β-red fiber (43.4 µm) was higher for ewes. One interesting observation was that the β-red and α-white fibers were similar in diameter, but both types were larger than α-red fibers. These results, however, are not in agreement with several other studies reported in the literature. Gauthier [29] reported that βR fibers had the smallest and α-white the largest diameter. Moody et al. [ 30] claimed that the βR fibers were larger than the αR and α-white fiber types. In this study, increasing the protein level slightly decreased the fiber diameters. The percentage of α-red fibers decreased, and the percentage of α-white fibers increased in the longissimus as the protein content in the ration increased ($p \leq 0.05$) with no significant ($p \leq 0.05$) difference for β-red fibers. Johnston et al. [ 31] reported that, in general, as the energy level in the ration increased, the percentage of intermediate muscle fibers decreased and the percentage of α-white muscle fibers increased. Ashmore et al. [ 7] concluded that α-red fibers have the capacity to transform into α-white fibers, and Meunier et al. [ 32] showed that muscle fibers were dynamic structures that can switch from one type to another. Therefore, from the results of this study and those presented by other researchers, it would seem that increasing the protein level influences the transformation from α-red to α-white fibers. In the present study, the effect of protein source on the number of muscle cells (fibers) was limited to β-red fibers ($p \leq 0.05$) as the longissimus of the DD lambs contained $8.92\%$ βR fibers compared to $7.39\%$ for the SB lambs. Sex influenced the percentage of the α-red and α-white fibers ($p \leq 0.05$) with wethers having $58.0\%$ α-red fibers compared to $52.0\%$ for ewes, whereas ewes had a higher percentage ($40.5\%$ vs. $33.2\%$) of α-white fibers ($p \leq 0.05$). These data support the premise that fiber differentiation, that is, conversion from α-red to α-white, accompanies physiological maturity. The influence of diet on lamb muscle fibers extends to other nutrients than proteins. As reported by Santello et al. [ 33], the semitendinosus muscle of ½ Dorper-Santa Inês lambs finished in different feeding systems (confinement and grazing plus oil supplementation) had the largest diameters for oxidative-glycolytic fibers (43.7 μm), followed by glycolytic fibers (36.0 μm) and oxidative fibers (20.3 μm). Lambs fed on sunflower grain ($9.10\%$) presented similar-sized diameters for red fibers (35.4 μm), intermediate fibers (36.0 μm), and white fibers (35.3 μm) in relation to the semitendinosus muscle. However, for the Longissimus lumborum muscle, the diameters of the different fiber types exhibited some differentiation, with values of 28.7, 29.8, and 32.2 μm for red, white, and intermediate fibers, respectively [34]. The results suggested that the effect of diet is rather complex, although the type and amount of protein in the diet is important, other nutritional factors could have a contributing role. Therefore, the inter-relationship warrants further investigations. The least-square means for fat thickness in longissimus, leg, and rump are presented in Table 3. There were no differences among groups due to protein levels for longissimus or leg fat. However, some differences, albeit not linear, occurred in rump fat. It would be difficult, however, to conclude that such differences were due to protein level when no differences occurred over the longissimus or on the leg. When protein sources were compared, the fat over the leg was greater ($p \leq 0.05$) for the SB lambs. Since the actual difference was small and no significant differences were observed for the longissimus or rump, it is doubtful if the leg difference is meaningful. Fat thickness was greater ($p \leq 0.05$) over the longissimus for ewes than for wethers, which agrees with Gutiérrez-Peña et al. [ 35]. Also, Ahmad et al. [ 36] reported that female lambs had the highest fat mass and adipocyte. The least-square means for subcutaneous fat cell number and diameters are given in Table 4. As reported by other researchers, fat cell diameters generally increased with the level of energy in diet [37]. As dietary energy levels increase, the body fat normally increases with a concomitant increase in fat cell size and in number [11]. However, an increase in dietary protein, as used in this study did not appear to have a consistent effect on fat cell diameters. The fat cell diameters were larger ($p \leq 0.10$) in ewe lambs, which were closer to physiological maturity and also fatter than wether lambs. Total intramuscular fat is due both to the number and size of fat cells. Size could be measured but, because of the uneven distribution of fat cells, it was difficult to quantify fat cell numbers. The lower cell numbers for both longissimus and rump were found in the $12.5\%$ protein group. The number of fat cells in longissimus increased for the $15.7\%$ protein group and decreased for the $12.5\%$ group. However, in the rump, the population of fat cells increased for the 15.7 and $18.9\%$ groups and the changes were ($p \leq 0.05$). All diameters followed an inverse pattern. Protein source affected neither the number nor size of cells from either location. Sex affected both size and number of fat cells with ewes having larger cells ($p \leq 0.05$ for the longissimus) and wethers having more cells ($p \leq 0.05$) for rump, resulting in partial accord with those of Facciolongo et al. [ 38]. These authors found that diet × sex interaction had a significant impact on the carcass fat incidence, and this was unchanged by sex in the lambs fed on SB meal. The influence of sex on the intramuscular fat proportion appears to be quite controversial. In previous research [39], there was no difference in the relation to sex for slaughtered lambs at a similar body weight as in the present study. Nevertheless, other authors [40] observed a higher proportion of fat muscle in females, ascribing this to the greater predisposition in females to build up fat at an earlier age, and to their slower growth level, which subsequently means that they reach the slaughter body weight later. Moreover, it was reported that the differences between sexes were more evident in suckling lambs than in fat lambs [41]. Furthermore, Bloor et al. [ 42] concluded that sex had a major impact on body fat distribution, and males were more susceptible to visceral adiposity and obesity-related diseases than females, although the underlying mechanisms for these gender differences were not well understood. This was probably due to the difference in maturity as the ewes at equal weight were closer to physiological maturity. Further research is required to gain insight into the molecular and cellular mechanism(s). ## 4. Conclusions Based on the results of the present study, it can be concluded that the dietary protein source fed to lambs had significant effects on the characteristic of muscle fibers; in particular, on α-white fibers in both ewe and whether with respect to fiber size and distribution. Moreover, the lamb sex significantly influenced the features of subcutaneous fat tissue. Further, both sources and levels of dietary protein had a measurable effect on lamb muscle and fat histological characteristics. Of special note is the apparent conversion of α-red (intermediate) fibers to α-white fibers in lambs, especially in ewes, corresponding to increasing amounts of protein feed from distillers dried grain with soluble meal. These findings are significant for lamb nutrition and meat quality when alternative sources of proteins (soybean meal) are considered in lamb production. More research is warranted to deeply verify the present findings as well as to identify other nutrition factors, such as feed amino acids profile, that may contribute to fiber type differentiation. 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--- title: Mediating Effect of Motivation on the Relationship of Fitness with Volitional High-Intensity Exercise in High-School Students authors: - André Bento - Luis Carrasco - Armando Raimundo journal: Healthcare year: 2023 pmcid: PMC10048690 doi: 10.3390/healthcare11060800 license: CC BY 4.0 --- # Mediating Effect of Motivation on the Relationship of Fitness with Volitional High-Intensity Exercise in High-School Students ## Abstract We aimed to investigate the relationship between physical fitness and motivation in adolescents and analyze if the associations of physical fitness with volitional exercise intensity in adolescents are mediated by motivation. The participants were 108 adolescents (58 girls 16.0 ± 0.92 years). Cardiorespiratory fitness (CRF) was assessed using the Yo-YoITL1, and the push-up test was used to evaluate strength. Body composition was measured by bioelectrical impedance analysis. The intervention was applied in the first 10–15 min of each Physical Education class (PEC), twice a week, for 16 weeks and ranged from 14 to 20 all-out bouts intervals, adopting a 2:1 work to rest ratio. A cut-point of ≥$90\%$ of the maximal heart rate (HR) was used as a criterion for satisfactory compliance with high-intensity exercise. Volition intensity was assessed through a forearm wearable plethysmography heart rate sensor to ensure compliance with the exercise stimulus at the predetermined target HR zone. Motivation was estimated with a validated questionnaire (BREQ-3). Mediation effects were estimated using bootstrapped $95\%$ confidence intervals and were deemed significant if zero was not included in the intervals, and values below 0.05 were considered to indicate statistical significance. The mediation analysis revealed a non-significant indirect effect of physical fitness through motivation on exercise intensity, specifically on CRF (B = −0.0355, $95\%$ BootCI [−0.5838; 0.4559]), muscular fitness (B = −0.7284, $95\%$ BootCI [−2.0272; 0.2219]) and body fat ($B = 0.5092$, $95\%$ BootCI [−0.4756; 1.6934]). These results suggest that high or low values of motivation did not increase or decrease volitional high-intensity exercise, and lower levels of fitness (CRF, muscular and body fat) were associated with higher volitional exercise intensity. These findings highlight the need for regular moderate-to-vigorous physical exercise for maintaining or improving physical fitness, regardless of motivation regulations, and emphasize the importance of new strategies in PEC with acute vigorous-intensity activities that retain the health-enhancing effects. ## 1. Introduction Despite the numerous benefits of regular physical activity (PA), Western children and adolescents spend too much time in sedentary behaviors, which is worsening every decade [1]. The World Health Organization (WHO) stated that adolescents should achieve at least an average of 60 min per day of moderate-to-vigorous PA (MVPA) and must limit sedentary time [2]. The suggested 150 min per week of moderately vigorous exercise or PA is frequently not achieved by individuals due to a lack of motivation [3]. A school is a place where adolescents spend most of their day. It is known that schools and Physical Education classes (PEC) are privileged spaces and promoters of positive changes for the rest of life [4,5,6], in which time-efficient approach interventions have a prominent role. There are several reasons why PECs should be taught in schools, but the effectiveness of current methods for PEC is frequently challenged, notwithstanding this point of view [7]. It has also been demonstrated that most PEC programs have difficulty attaining the full range of health and educational outcomes included in the PEC curriculum [5]. Professionals constantly point to an overloaded curriculum that creates additional time demands as the main barrier to achieving these parallel educational and health goals [4,6]. Recreational sport and exercise can be performed for their associated enjoyment or for the challenge of participating in an activity [8]. Volition consists of meta-motivational processes; when higher-level action control processes fail, volition may consist of increasing vigor. Research on the importance of volition in the area of exercise psychology is very limited; however, volitional skills have proven to be a sound predictor of performance in other areas of sport, such as elite sports [9]. According to the Self-Determination Theory (SDT), two types of motivation influence personal behavior: the intrinsic (doing a task for the inherent pleasure) and the extrinsic (doing an activity for instrumental reasons, obtaining separable outcomes, or to avoid disapproval). Extrinsically motivated behaviors are expressed in four regulations: external (influenced by external contingencies), introjected (performing to obtain social approval or avoiding internal pressure), identified (recognition and acceptance of the behavior) and integrated (accepting and integrating behavior in other aspects of the self) [10]. The great majority of studies look at the connections between exercise habits and behavioral regulation, and while some have consolidated autonomous and controlled forms of regulation into summary scales or embraced the Relative Autonomy Index (RAI), while the majority have integrated some or all of the particular regulations listed within [8]. RAI represents the self-determination continuum, where lower scores indicate less autonomous motivation and higher scores indicate more autonomous motivation [11]. Intense efforts have been considered inappropriate and not motivating for the general/sedentary population due to feelings of incompetence [12,13]. Notwithstanding the limited number of people willing to engage in moderate-to-vigorous physical exercise (MVPE) and the high attrition of those who participate [14], the evidence shows a high effectiveness of intense exercise to reduce mortality, even considering a long lifespan [15]. According to Bond et al. [ 16], time spent in high-intensity activities is the most important factor in promoting vascular health and autonomic cardiac modulation. Clearer definitions of the nature of the exercise behaviors under analysis (type, intensity, volume, density), which may differ between studies, as well as their potential interest to the individual, may offer more information on this topic. The growing evidence of the usefulness of SDT-based interventions for promoting the adoption and maintenance of exercise is a significant advance, but few studies include biological markers of successful exercise-related outcomes [8], such as volitional intensity. Adjusting exercise intensity using heart rate (HR) has been a valid option, mainly in prolonged and submaximal periods. HR has become one of the most used outcomes to assess intensity, and several authors suggest that high-intensity exercise corresponds to a value equal to or higher than $90\%$ HRmax [17,18,19]. The present study examined the relationship between physical fitness, motivation and volitional high-intensity exercise in adolescents. This study aimed to assess whether physical fitness had an indirect effect on exercise intensity (through motivation). To the best of our knowledge, no studies formally tested the mediating role of motivation in exercise intensity and physical fitness relationships in PEC, so we hypothesized that motivation (in terms of RAI) exerts a mediating effect on the relationships between cardiorespiratory (CRF) and muscular fitness and exercise intensity in this population. ## 2.1. Trial Design Based on the need and importance of increasing the knowledge about the relationship between exercise intensity and motivation, we aimed to analyze the level of exercise intensity, motivation and fitness in a sample of a Portuguese adolescent population. Therefore, the main purpose of this study was to analyze the indirect (motivation-mediated) effects of physical fitness on adolescents from the 10th to 12th grades on volitional exercise intensity. More precisely, the study sought to learn how these variables are associated, allowing us to identify possible indicators to guide national strategies in promoting healthy lifestyles in young people. Exploring the mechanisms or processes that mediate the effects of fitness on exercise intensity is crucial for developing effective interventions in PEC. ## 2.2. Participants Data were retrieved from the baseline assessment and intervention group of a randomized controlled trial investigating the effects of High-Intensity Interval Training (HIIT) in High-School PEC. This project was registered on ClinicalTrials.gov (ID: NCT04022642) and approved by the Ethics Committee of the University of Évora (doc. 19017). In all aspects, this trial was conducted according to the Declaration of Helsinki on Human Research. Two public schools in the city of Beja (Portugal) were invited to participate. Written consent was obtained from the school principal and parents before the start of the investigation. After an invitation, the researchers met with the school principal and provided information on the project. After accepting to participate, 108 adolescents from the 10th to 12th grades (50 boys and 58 girls, mean age 16.0 ± 0.9 years) and their parents were informed of a detailed description of the scientific background, objectives, and safety. Students were ineligible if they did not provide parental consent to participate, had physical limitations or revealed intellectual disabilities. ## 2.3. Interventions Throughout the 16 weeks, students took part in the regular 90 min PEC twice a week, conducted by the schools’ PEC teachers following the regular curriculum. Students replaced the warm-ups established in the PEC curriculum with the proposed HIIT training sessions. After the HIIT sessions, students completed the planned PEC. The HIIT sessions were applied in the first 10–15 min of each PEC, including a brief warm-up, ranged from 14 to 20 all-out bouts intervals adopting a 2:1 work-to-rest ratio and involving a combination of aerobic and body weight muscle-strengthening exercises designed to be fun, engaging, as well as vigorous in nature. Sessions were designed progressively from 4 min in week zero to 10 min in week three using the Tabata protocol (20 s intense work, followed by 10 s rest). From week four to week seven, the same volume of exercises was applied but using 30 s intense work, followed by 15 s rest. From weeks 9 to 15, sessions were completed in pairs (Figure 1). A cut-point of ≥$90\%$ of maximal HR was a criterion for satisfactory compliance to high-intensity exercise. HR has become one of the most used outcomes to assess the intensity, and several authors suggest that each interval corresponds to a value equal to or higher than $90\%$HRmax [17,18,19]. During the supervised intervention, the researchers recorded HR using the Heart Zones Move™ software application, which uses a forearm wearable plethysmography heart rate sensor (Scosche Industries, Oxnard, CA, USA) to ensure compliance with the exercise stimulus at the predetermined target HR zone. In addition, rating perceived exertion (RPE) was also measured in each exercise session to estimate effort, fatigue and training load, targeting >17 on the 6–20 Borg scale [20,21]. ## 2.4. Outcomes Motivation and physical fitness (CRF, upper-body strength) were assessed by the Principal Investigator at the schools participating in the study. Participants’ body composition and body mass assessments were conducted sensitively through the presence of a same-sex research staff when possible. The Principal Investigator provided a brief verbal description and demonstration of each fitness test before evaluation. ## 2.4.1. Motivation Motivation was assessed with the Behavioral Regulation in Exercise Questionnaire 3 (BREQ-3) [22]. BREQ-3 is a valid and reliable measurement instrument to measure behavior regulation underlying the self-determination theory in the exercise domain and consists of 18 items with a five-point Likert scale, which varies between 1 (“not true for me”) and 5 (“very true for me”). The scores from each BREQ subscale (amotivation, external, introjected, identified and intrinsic motivation) were weighted and subsequently aggregated to form a solitary numerical index, the RAI, representing the self-determination continuum where lower scores indicate less autonomous motivation, whereas higher scores indicate more autonomous motivation: (amotivation multiplied by −3) + (external regulation multiplied by −2) + (introjected regulation multiplied by −1) + (identified regulation multiplied by 2) + (intrinsic regulation multiplied by 3) [11]. ## 2.4.2. Physical Fitness CRF was assessed using the Yo-Yo Intermittent Endurance Test level one. This test has been previously confirmed as valid and reliable to assess aerobic fitness and intermittent high-intensity endurance in 9- to 16-year-old children [23]. This test consists of incremental shuttle running starting from the speed of 8 km·h−1 until exhaustion. The maximum running speed is 14.5 km·h−1. Each shuttle run consists of 2 × 20 m interspersed by 10 s of active recovery (slow jog or walk) for a short 2.5 m shuttle. Within each speed stage, there are several shuttle runs. Running speed is prescribed by a pre-recorded audio track. Participants must reach the 20 m line by the time each audio is heard. The test is finished if the participant is unable to maintain the required speed for the second time during the bout of shuttle running. HR was monitored by telemetric HR during testing. The peak HR recorded during the test was assumed to be representative of maximal HR [24]. Upper-body strength was assessed using the push-up test (FITescola®; [25]). The test starts with the participant’s hands and feet touching the floor, and the body in a plank position, with feet apart and the hands positioned below the shoulder line. The participants should lower the body until forming a 90° angle between the arm and the forearm and then return to the starting position. This action was repeated with a previously defined cadence of 20 push-ups per minute. ## 2.4.3. Body Composition Participants’ body composition and body mass were measured to the nearest 0.1 kg in light sportswear using bioelectrical impedance analysis (Tanita MC-780, Tokyo, Japan), and height was measured to the nearest 1 mm using a portable stadiometer (Seca 213 Portable Height Measuring Rod Stadiometer, Hamburg, Germany). Body composition measurements were performed through bioelectrical body impedance analysis (BIA). Measurements were performed without accessories that contain metal (earrings, belts, coins), and female adolescents should not have a menstrual period. To ensure normal hydration status for BIA testing, participants were asked to adhere to the following pretest requirements: no vigorous exercise within 12 h of the test and no caffeine or alcohol consumption within 12 h of the test [26]. Both weight and height were measured twice to reduce the risk of measurement error. BMI was calculated using the standard formula (weight [kg]/height [m2]). ## 2.5. Sample Size Power calculations were tested prior to further statistical analysis and based on empirical estimates of sample sizes to detect the Mediated Effect needed for 0.8 Power [27]. For the medium effects (0.39) of both paths a and b, 78 students were required to detect the mediation effect. R2 effect-size measures are presented to assess variance accounted for in mediation models. ## 2.6. Data Analysis All statistical analyses were performed with the Statistical Package for the Social Sciences v.24 (IBM Corp., Armonk, NY, USA). Descriptive statistics were used to characterize the subjects and exercise test results. All variables were assessed for normality using the Kolmogorov–Smirnov test. A bivariate analysis, using the parametric Pearson correlation coefficient or the nonparametric Spearman correlation coefficient (rs), was used to indicate the strength of the association between variables. Interpretation of correlation coefficients was as follows: r ≤ 0.49 weak relationship; 0.50 ≤ r ≤ 0.74 moderate relationship; and r ≥ 0.75 strong relationship [28]. All p-values were two-tailed, and values below 0.05 were considered to indicate statistical significance. To examine whether motivation mediated the relationship of physical fitness with exercise intensity, separate models were created for each outcome with physical fitness as a predictor and motivation as a mediator. Figure 2 illustrates the overall mediation models used in the analysis. The diagram on the left side shows the total effect (path c), which represents the effect of predictor X on outcome Y without considering mediation. In addition, the total effect of X on Y is equal to the sum of the direct and indirect effects of X. This path quantifies how much two cases that differ by a unit on X are estimated to differ on Y. The diagram on the right side shows a simple mediation model that represents the effect of predictor X on outcome Y including mediation (M). In this model, there are two pathways by which (X) can influence (Y), the indirect effect (path a.b) and the direct effect (path c’). The indirect effect is the product of path a (the effect of the predictor on the mediator) and path b (the effect of the mediator on outcomes partially out the effect of the predictor) and represents how Y is influenced by X through M. The indirect effect quantifies how much two cases that differ by one unit on X are estimated to differ by a.b units on Y because of the effect of X on M, which, in turn, affects Y. The direct effect represents the effect of (X) on outcome (Y) that cannot be attributed to mediator (M). So, this path quantifies how much two cases that differ by one unit on X are estimated to differ by c’ units on Y holding M. Mediation analyses were performed according to Hayes [29], who advises that mediation can occur even when the total effect (the relationship between (X) and (Y), represented by path c) is not statistically significant. This is because the total effect is estimated by a different statistical model from the indirect effect, often having a lower power than the indirect effect test. Requiring total effect also ignores the risk of opposing direct and indirect effects, which might combine to produce a nonsignificant total effect. Yet, before testing mediation, path a and path b were quantified with regression coefficients. The SPSS macro developed by Preacher and Hayes [30] (PROCESS version 4.0) was used to test mediation. This tool tests the significance of indirect effects using bootstrap confidence intervals. The bootstrap method does not require assumptions of normality of the sampling and has higher power and better Type I error control when compared with the product-of-coefficients approach, most well known as the Sobel test [29,30,31]. The indirect effects were deemed significant if 0 was not included in the bootstrap confidence intervals. We report the results of bootstrapping procedures, with the resampling of 10,000 bootstrap replicates ($95\%$ confidence intervals, $95\%$ BootCIs). Finally, the indirect effects were described by unstandardized (B) and completely standardized effects (β). ## 3. Results Data for 108 students ($53.7\%$ female) aged 16.0 ± 0.92 years that presented valid volitional intensity data were available for analysis (Table 1). Figure 3A to C show the results of the mediation analysis for each outcome according to the conceptual model (Figure 2). Total effect (path c) was significant in models A (CRF) and C (body fat) mediated through motivation. Regarding bivariate correlation, exercise intensity showed positive correlations with body fat ($r = 0.318$, $p \leq 0.01$) and a negative correlation with CRF and muscular fitness (ρ = −0.449, $p \leq 0.01$; r = −0.190, $p \leq 0.05$). The bootstrap-derived $95\%$ confidence intervals included zero for all outcomes mediated through motivation, revealing a non-significant, indirect effect of physical fitness through motivation on exercise intensity, specifically on CRF (B = −0.0355, $95\%$ BootCI [−0.5838; 0.4559]; R2 = 0.17; post hoc power = 1), muscular fitness (B = −0.7284, $95\%$ BootCI [−2.0272; 0.2219]; R2 = 0.08; post hoc power = 0.86) and body fat ($B = 0.5092$, $95\%$ BootCI [−0.4756; 1.6934]; R2 = 0.10; post hoc power = 0.93). There were direct effects (path c’) of CRF ($p \leq 0.0001$) and body fat ($p \leq 0.01$) on exercise intensity, suggesting that high values of CRF decrease volitional exercise intensity and high values of body fat increase physical exercise intensity, without mediation through motivation. In path a (the effect of physical fitness on motivation), all models reveal a significance but show no significance in path b (the effect of motivation on exercise intensity), which is confirmed by bivariate correlations: a negative correlation with body fat (ρ = −0.325, $p \leq 0.001$) and positive correlations with CRF and muscular fitness (ρ = 0.454, $p \leq 0.001$; ρ = 0.430, $p \leq 0.001$). Regarding path b, motivation reveals a negative correlation with exercise intensity (ρ = −0.182, $$p \leq 0.046$$). Direct effects (path c’) remain significant on models A (CRF) and C (body fat) mediated through motivation. Therefore, the absence of an indirect effect suggested that high or low values of RAI did not increase or decrease volitional high-intensity exercise. ## 4. Discussion The present study examined the relationship between physical fitness, motivation and volitional high-intensity exercise in adolescents. This study aimed to assess whether physical fitness had an indirect effect on exercise intensity (through motivation). The major findings of this study imply that the absence of an indirect effect suggested that high or low values of RAI did not increase or decrease volitional high-intensity exercise. Nevertheless, studies have shown that exercise behavioral regulation was found to be predictive of vigorous exercise, where introjected regulation, identified regulation and intrinsic motivation were positively associated with strenuous exercise behaviors [32]. Still, intrinsic motivation appears to be a more consistent predictor of moderate and vigorous exercise than identified regulation, and autonomous motivation was predictive of long-term moderate-to-vigorous exercise [33]. The mediation mechanism assumes that the independent variable influences the mediator, and the mediator affects the dependent variable. So, the independent variable’s total effect is divided into indirect effects through a mediator. In our work, we used this statistical method to test whether motivation was a mediator of the relationship of physical fitness with volitional exercise intensity. In this study, mediation analyses indicate that adolescents with a higher physical fitness and/or more motivation did not show better biological markers of successful exercise-related outcomes such as volitional intensity. This research adds to the existing literature by examining the indirect effect of physical fitness on volitional exercise intensity in adolescents via motivation. It is critical to define and measure the mechanisms through which physical fitness may be linked to volitional exercise intensity in order to improve recommendations and interventions. This is because investigating mediators can aid in identifying critical aspects that require more attention in order to enhance outcomes. In children and adolescents, higher amounts of sedentary behavior are associated with increased adiposity and poorer cardiometabolic health and fitness [2]. Lack of leisure time, reduced access to facilities and low motivation to engage in physical activities are frequently reported barriers to strong adherence to exercise programs [34,35]. Intense efforts have been considered inappropriate and not motivating for the general/sedentary population due to feelings of incompetence [12,13]. However, in our study, lower levels of fitness (CRF, muscular and body fat) were associated with higher volitional exercise intensity. Notwithstanding the limited number of people willing to engage in MVPE and the high attrition of those who participate [14], the evidence shows high effectiveness of intense exercise to reduce mortality, even considering a long lifespan [15]. An optimal stimulus that promotes cardiovascular and peripheral adaptations implies several minutes per session in the so-called red zone, which usually means a minimum intensity of $90\%$VO2max [36]. Interventions designed to increase MVPE in PEC indicate that interventions can increase the proportion of time students spend in higher intensities during PEC and reduce sedentary behavior, since motivational climates that emphasize effort and improvement and provide opportunities to demonstrate leadership and make decisions have a positive impact on PA [37]. Physical self-perception could be considered for adherence to MVPE, although Rey et al. [ 38] suggest that adolescents’ psychological perceptions and health might be improved in response to morphological adaptations, without concomitant improvements in objectively measured physical characteristics or performances. Despite the novelty and interest of our findings, some limitations must be addressed. First, the use of a cross-sectional design, which prevents the determination of the temporality of the effect of physical fitness and motivation on exercise intensity and the inference of causality from our hypothesized path models, is limited by the use of cross-sectional data. Second, our sample was limited to adolescents from the 10th to 12th grades from a public school in the city of Beja (Portugal). This means that our findings cannot be applied to other populations. Despite this, our selected bootstrapping method has strong statistical power and is considered a useful tool for avoiding Type I errors [29]. Third, other mediator variables may contribute to the links between physical fitness and volitional exercise since most studies opted to set intensity through external load, expressed in speed or distances. Few studies have objectively measured internal load by monitoring HR [34,35,39,40,41,42,43] or RPE [43,44], and only some defined cut lines as high intensity >$85\%$HRmax [40,42] or >$90\%$HRmax [34]. HR has become one of the most used outcomes to assess intensity. Adjusting exercise intensity using HR is a valid option, mainly in prolonged and submaximal periods. It is expected that HR reaches maximum values (>90–$95\%$ HRmax) close to the speed/power associated with VO2max, which does not always happen, especially in very short exercises (<30 s) [17,18]. This may be related to the known delay in HR response at the beginning of exercise, which is slower than the VO2 response. In conclusion, high or low values of RAI did not increase or decrease volitional high-intensity exercise, and lower levels of fitness (CRF, muscular and body fat) were associated with higher volitional exercise intensity. To the best of our knowledge, this is the first study addressing the indirect effect (through motivation) of physical fitness on volitional exercise intensity using a mediation model in a sample of older adolescents; however, future studies are needed to confirm these findings. The idea that public health gains will be higher if we help the least motivated become more motivated due to the lack of sufficient motivation to participate in moderately intense exercise or PA is being challenged. In modern society, it is unlikely that individuals will ever return to the high average PA levels of the past. The results of this study emphasize the importance of new strategies in PEC with acute vigorous-intensity activities. Time-efficient interventions have a preeminent role; moreover, exercise protocols that result in short-term physiological health improvements are of interest to physical education teachers, as well as to rehabilitation, health and exercise professionals. Practical Applications With this study, the authors aim to provide novel HIIT protocols for schools with less volume (only twice a week) and higher density (less rest in each interval) which include resistance exercises through calisthenic exercises and plyometrics. In addition to the extremely low volume (on average, 10 min/week), it should be noted that there is no need for external loads to implement these protocols; the use of all-out bouts and plyometrics are also simple approaches. The actual PEC time is still restricted due to activities and teaching breaks, as well as absences due to illness, medical appointments, and a lack of appropriate clothing, making it difficult to find content that can positively influence healthy physical fitness in students due to an objective lack of time. Replacing the traditional warm-up without interfering with other curricular content provided in PEC with this time-efficient approach could have a prominent role in improving students’ fitness. 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--- title: Identification of Circular RNA Profiles in the Liver of Diet-Induced Obese Mice and Construction of the ceRNA Network authors: - Xiaoxiao Zhang - Shuhua Gu - Shunyi Shen - Tao Luo - Haiyi Zhao - Sijia Liu - Jingjie Feng - Maosheng Yang - Laqi Yi - Zhaohan Fan - Yu Liu - Rui Han journal: Genes year: 2023 pmcid: PMC10048691 doi: 10.3390/genes14030688 license: CC BY 4.0 --- # Identification of Circular RNA Profiles in the Liver of Diet-Induced Obese Mice and Construction of the ceRNA Network ## Abstract Obesity is a major risk factor for cardiovascular, cerebrovascular, metabolic, and respiratory diseases, and it has become an important social health problem affecting the health of the population. Obesity is affected by both genetic and environmental factors. In this study, we constructed a diet-induced obese C57BL/6J mouse model and performed deep RNA sequencing (RNA-seq) on liner-depleted RNA extracted from the liver tissues of the mice to explore the underlying mechanisms of obesity. A total of 7469 circular RNAs (circRNAs) were detected, and 21 were differentially expressed (DE) in the high-fat diet (HFD) and low-fat diet (LFD) groups. We then constructed a comprehensive circRNA-associated competing endogenous RNA (ceRNA) network. Bioinformatic analysis indicated that DE circRNAs associated with lipid metabolic-related pathways may act as miRNA sponges to modulate target gene expression. CircRNA1709 and circRNA4842 may serve as new candidates to regulate the expression of PTEN. This study provides systematic circRNA-associated ceRNA profiling in HFD mouse liver, and the results can aid early diagnosis and the selection of treatment targets for obesity in the future. ## 1. Introduction Obesity refers to excessive accumulation or abnormal distribution of body fat and weight gain. It is a chronic metabolic disease affected by the interaction of environmental and genetic factors and has become an epidemic in the modern world [1]. More and more evidence shows that obesity has become a risk factor for cardiovascular and cerebrovascular diseases and metabolic diseases. For example, the prevalence of obesity is closely related to the incidence and severity of nonalcoholic fatty liver disease NAFLD [2]. The younger age of obesity has led to an increased incidence of hepatic steatosis and its associated comorbidities in pediatric patients, with an alarming global prevalence [3]. The correlation between obesity and multiple diseases suggests that intervention in obesity is an important and feasible way to prevent complex and frequent morbidity. It is important to investigate the mechanisms and potential biomarkers of obesity to prevent and treat obesity and obesity-related lipid metabolic diseases. In recent years, research on the function of non-coding transcripts in regulating obesity has developed rapidly. Many non-coding transcripts, such as miRNAs and long non-coding RNA (lncRNAs), participate in the regulation of metabolic pathways leading to obesity [4,5]. Previous research found that different lncRNAs are differentially expressed in obese human or animal subjects, and the mechanism has been explored persistently [6]. Notably, a new type of non-coding RNA, circular RNA (circRNA), a covalently closed loop structure without 5′-3′ polarity, has focused on active research in diversity processes. CircRNA is resistant to exonuclease and is more stable [7,8]. CircRNA participates in various types of diseases, such as cancers [9], neurological diseases [10], and cardiovascular diseases [11], using various mechanisms, including acting as miRNA sponges to regulate target gene expression [12], or even translating polypeptides [13]. In mammals, a few circRNAs play an important role in obesity. For instance, CircTshz2-1 and circArhgap5-2 have been reported to be vital regulators of adipogenesis in human adipose tissue [14]. CircSAMD4A is significantly upregulated in obese patients; it regulates preadipocyte differentiation by acting as an miR-138-5p sponge [15]. Similarly, Chen et al. identified 231 DE circRNAs in mice, and functional enrichment analysis revealed that circRNA_0000660 is involved in the lipid metabolism pathway [16]. In general, these data robustly indicate that circRNAs play an important role in the biological process associated with obesity. Nevertheless, the expression profile and biological functions of circRNAs in the lipid metabolism of obese mice remain elusive. In this study, we preliminarily identified the circRNA profile in the liver of diet-induced obese mice and the control group by RNA sequencing (RNA-seq). The function of DE circRNA and miRNA target sites was predicted by bioinformatic methods. This may help us to explore the molecular mechanism of lipid metabolism and provide new therapeutic targets for obesity. ## 2.1. Experimental Animals Four-week-old C57BL/6J mice (male, $$n = 20$$, No.110324210102152042, Beijing SBF Biotechnology Co., Ltd., Beijing, China) were randomly divided into two groups after being adaptively fed for 1 week. The high-fat diet (HFD) group ($$n = 10$$) was fed a high-fat diet ($60\%$ fat; D12492, Beijing SBF Biotechnology Co., Ltd., Beijing, China) and the low-fat diet (LFD) group ($$n = 10$$) was fed a low-fat control diet ($10\%$ fat; D12450B, Beijing SBF Biotechnology Co., Ltd., Beijing, China) for 7 weeks. During the entire experiment, the mice were maintained on a 12-h light:dark cycle in and environment controlled for temperature and humidity, with free access to food and water. The food consumption of the mice was measured daily, and body weight was measured weekly. At 12 weeks of age, 5 mice from each group were randomly selected and fasted for 12 h. After that, the mice were anesthetized by exposure to a lethal dose of CO2, and blood samples were collected via eyeball enucleation. The liver and adipose tissues (epididymis, inguinal, and subcutaneous) were collected immediately after sacrifice for further analysis. All animal experiments were approved by the Animal Ethics Committee of Hebei North University. ## 2.2. Biochemical and Histological Analysis Serum was collected by centrifugation of blood samples and used for biochemical analysis ($$n = 5$$/group). Fasting blood glucose (FBG) (cat. no. 100000240), triglyceride (TG) (cat. no. 100000220), and total cholesterol (TC) (cat. no. 192061) concentrations were measured by the respective test kits, which were provided by Biosino Bio-technology and Science Inc. (Beijing, China). The concentration of high-density lipoprotein cholesterol (HDL-C) was measured using mouse enzyme-linked immunosorbent assay lipoprotein (cat. no. CD20316). The enzyme alanine aminotransferase (ALT) was measured by the Reitman-Frankel method, and corresponding standard curves were used to calculate the concentration. All procedures were performed according to the manufacturer’s instructions. The glucose tolerance test (GTT) and insulin tolerance test (ITT) were performed on mice fasted for 6 h, followed by intraperitoneally injecting glucose solution (2 g/kg) or insulin solution (0.5 U/kg). Blood glucose concentrations were measured from the tail vein using a glucometer at different timelines. The liver and adipose tissue were fixed in $4\%$ paraformaldehyde for 24 h, dehydrated, embedded in paraffin, and sectioned. The sections were stained with hematoxylin and eosin (HE) and observed under a light microscope. ## 2.3. Identification of circRNAs in the Liver The total RNA in the liver tissue ($$n = 3$$/group) was isolated and purified using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Approximately 5 μg of total RNA was used to deplete ribosomal RNA, followed by treatment with RNase R (Epicenter Inc, Madison, WI, USA) to remove linear RNAs. The enriched circRNAs were used to construct fr-firstrand sequencing libraries [17], followed by paired-end sequencing on an Illumina Novaseq™ 6000 (LC Bio, Hangzhou, China). To identify circRNAs, Cutadapt [18] was first used to remove reads that contained adaptor contamination, low-quality bases, and undetermined bases. Bowtie2 [19] and Tophat2 [20] were used to map reads to the genome of species, and the remaining unmapped reads were mapped to the genome using Tophat-Fusion [21]. CIRCExplorer2 [22,23] and CIRI [24] were used to de novo assemble circRNA from the mapped reads. Then, back-splicing reads were identified in unmapped reads using Tophat-Fusion. CircRNA expressions from different groups were calculated by scripts, and we normalized the back-spliced reads using spliced reads per billion mapping. The data were uploaded to the Gene Expression Omnibus (GSE214134). ## 2.4. Bioinformatic Analysis and Construction of the ceRNA Network Transcripts with $p \leq 0.05$ and \|log2(FoldChange)\| > 1 were regarded as showing differential expression by the edgeR package [25]. Functional enrichment analysis was performed on Sangerbox [26]. GO categories and KEGG pathways with p values < 0.05 showed significant differences in expressed circRNAs, targeting relationships between genes predicted using TargetScan, miRanda, and miRWalk. The ceRNA network was constructed using Cytoscape 3.8.2. ## 2.5. Validation of RNA-Seq Results Real-time quantitative polymerase chain reaction (RT-qPCR) and Sanger sequencing were performed for circRNA validation. The primer sequences were designed using Primer 5 and are shown in Supplementary Table S1. The relative quantitative value was evaluated using the 2−ΔΔCt method. ## 2.6. Statistical Analysis Statistical analysis was performed in SPSS using the Student’s t-test. Data were expressed as mean ± SD. $p \leq 0.05$ was considered statistically significant. ## 3.1. Effects of a High-Fat Diet on Mice Compared with the LFD group, the average body weight of mice in the HFD group significantly increased at 6 weeks of age, and body weight was >$20\%$ greater than that in the LFD group at 12 weeks old ($p \leq 0.01$) (Figure 1b). Daily food intake was not significantly different between the two groups ($p \leq 0.05$); however, the energy intake in the HFD group was significantly greater than that in the LFD group ($p \leq 0.01$) (Figure 1c). FBG, TC, and ALT levels in the HFD group significantly increased ($p \leq 0.05$), whereas the TG level in serum was not significantly elevated ($p \leq 0.05$) and the HDL-C level decreased ($p \leq 0.05$) (Figure 1d). In the GTT, the peak time of blood glucose was delayed in the HFD group, and the blood glucose levels failed to return to normal 120 min after injection, significantly increasing the area under the curve (AUC) compared with the LFD group (Figure 1e). The ITT revealed that blood glucose levels in the HFD group had a lower and slower decline, with significantly higher AUC than that in the LFD group (Figure 1f). In addition, mice in the HFD group had a pale-yellow enlarged liver as well as hepatic fat deposition and inflammatory cell infiltration; quantification of percentage of hepatic steatosis was 5~$33\%$ (score 1) using the NAFLD activity score (NAS) (Figure 1g). HE staining revealed that adipocytes in the HFD group were significantly larger than those in the LFD group (Figure 1h). ## 3.2. Overview of RNA-Seq A total of 314,264,818 raw reads were generated. After discarding the reads with adapters and undetermined and low-quality reads, 232,599,364 valid reads were obtained. Through quality control, the error rates of HFD and LFD were generally less than $0.5\%$, and statistics on the GC content reads showed that the average values of HFD and LFD were $60\%$ and $61.2\%$, respectively (Table 1). The valid reads were mapped to the mouse reference genome, and the unmapped reads were subsequently selected. A total of 7469 circRNAs (5342 circRNAs were novel) were detected by CIRCExplorer2 and CIRI and used for the subsequent analysis. Based on the junction positions, the 7469 circRNAs were divided into exon circRNA ($86\%$), intron circRNA ($13\%$), and intergenic circRNA ($1\%$) (Figure 2a). Approximately $7.8\%$ of circRNAs came from chr2 and $40.33\%$ of circRNA transcripts were 200–400 base pairs (bp) in length (Figure 2b,c). In addition, the number of exons in circRNA transcripts mainly ranged from 1 to 4 (Figure 2d). ## 3.3. Differentially Expressed circRNAs Transcripts with $p \leq 0.05$ and \|log2(FoldChange)\| ≥ 1 were regarded as DE. There were 21 significantly DE circRNAs between the two groups ($p \leq 0.05$), with 13 significantly upregulated and 8 significantly downregulated (Figure 3a and Supplementary Table S2). The heatmap shows the DE circRNA expression pattern between the two groups (Figure 3b). GO and KEGG analyses was used to analyze the biological functions of DE circRNAs. A total of 200 GO terms were significantly enriched ($p \leq 0.05$). GO annotations revealed that these circRNAs were significantly enriched in long-chain fatty acyl-CoA binding (GO:0036042), phosphatidylcholine transfer activity (GO:0120019), lipid hydroperoxide transport (GO:1901373), and triglyceride acyl-chain remodeling (GO:0036153) (Figure 3c). These DE circRNAs were significantly enriched in six signaling pathways ($p \leq 0.05$). Interestingly, some related signaling pathways involved lipid metabolism, such as inositol phosphate metabolism (KO00562), the phosphatidylinositol signaling system (KO04070), insulin resistance (KO04931), the sphingolipid signaling pathway (KO04071), and primary bile acid biosynthesis (KO00120) (Figure 3d). To verify the reliability of circRNA expression data, 5 circRNAs were randomly selected based on the back-splicing junction read count and evaluated by RT-qPCR. This was consistent with the trend of RNA-seq results (Figure 3e). ## 3.4. Construction of the ceRNA Network CircRNAs can act as ceRNAs and thus influence the expression levels of their target genes. In this study, based on the DE circRNA sequence detected by RNA-seq, Targetscan and miRanda were used to predict the target relationship between circRNAs and miRNAs. The miRNAs were then used to predict the potential target mRNAs using miRWalk. As a result, 1782 mRNAs containing miRNA-recognizing sites (MRE) were detected. These target mRNAs were significantly enriched in 432 GO terms and 112 KEGG pathways. The top five enriched GO terms and KEGG pathways are shown in Figure 4a. Genes enriched in these processes were selected for ceRNA network construction, which included 149 circRNA–miRNA and 2195 miRNA–mRNA interaction pairs as well as 1538 nodes (21 DE circRNAs, 97 correlated miRNAs, and 1420 mRNAs) and 2344 edges (Figure 4b). Notably, some well-known lipid metabolism–related processes, such as the mitogen-activated protein kinase (MAPK) signaling pathway (KEGG:mmu04010) and PI3K/AKT signaling pathway (KEGG:mmu04151), were detected. The detailed ceRNA network of these two pathways was then constructed. In the MAPK signaling pathway ceRNA network, 14 DE circRNAs, 26 miRNAs, and 61 genes were enriched, with circRNA1709 containing 37 miRNA binding sites, which can be regulated by mmu-miR-203, mmu-miR-193, and other miRNAs, including AKT3, MAPK9, FGF9, and IGF1R and other genes involved in lipid metabolism regulation (Figure 4c). In the PI3K/AKT signaling pathway ceRNA network, 13 DE circRNAs, 29 miRNAs, and 64 genes were enriched; it is worth noting that circRNA1709 occupies a central regulatory role in this pathway (Figure 4d). We performed a protein–protein interaction (PPI) network analysis of the potential target gene of circRNA1709, and the results are shown in Figure 5. The hub gene in the PPI network was further analyzed using cytoHubba, and 10 hub genes, including KRAS, PTEN, CREB1, MAPK8, MAPK9, MAPK14, GSK3B, MET, AKT3, and CDKN1B, were identified. It is worth noting that another circRNA molecule identified in this study, circRNA4842, whose parental gene is PTEN, is cyclized by the third, fourth, and fifth exons of the PTEN gene (Figure 6a). Our quantitative analysis of PTEN gene expression in mouse liver tissue revealed that PTEN gene expression in the liver of mice fed an HFD was downregulated ($p \leq 0.05$) (Figure 6b). Further research on the relationship between PTEN, circRNA1709, and circRNA4842 is needed. ## 4. Discussion Obesity refers to a certain degree of excessive weight and fat layer caused by excessive accumulation of body fat, especially triglycerides [27]. Increasing evidence suggests that obesity is associated with liver disease, insulin resistance, and multiple metabolic syndromes; therefore, a deeper understanding of the underlying mechanism of obesity is necessary for early diagnosis and new effective treatments [28]. In this study, we established an obese mouse model by feeding mice with an HFD. The body weight, FBG, TC, and ALT in the serum of mice in the HFD group significantly increased compared with that in the LFD group. The HFD group showed significant abnormal glucose tolerance and insulin resistance, accompanied by hepatic steatosis and inflammatory cell infiltration in mice in the HFD group. The aforementioned results show that the obese mouse model in this study is successfully constructed and can be used for subsequent studies. Food-induced models of animal obesity are similar to human obesity and are often used to study the relationship between diet, genes, and other factors and obesity diseases. A mouse obesity model constructed by high-calorie-diet feeding can be used as a suitable animal model for preclinical pharmacodynamic studies of obesity and related complications [29,30]. Obesity is affected by a combination of genetic factors and environmental factors. In previous studies, some key genes involved in lipid metabolism, such as PPARγ, CEBPα, and FABP4, were identified [31,32]. Gene expression can be regulated by different molecules, such as lncRNAs and miRNAs, at different levels, such as post-transcription and post-translation [33,34]. As a newly described class of RNAs, circRNAs have gained much attention based on their regulatory role in different biological processes [35,36]. In this study, we performed deep RNA-seq on liner-depleted RNA extracted from the liver tissues of mice. A total of 7469 circRNAs were detected by our RNA-seq analysis, and compared with the public database, 5342 novel isoforms were identified. Our results show that the circRNA molecules expressed in mouse liver tissue are mainly exon cyclic, which is similar to the results of circRNA studies in other species. In addition, the chromosomal distribution, length, and exon content of cyclic RNA molecules in mouse livers can provide a reference for further study of circRNA. It is noteworthy that we found internal ribosomal entry sites (IRES) in some circRNAs, suggesting that these IRES-containing circRNAs may have the potential to code for peptides. Related studies have also confirmed the existence of circRNAs with coding ability, and follow-up research can focus in this direction [37,38]. In this study, the differences in circRNA expression profiles of liver tissues in the HFD and LFD groups were further compared, and a total of 21 DE circRNAs were found, of which 13 were upregulated in the HFD group and 8 were downregulated. These DE circRNAs may play an important regulatory role in diet-induced obesity. Prediction of the function of these DE circRNAs can be made with reference to their parental genes [39]. Previous studies have shown that an important role of circRNAs is to regulate the expression of their linear counterparts, which can be positive or negative [40]. For example, CircFECR1 can reduce the promoter methylation of its parental gene and improve its expression through epigenetic mechanisms [41]. In this study, the functional enrichment analysis of DE circRNAs was performed with reference to their parental genes. DE circRNAs are enriched in multiple pathways associated with lipid metabolism, such as long-chain fatty acyl-CoA binding, insulin resistance, and the phosphatidylinositol signaling pathway. Our study provides a reference for the subsequent development of these circRNAs to regulate the expression of their parental genes and affect the process of lipid metabolism. The network of DE circRNAs, parental genes, and target miRNAs is shown in the Supplementary Materials Figure S1. For the function prediction of circRNAs, it is incomplete to mention only the relationship between circRNAs and their parental gene. In this study, based on the sequence structure of DE circRNAs, we predicted the miRNA recognition site, accompanied by prediction of the miRNA target gene, and successfully constructed the ceRNA network. The theory of the ceRNA regulatory network states that RNA molecules with the same MRE can competitively bind miRNAs, thereby affecting the expression of target molecules and regulating different biological processes [42]. In this study, 21 DE circRNAs interacted with 97 miRNAs and thus have potential interaction with 1420 genes. The result of functional enrichment analysis showed that some well-known lipid metabolism-related processes, such as the MAPK signal pathway and PI3K/AKT signaling pathway, were detected. MAPK are key mediators of signal transduction in mammalian cells [43]. The MAPK signaling pathway plays a key role in activating downstream transcription factors through kinase cascades, mediating gene expression and initiating cellular events, such as appetite, lipogenesis, glucose homeostasis, and thermogenesis [44,45]. Abnormal activation of PI3K/AKT signaling pathways promotes the development of obesity [46]. PI3K and AKT in the signaling pathway can be involved in glycogen synthesis, glucose uptake, and lipogenesis when activated by upstream signals such as hormones and growth factors [47]. In addition, the PI3K/AKT pathway is indispensable in the insulin signaling pathway and is associated with obesity and the severity of insulin resistance [48]. In this study, circRNA1709 occupies an important position in the ceRNA regulatory network we constructed, and its target gene plays a key regulatory role in the MAPK and PI3K/AKT signaling pathways. We analyzed the hub target gene of circRNA1709 using the PPI network and identified 10 hub genes, including KRAS, PTEN, CREB1, MAPK8, MAPK9, MAPK14, GSK3B, MET, AKT3, and CDKN1B. Among these genes, the PTEN gene is the key gene involved in the regulation of lipid metabolism. The PTEN gene is a mutable tumor suppressor gene, and the PTEN protein has the dual enzymatic activity of protein phosphatase and lipid phosphatase and is involved in regulating body growth and metabolism through complex signal transduction pathways [49]. PTEN can block the PI3K/AKT signaling pathway; activate FoxO1 or inactivate mTORC; regulate SREBP and MAF1; affect the expression of enzymes, such as Fasn and Acc; and inhibit lipid production [50]. In addition, multiple studies have found that the PTEN gene is associated with insulin resistance [51]. PTEN mutants appear in various primary tumor tissues, whereas overexpression of wild PTEN is found in chronic insulin-resistant diseases. Function-loss experiments have proved that PTEN participates in the regulation of circulating glucagon levels and insulin resistance in HFD-fed mice [52]. Obesity is the most important risk factor for insulin resistance; however, the specific pathogenesis needs to be further studied. The obese mouse model constructed in this study showed abnormal glucose tolerance and insulin resistance. The circRNA1709 screened in this study may be involved in the development of insulin resistance by regulating PTEN expression, and subsequent functional studies are necessary. It is also noteworthy that this study found two DE circRNAs that have a potential relationship with PTEN, including circRNA1709 (which might target the PTEN gene) and circRNA4842 (PTEN might be the parental gene). These two circRNAs can be used as molecular targets to explore the molecular mechanism of lipid metabolism and provide a new therapeutic target for lipid metabolic diseases. In addition, these two circRNAs exhibit different expression patterns in the liver of HFD-fed mice, which suggests that PTEN gene regulation and the development of insulin resistance may be achieved by different circRNAs through interaction. Biological mechanisms should be systemically studied, as the results obtained by discussing the role of individual circRNAs alone are not credible. ## 5. Conclusions In this study, we successfully constructed a diet-induced obese mouse model and screened the circRNA expression profiles in liver tissues. A total of 7469 circRNAs expressions were detected, of which 21 were DE in the HFD and LFD groups. DE circRNAs may be involved in lipid metabolic-related pathways and act as miRNA sponges to modulate gene expression. CircRNA1709 and circRNA4842 may serve as new candidates to regulate PTEN expression and thus be involved in the regulation of lipid metabolism and insulin resistance. 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--- title: Composition of Powdered Freeze-Dried Orange Juice Co-Product as Related to Glucose Absorption In Vitro authors: - María del Mar Camacho - Juan José Martínez-Lahuerta - Isabel Ustero - Eva García-Martínez - Nuria Martínez-Navarrete journal: Foods year: 2023 pmcid: PMC10048701 doi: 10.3390/foods12061127 license: CC BY 4.0 --- # Composition of Powdered Freeze-Dried Orange Juice Co-Product as Related to Glucose Absorption In Vitro ## Abstract The reuse of food by-products is crucial for the well-being of the planet. Considering the high content of nutrients and other bioactive compounds in many of them, investigating their suitability for use as human food ingredients is an interesting challenge. In this study, in addition to the proximate composition, phenol content and antioxidant activity (AOA = 3.2 mmol Trolox equivalent (TE)/100 g, db) of orange juice powder by-product (CoP), different in vitro properties related to carbohydrate metabolism have been characterised. Specifically, the glycaemic index (GI), the glycaemic load (GL), the glucose dialysis retardation index (GDRI = $13.6\%$), the glucose adsorption capacity (GAC = 22.5 mM) and the inhibition capacity of α-amylase (α-$A = 46.9$%) and α-glucosidase (α-$G = 93.3$%) of powdered orange juice waste have been determined and related to fibre and phenolics composition. Taking advantage of the high fibre content of the by-product ($36.67\%$), its GL was calculated for a CoP dose that allows labelling the food to which it is added as a source of fibre. The low GI value ($24.4\%$) and the low GL (0.918 g available carbohydrates per serving) allowed us to conclude that the product studied could be an interesting opportunity for the food industry to offer it as a healthy food ingredient to be included in the diet, especially for those suffering from type 2 diabetes mellitus. Of the total phenolic compounds (TP = 509 mg equivalent of gallic acid (GAE)/100 g, db), $68\%$ were found in free fraction (FP), and their contribution to the total AOA was $40.6\%$, while this was $54.9\%$ for the $32\%$ of phenols bound to plant tissues (BP). ## 1. Introduction One of the current interests directly affecting the agri-food industry is the use of the waste generated, following the circular economy model. Many food by-products are of high nutritional and potentially functional value [1,2]. Trying to combine both aspects, the processing of these by-products into food or ingredients for human consumption seems to be a highly interesting task. Focusing on the citrus sector, the juice-processing industry processes annually slightly more than $27\%$ of the world’s orange production, which represents a number of about 28 million tons [3]. Considering that approximately $50\%$ of the total weight of the used fruit becomes waste, it is interesting to find a way to take advantage of the bagasse, especially based on its high content of bioactive compounds, including dietary fibre and phenolics [4]. The presence of fibre in the orange peel is an important aspect to be considered since, in many countries, consumers’ fibre intake is insufficient and falls short of the 25 g/day recommendation of the expert committee of the Food and Agriculture Organisation of the United Nations (FAO) and the World Health Organisation (WHO) [5]. Many properties are attributed to fibre: regulating overweight and obesity, lowering the glycaemic index, preventing colon and rectal cancer, preventing cardiovascular diseases and combating constipation, among others [6]. Fibre mechanisms of action are based on the increase in chewing time and the sensation of satiety, delaying gastric emptying [7] and its ability to reduce serum glucose levels [8]. On the other hand, the current lifestyle of western society is rapidly and progressively deteriorating its eating habits by using highly processed products for its daily diet. As a consequence, there is an increase in certain metabolic pathologies that have, as one of their aetiological mechanisms or necessary collaborators, the ingestion of diets rich in carbohydrates and lipids. We are referring mainly to type 2 diabetes mellitus (T2DM) and obesity. In Spain, from 1987 to 2012, the age-adjusted prevalence of obesity increased from 8.0 to $16.5\%$ and that of T2DM from 4.2 to $7.1\%$, particularly in men [9]. The International Diabetes Federation has released new figures showing that 537 million adults have diabetes worldwide, an increase of $16\%$ (74 million) from previous estimates made by this Federation in 2019 [10]. The most recent data provided by the WHO indicate that in Europe, in 2022, overweight and obesity affected almost $60\%$ of adults and one in three children, and T2DM affected 60 million people (approximately $9\%$ of the adult population) [11]. Furthermore, and most worryingly, there is a shift in the incidence of cardiovascular pathologies associated with these diseases to earlier stages of life, many of them with fatal outcomes. In this sense, the design of food or food ingredients that help in the control of these pathologies related to postprandial hyper-glycaemic could promote the personalisation of diets for these population groups. Two concepts are very important when it comes to choosing the right carbohydrate intake for our daily lives: GI and GL. The GI measures the ability of foods to raise glucose levels after ingestion compared to a reference food. In this sense, the GI is defined as the increase in the area under the glycaemic response curve resulting from the ingestion of 50 g of carbohydrates (CHO) of the tested food, expressed as a percentage of the response of the same amount of CHO from a standard food (glucose or white bread), taken by the same subject [12]. The value obtained for the reference food is 100, and that of the tested food is expressed as a percentage of this reference. In this way, foods with a high GI will cause rapid and significant upward fluctuations in blood sugar, while those with a low GI lead to smaller increases. In the daily diet, foods with a low GI (rice, potatoes, pasta, pulses, etc.) are the foods of choice, as the insulin requirement for their metabolism is lower. For the purposes of the GI, it is important to note that it depends not only on the composition of the food (type of starches, fibre content, type of CHO, fat content and acidity) but also on the techniques used for processing and cooking [13]. Despite the fact that GI values are useful to predict the glycaemic response of foods and to avoid unwanted postprandial hyperglycaemia, another concept that provides a more practical tool for understanding how much the ingestion of food will raise a person’s blood glucose level is the GL. It is calculated by multiplying the grams of total available carbohydrate (CHOa) per serving of food consumed by the food’s GI and dividing by 100 [14,15]. Thus, GL becomes a useful tool for helping people to account for both the quantity and the quality of carbohydrates present in a dose of an ingested food [15]. In a simplified way, we could say that while the GI refers to the rate with which a type of carbohydrate is absorbed and passes into the blood, the GL refers to the intensity of the insulin response that the food we have eaten will provoke. Both GI and GL are not only two very important indicators to consider for the population in general but especially for those suffering from diabetes or obesity. Knowledge of them enables us to choose the right carbohydrate intake for our daily lives and to assess the quantity of food that is likely to be suitable for maintaining an adequate blood glucose level [16,17]. For instance, some fruits are high in water and low in carbohydrates. In these cases, although their GI may be high, their GL will be low. Therefore, consuming one to two servings of these fruits will not raise blood glucose significantly when compared with other foods that have a high GI and GL. In addition to controlling carbohydrate intake, another of the strategies to reduce postprandial hyperglycaemia is to limit the activity of digestive enzymes involved in carbohydrate metabolism in the intestinal tract. In this regard, α-amylase is the enzyme that degrades the polymeric substrate into shorter oligomers by catalysing the hydrolysis of α-1,4-glucan bonds present in starch, maltodextrins and other related carbohydrates; α-glucosidase catalysed the hydrolytic cleavage of disaccharides (maltose and sucrose) into monosaccharides (glucose and fructose) for absorption in the human intestine [18]. Inhibition of α-amylase and α-glucosidase enzymes is therefore important for the control of postprandial glycaemia. Inhibitors of the latter reversibly occupy the binding sites of α-glucosidase on sugar, thereby reducing the degradation of polysaccharides and delaying the intestinal absorption of carbohydrates, thus achieving their hypoglycaemic effect. Clinically, these inhibitors can be used to treat T2DM to prevent the onset of hyperglycaemia and other associated cardiovascular risk factors such as hyperlipemia and obesity [19]. With the aim of promoting the use of the by-product orange juice powder as a natural food ingredient, in addition to the proximal characterisation of the powdered orange juice co-product, in terms of water, sugars, fats, proteins, ash and dietary fibre content, both soluble and insoluble fractions, some properties related to carbohydrate metabolism have been analysed, these being the GI, GL, glucose dialysis delay index, glucose adsorption capacity and α-amylase and α-glucosidase inhibition capacity. These in vitro bioactive properties and their relationship with fibre and phenols are the most novel aspects of the study. ## 2.1. Obtaining the Powdered Orange Juice Co-Product The orange juice waste used as raw material was provided by the Belles Arts Sant Carles cafeteria of the Universitat Poltècnica de València (Spain) in February 2022. To obtain the co-product powder (CoP), the residue, after completely removing the central column and seeds, was crushed and emulsified (Robot coupe blixer2, Valencia, Spain) and water was added at a ratio of $\frac{1}{0.38}$ to facilitate the process and create a homogeneous mixture [20]. Emulsification was carried out for 5 min per 750 g of residue. The mix was distributed into 16.8 cm radius aluminium plates to cover 1 cm thickness and frozen (Liebherr LGT 2325, Ochsenhausen, Germany) at −45 °C for at least 24 h until drying (Telstar LYOQUEST-55, Barcelona, Spain). The process conditions were −50 °C in the condenser, with a pressure of 0.05 mbar and a shelf temperature of 50 °C for 21 h. The freeze-dried cakes obtained were crushed (Thermomix®, Vorwerk, Spain) in batches of 40 g at 2000 rpm for 20 s, repeating the operation until all the powder obtained passed through a 200 µm sieve (CISA $\frac{200}{50}$, Barcelona, Spain), with the help of a sieve shaker (RP 200 N CISA, Barcelona, Spain). The reason for doing so was to ensure the same solute composition in the powder as in the orange juice waste used as raw material. ## 2.2. Proximate Composition Analysis The water content of the freeze-dried sample was determined with a Karl Fisher automatic titrator (Mettler Toledo, Compact Coulometric Titrator C10S, Worthington, OH, USA). Protein, ash, fat and total sugars content were analysed applying standard methods AOAC $\frac{955.04}{90}$, $\frac{942.05}{90}$, 920.39c and 31.042, respectively [21]. Total dietary fibre (TDF) content and its soluble (SDF) and insoluble (IDF) fractions were analysed by the enzymatic gravimetric method proposed by Johansson et al. [ 22] using a kit for the quantification of total dietary fibre (1.12979.0001, Sigma-Aldrich, Darmstadt, Germany). All analyses were performed in triplicate. Results are expressed as mean ± standard deviation. ## 2.3. Total Phenolic Compounds and Flavonoid Profile To determine the TP of the CoP, both FP and BP were extracted and the sum was considered. The main flavonoids present in each fraction were analysed. In both cases, the method described by Camacho et al. [ 20] was used. Briefly, 1 g of sample was extracted with MeOH at 30 °C and the filtrate obtained was extracted again with the same solvent but at 60 °C. The sum of the two extracts was FP. The residue obtained from the extractions was then subjected to basic hydrolysis followed by acid hydrolysis and its filtrate was extracted with MeOH at 30 °C, yielding the BP extract. The quantitative analysis of total phenols in both extracts was carried out using a modified Folin–Ciocalteau spectrophotometric method, according to Alu’datt et al. [ 23]. Measurements were performed with a UV–Vis spectrophotometer (V-1200 VWR, VWR, Radnor, PA, USA) and the phenolic content was expressed as mg GAE/100 g dry basis (db), using a GAE standard curve (Sigma-Aldrich, Steinheim am Albuch, Germany). The flavonoid profile of both FP and BP extracts was determined by UHPLC (Jasco equipment, Cremella, Italy) connected to a DAD detector (Jasco equipment, Cremella, Italy) and a Synergi 4 mHydro-RP 80 Å, LC column 150 × 4.6 mm (Phenomenex, Valencia, Spain) which was kept at 25 °C. The mobile phase used was composed of MeOH (A) and H2O (B), and linear gradient elution was performed starting at 30:70 (A:B) to reach 100:0 (A:B) at 30 min, with a flow rate of 1 mL/min and the injection volume was 10 μL. Chromatograms were recorded at 284 and 325 nm. The standard curves of the reference flavonoids, narirutin (Nat), hesperidin (Hes), didymin (Did), sinensetin (Sin), nobiletin (Nob) and tangerenin (Tan) (TCI Europe N.V., Paris, France) were used to quantify the flavonoids. ## 2.4. Antioxidant Activity The AOA of the FP and BP extracts was carried out by the DPPH (1,1-diphenyl-2-pricrylhydrazyl) method [24]. Results were expressed in mmol TE/100 g (db) using a Trolox standard curve (Sigma-Aldrich, Steinheim am Albuch, Germany) and the same spectrophotometer described above. ## 2.5. α-Amylase and α-Glucosidase Inhibition Assay Phenolic compounds have been described to play an important role in the inhibition of digestive enzymes [25,26,27]. In this case, a conventional phenolic compounds extraction was performed, according to the methodology described by Mccue et al. [ 28]. CoP (1 g) was mixed with 10 mL of distilled water, homogenized, centrifuged at 11,200× g at 4 °C for 20 min (Gyrozen 1236R, Daejeon, Republic of Korea) and filtered (0.45 μm membrane filter). Inhibition of α-A was determined following the method described by Alu’datt et al. [ 23]. The mixture composed of 40 µL of extract or control (distilled water) with 400 µL of a starch solution (R05YI, ROQUETTE, Benifaió, Spain) and 200 µL of α-A (A3176-500U, Sigma-Aldrich, St. Louis, MO, USA) was kept at room temperature for 3 min and 3,5 dinitro salicylic acid (DNSA) method was used for detection of reducing sugars and determining the absorbance (A) at 540 nm. The percentage inhibition of the enzyme was calculated following Equation [1]. [ 1]% Inhibition∝-$A = 1$ − AsampleAcontrol × 100 Inhibition of α-G was determined using an α-G activity assay kit (MAK123, Sigma-Aldrich, St. Louis, MO, USA) following the protocol of the technical bulletin. 20 µL of phenolics extract was mixed with 200 µL of α-glucosidase solution and incubated at 37 °C for 20 min. The absorbance (A) was measured at 405 nm and the per cent enzyme inhibition was calculated following Equation [2]. [ 2]% Inhibition∝-$G = 100$−Asamplefinal − AsampleinitialAcalibratorfinal − AWaterfinal ## 2.6. Glucose Adsorption Capacity The GAC of the CoP was performed according to Flores-Fernandez et al. [ 8]. CoP ($1\%$) was added to 25 mL of a glucose solution (100 mM) in sextuplicate. Three of the mixtures were analysed for initial glucose using the glucose oxidase-peroxidase (GOD-POD) method for samples without resistant starch (Starch assay kit, STA-20, Sigma-Aldrich, St. Louis, MO, USA), using the reagents glucose oxidase-peroxidase (G3660), o-dianisidine dihydrochloride (D2679-1VL) and D-(+) glucose solution, all Sigma (Vidra-Foc, Barcelona, Spain) and measuring the absorbance at 540 nm. The remaining three mixtures were placed in an incubation chamber (Nüve Test Gabinet chamber TK120, Istanbul, Turkey) at 37 °C for 6 h, under constant agitation. Subsequently, they were centrifuged at 4000× g for 20 min and the final glucose in the supernatant was analysed in the same way as described above. GAC was calculated using Equation [3]. [ 3]GAC=Ci− Cfm × V where Ci and Cf are the glucose concentrations of the samples before and after incubation, respectively, V is the volume of solution and m is the weight of CoP used for the test. ## 2.7. Glucose Dialysis Retardation Index The GDRI of the CoP was determined as described by Fuentes-Alventosa et al. [ 29]. Samples of 400 mg of sugar-free CoP (extracted twice with $80\%$ ethanol) were completely hydrated with 15 mL of distilled water containing 30 mg of glucose. After 1 h under continuous agitation, the samples were transferred into pre-hydrated dialysis bags (15 cm length) (12,000 MWCO, Sigma Chemical Co, Merk, Darmstadt, Germany). Each bag and a control bag (with glucose, but without sample) were placed in a reservoir containing 400 mL of distilled water and kept in a thermostatic water bath at 37 °C for 1 h with constant agitation. After this time, the glucose concentration was determined spectrophotometrically (500 nm) by the GOD-POD method. GDRI from the dialysis bag into the dialysate was calculated using Equation [4]. [ 4]GDRI=(100 −CsCc) × 100 where Cs and Cc are the glucose concentrations of the samples and the control, respectively. ## 2.8. Estimated Glycaemic Index An in vitro digestion of the CoP was performed as described by Brennan and Tudorica [30], involving a proteolytic stage followed by incubation with pancreatic α-amylase restricted by dialysis tubing. Every 15 min for 120 min, aliquots of 1 mL from the dialysate were withdrawn in triplicate for analysis of reducing sugar content using DNSA method. The withdrawn dialysate was replaced each time with sodium–potassium phosphate buffer. A standard curve using glucose was prepared. Equation [5] was used to calculate the hydrolysis index (HI), where A represents the “area under the glycaemic response curve (amount of glucose dialysed as a function of time)”, and Equation [6] for the estimated glycaemic index (GIe). [ 5]HI=(AsampleAcontrol) × 100 [6]GIe=0.862 × HI+8.198 ## 3.1. Characterization Proximal and Phytochemical Analysis Table 1 shows the results obtained from the analysis of the proximate composition, the total content of free and bound phenols content and the sum of the identified flavonoids of the powdered co-product studied. The water content was in the range recommended by other authors for high-quality freeze-dried products [31]. Table 1 also shows that CoP is rich in nutritional ingredients such as total sugars, proteins and minerals. However, it also has a low-fat content. All these values were similar to those found by other authors on citrus peel [32,33,34]. The CoP may be considered a high-fibre food ingredient, with a total DF amount of 36.67 ± 0.11 g fibre per 100 g of co-product. These values are similar to those found in other studies, also for orange peel [35,36,37]. As regards the fibre fractions, SDF is responsible for an increase in viscosity in the intestine that hinders glucose diffusion and absorption, as well as α-amylase activity [8]. This enzyme is involved in the digestion and absorption of CHO by hydrolysing the α-1,4-glycosidic bonds within the glucose polymers ingested with the diet. IDF supports intestinal health by promoting regular bowel movements, delaying gastric emptying and possibly having a laxative effect [38]. In addition, IDF is able to bind carcinogens, mutagens and other toxic chemicals formed during food digestion, allowing their subsequent elimination through faeces [39]. Most foods containing fibre have more IDF than SDF, so in general, one-third of fibre is soluble, and two-thirds of fibre is insoluble [40]. In the case of CoP the IDF/SDF ratio (11.7) was significantly higher. Similar relationships have been found by other authors in previous citrus peel studies [35,36,37]. From this point of view, it does not appear that CoP can be proposed as an ingredient to assist in the control of postprandial glycaemia. The content of TP present in the orange juice co-product powder was 509 ± 15 mg GAE/100 g co-product powder (db), of the same order as that found by Escobedo-Avellaneda et al. [ 41]. Of the total phenols analysed, $68\%$ were found in the free fraction and $32\%$ in the bound fraction, the latter associated with the cell wall and more difficult to extract (Table 1). The results obtained agree with those of Alu’datt et al., who showed that most phenols are found in free form in different fruits of the Rutaceae family [23]. Figure 1 shows an example of a UHPLC chromatogram which, at 284 and 325 nm, shows the peaks corresponding to the flavonoids identified in this study. Nat, Hes and Did, glycosylated flavanones, were identified at 284 nm. Sin, Nob and Tan, methoxylated flavones, were identified at 325 nm. The flavonoids identified in our study coincide with those found by Manthey and Grohmann [42] and Escobedo-Avellaneda et al. [ 41]. All the flavonoids eluted in the order indicated by other authors [43,44]. Table 1 shows the total flavonoid content quantified in the free and bound fractions. Specifically, the total content of Hes, Nat, Did, Sin, Nob and Tan associated with the powdered by-product (mg/100 g by-product) was 4045 ± 165, 529 ± 15, 128 ± 3, 69.5 ± 0.4, 74 ± 2 and 6.9 ± 1.5, respectively. Consistent with the references consulted, the main flavonoids found in each phenolic fraction were the flavanone glycosides Hes and Nat [41]. On the other hand, $96.4\%$, $99.5\%$ and $90.8\%$ of the analysed Hes, Nat and Did, respectively, and $100\%$ of Sin, Nob and Tan were found in the free fractions. To further evaluate the functionality of phenolic compounds, DPPH radical scavenging activity was determined, which is the most representative indicator reflecting the antioxidant activity of a plant extract [45]. The antioxidant activities of the free and bound extracts were 1.3 ± 0.2 and 1.9 ± 0.2 mmol Trolox equivalent (TE)/100 g (db), respectively. The total 3.2 mmol TE/100 g is in the range of other fruits recognised for their high antioxidant capacity [46]. It is worth noting that the bound fraction, with the lowest TP and identified flavonoid content, had the highest AOA. The distribution of antioxidant activity associated with each of the fractions indicates that the FF and BF fractions contributed $40.6\%$ and $59.4\%$ of the total antioxidant activity of orange peel, respectively. This is in concordance with Zou et al. [ 47] and Alu’datt et al. [ 23], who reported that bound phenolic compounds extracted from some citrus fruits had higher antioxidant activity than free phenolic compounds. Among the bound phenolic compounds, phenolic acids are the most abundant [48], and of these, cinnamic acids such as ferulic, coumaric, caffeic and synaptic acids, among others, have the highest antioxidant activity in citrus peel compared to other phenolic compounds [49]. In wild Chinese mandarins, ferulic acid is the main contributor to AOA [50]. Thus, it could be argued that these phenolic acids may be the major contributors of antioxidant activity to the BF fraction. ## 3.2. Bioactivity Assays Both α-amylase and α-glucosidase are enzymes that help to release glucose, so their inhibition helps to lower glycaemia [51]. The activity of these enzymes seems to be inversely related to the presence of phenolic compounds in the sample due to their ability to interact with the enzyme to decrease its catalytic activity, either through conformational changes or by binding at the active site [25,26,27]. Xiong et al. [ 52] report that this inhibition is not only dependent on the concentration of phenolic compounds but also on their composition or phenolic profile. The effect on α-glucosidase and α-amylase of some compounds may be different. For example, in a study testing twenty-one naturally occurring flavonoids, hesperidin and kaempferol activated α-glucosidase and largely inhibited α-amylase, while luteolin and quercitrin largely inhibited both enzymes [53]. Rasouli et al. [ 54] also reported differential α-glucosidase/α-amylase inhibitory activities of phenolic compounds. The α-A inhibition determined for CoP (Table 2) was higher than those obtained in other studies for different citrus powders acquired from the edible part of pummelo ($30.2\%$), lemon ($11.6\%$), grapefruit ($18.9\%$) or different orange varieties such as shamouti ($29.5\%$), clementine ($29.8\%$) and red-orange ($32.3\%$) [22]. The α-G inhibition values (Table 2) were higher than in the cases mentioned above, although similar to those of the lemon powder ($100\%$). This may be due to the fact that the phenolic compound content of the edible part of the fruit is lower than that of the peel [55]. In any case, the ability of phenols to inhibit the activity of these two enzymes would indeed contribute to lowering blood glucose levels. The GAC of the freeze-dried orange juice co-product (Table 2) was higher than that presented in other studies, such as that of citrus limmetta peel flour, which presented a value of 16.58 mM [8]. This high capacity could be related to the high content of insoluble fibre, which can effectively absorb glucose [56]. Figure 2 shows the glycaemic response curve of CoP versus control (glucose), used to calculate the hydrolysis index (Equation [5]), which is necessary for the estimation of the glycaemic index (Equation [6]), value shown in Table 2 for CoP. As stated by Sivakamasundari et al. [ 13], foods with rapidly digestible, absorbed and metabolized carbohydrates are considered high GI (values with reference to glucose greater than or equal to 70). Medium GI foods are those with values greater than 55 and less than 70. Foods with carbohydrates whose physiological mechanisms are slower and have less impact on blood glucose and insulin levels are considered low GI (GI values less than or equal to 55). Carbohydrates with rapid absorption result in high GI values, while those with slow absorption produce flatter glycaemic responses and consequently low GI [57]. Considering these values, GIe of CoP is low, lower than those obtained for most fruits and other foods and more in the order of that of pulses [15,58], although similar to that of the peel of another citrus fruit such as grapefruit (19.89 ± 2.88) [59]. From the value of GI and the sugar content of the CoP, an estimate of its GL was made, by using the calculation procedure described in the introduction section. Assuming that the 46 g of total sugars analysed correspond to the CHOa of each 100 g CoP (Table 1, [60]), the GL calculated would be 11 g CHOa/100 g CoP. Although the GL may be expressed in this way, for convenience, it is usually referred to as a serving. As the powdered co-product studied is not intended for direct consumption but rather as an ingredient to be added in the preparation of different foods, it is difficult to propose the amount of CoP that a serving can contain. For reference, the following assumption was made. If this ingredient were to be added to a fibre-free food, such as yoghurt, e.g., in order to be labelled as a “source of” fibre, this would mean that the food would have to contain $3\%$ fibre according to European legislation (Reg (EC) $\frac{1924}{2006}$). As the fibre content of the CoP is 36.67 g fibre/100 g CoP (Table 1), 8.18 g CoP would need to be added to every 100 g of the formulated yoghurt to achieve the target. In this case, the GL of the CoP needed to guarantee the fibre content of a “source of fibre” food would be 0.918 g CHOa per serving. This is a very low GL, as a GL above 20 and average values between 11 and 19 are considered high and moderate glycaemic load values, respectively [15]. In this way, the CoP provides low available carbohydrates per serving (low GL), which are also slowly absorbed (low GI). Therefore, its consumption can be a tool to help control postprandial glucose levels, which is particularly suitable for people suffering from diseases such as T2DM or overweight. Finally, with respect to IRDG (Table 2), the CoP presented a higher value than other citrus fruits such as lemon ($5\%$) [61], but lower than other foods such as asparagus powder co-product [29], banana, dragon fruit and cantaloupe [62] or pea peel [63], among others, with values ranging from 15 to $48\%$. Despite the high fibre content of CoP, this low IRDG can be justified by the low soluble fibre content mentioned above (Table 1). Even so, CoP could be effective in slowing glucose absorption because of its high capacity for glucose adsorption and inhibition of the enzymes α-amylase and α-glucosidase. ## 4. Conclusions According to the results obtained, the powdered orange juice by-product is a waste with a high content of insoluble fibre, which confers various beneficial properties for health, favouring a greater faecal volume and accelerating intestinal transit time. From this point of view, it can be recommended to be used as an ingredient to increase the fibre content of foods and come closer to WHO recommendations, while, due to its low glycaemic index and glycaemic load, it does not contribute to the increase of postprandial glycaemia. In addition, its high inhibition capacity, especially of α-glucosidase, but also of α-amylase, related to the high content of phenolic compounds, and its glucose adsorption capacity gives it a certain capacity for the regulation of postprandial glucose. 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--- title: Can a Multidisciplinary Weight Loss Treatment Improve Motor Performance in Children with Obesity? Results from an Observational Study authors: - Francesca Gallè - Giuliana Valerio - Espedita Muscariello - Ornella Daniele - Valentina Di Mauro - Simone Forte - Teresa Mastantuono - Roberta Ricchiuti - Giorgio Liguori - Pierluigi Pecoraro journal: Healthcare year: 2023 pmcid: PMC10048705 doi: 10.3390/healthcare11060899 license: CC BY 4.0 --- # Can a Multidisciplinary Weight Loss Treatment Improve Motor Performance in Children with Obesity? Results from an Observational Study ## Abstract In the last two decades, the relationship between weight status and children’s motor skill competence has been receiving increasing attention, given its possible role in the prevention and treatment of obesity. This study aimed to evaluate the effect of a multidisciplinary obesity treatment on motor performance in a sample of Italian children and adolescents. Visual and auditory reaction time (VRT and ART), vertical jump elevation (VJE) and power (VJP), body mass index (BMI) and BMI-standard deviation score (BMI-SDS), waist circumference (WC), body composition, dietary habits and physical activity (PA) levels were assessed at baseline and at 6- and 12-month follow-up. Significant improvements were observed in BMI-SDS and FFM, diet and PA levels. Adolescents showed significant improvements in VRT and ART. Jump elevation and power increased in both children and adolescent subgroups. Girls exhibited greater changes than boys in both VRT and ART and VJP but lower changes in VJE. VRT improvement was related to age (OR = 0.285, $95\%$CI 0.098–0.830, $$p \leq 0.021$$) and FFM (OR = 0.255, $95\%$CI 0.070–0.933, $$p \leq 0.039$$). An increase in VJE was associated with BMI-SDS (OR = 0.158, $95\%$CI 0.036–0.695, $$p \leq 0.015$$) and with PA level (OR = 19.102, $95\%$CI 4.442–82.142, $p \leq 0.001$); the increase in VJP was related with the increase in PA (OR = 5.564, $95\%$CI 1.812–17.081, $$p \leq 0.003$$). These findings suggest the possible effects of a multidisciplinary obesity treatment on children’s motor competence. Since the improvement in motor skills can increase children’s motivation and adherence to weight loss treatment in the long term, these aspects should be further investigated. ## 1. Introduction In the last two decades, the growing prevalence of childhood obesity observed in many countries has resulted in many efforts from the scientific community to identify the most effective strategies to prevent or treat this condition [1,2]. Among these, multidisciplinary weight-loss interventions, including personalized nutritional and physical activity (PA) plans and psychological support for children and their families, have been recognized as useful measures for childhood obesity treatment [2,3]. With the aim of increasing adherence to PA recommendations, many studies have been focused on motor competence in obese children, showing that this is inversely related to their weight status [4,5,6]. Indeed, well-developed motor skills are considered a precursor of lifelong engagement in physical activity. Differences in motor performance between children with and without obesity have been found for gross motor tasks, as a mechanical consequence of excess body mass, and even for fine motor tasks, which are predominantly influenced by the ability to process sensory information and transmit proper muscle commands [7,8,9,10]. Carrying out everyday activities requires that children manage gross to fine motor coordination, and at the same time, motor skill competence represents a key determinant of PA engagement [4,11,12,13]. Therefore, the relationship between weight status and children’s motor skill competence is receiving increasing attention, given its possible role in the prevention and treatment of obesity [14,15,16,17]. The literature shows that children with obesity have poorer gross motor coordination performance than their healthy-weight peers, which highlights that weight status affects children’s motor competence levels [9]. Accordingly, weight loss related to obesity treatment interventions may have a beneficial effect on motor skills. In adults and adolescents, weight loss is associated with significant improvements in muscle function, motor control and performance [18,19]. In children, however, research has been so far mainly focused on the effects of weight reduction programs on body fat and other health-related measures, PA levels and physical fitness performance [17]. Studies examining whether weight loss achieved by attending a multidisciplinary program for obesity treatment may also improve children’s motor skill competence and coordination in the long term are still scarce [20,21]. Reaction time (RT) is the time interval between the application of a stimulus and the appearance of a proper voluntary response in a subject [22]. The measurement of RT is considered a simple and valuable cognitive test to evaluate the time needed to initiate and execute a given action [23]. Some factors, such as age and PA levels, have been found to be associated with RT [22,24,25]. Furthermore, some studies have shown a relationship between visual or auditory RT and overweight/obesity, with contradictory results [10,22,24,26,27,28,29]. Therefore, it would be interesting to examine whether perceptual-motor performance can improve in the long term along with weight reduction in children with obesity attending a multidisciplinary obesity treatment intervention. Since 2018, a second-level outpatient service for childhood obesity treatment based on the multidisciplinary cooperation of nutritionists, psychotherapists and kinesiologists, is active in the province of Naples, a county town of the Campania region, south Italy [30]. The aim of this study was to evaluate the effect of this multidisciplinary intervention on weight-related parameters, visual and auditory RT and jump performance in children participating in the treatment. We hypothesized that children following the program would show improvements in both areas (weight and motor skills) with respect to the baseline measures at the start of treatment. It was expected that the improvement in motor performance would be associated with parameters related to weight loss. Possible gender differences were also explored. ## 2.1. Study Design This was an observational study performed to assess anthropometric and motor performance changes in a sample of children and adolescents undergoing a multidisciplinary obesity treatment. All the activities were performed respecting the principles of the Declaration of Helsinki. The study was approved by the Local Health Authority “Napoli 3 Sud” of Naples review board (Deliberation n. 92 of 31 January 2020). ## 2.2. Participants and Setting Children and adolescents with obesity attending the outpatient clinic “Second Level Assistance Center for Diabetes and Obesity in Childhood” of the Local Health Authority “Napoli 3 Sud” were invited to participate in the study. Parents or guardians of the outpatients who accepted to participate signed informed consent to the use of their data. Participants were consecutively enrolled. All the activities related to the intervention and the data collection were performed in the outpatient clinic by the same professionals. ## 2.3. Intervention The multidisciplinary intervention has been previously described [30]. Briefly, it is structured in monthly sessions, which include (a) nutritional counseling, with dietary assessment and measure of anthropometric parameters, (b) PA counseling, with an assessment of daily activities and prescription of tailored PA protocols, and (c) motivational support for lifestyle change and maintenance. Each activity is performed by a trained professional in the presence of parents/guardians. ## 2.4. Outcomes Demographic features of children were collected at the first visit through parents’/guardians’ interviews. Participants were observed for a twelve-month period from November 2019 to September 2021. The following outcomes were assessed at baseline (indicated as T0), after six months (T6) and at 1-year follow-up (T12). Anthropometric measurements—body mass index (BMI), waist circumference (WC), bioimpedance analysis and functional tests (Vertical Jump Test-VJT; Reactive tests)—were performed at each visit. Weight and height were measured with a weight scale and altimeter (Wunder Model C201) according to standardized procedures by the same investigator, specifically trained. The BMI was calculated and expressed in kg/m2 and converted into BMI standard deviation score (BMI-SDS) [31]. WC was measured through a measuring tape (Seca, Hamburg, Germany) and expressed in cm. Body composition was assessed by bioelectrical impedance analysis (DS Medica, Milan, Italy); fat mass (FM) and fat-free mass (FFM) were expressed both in percentages and kilograms. Visual reaction time (VRT) and auditory reaction time (ART) were measured by using the OptoJumpTM infrared system (Optojump, Microgate, Bolzano, Italy). Each subject was invited to execute a jump inside the measurement area as quickly as she/he could when receiving a visual or auditory stimulus from the monitor. The OptojumpTM system recorded the reaction time. The lower-limb strength was assessed through the squat jump test (SJT). The OptoJumpTM infrared system was used to measure the maximum vertical jump with the hands placed aside. Vertical jump elevation (VJE) and power (VJP) were measured by computing the time between contact time and flight time. Three attempts were allowed, with a break of one minute between each attempt; the best performance was recorded for each participant. VRT and ART were expressed in seconds; jump elevation was expressed in centimeters and jump power was expressed in Watt/kg. Dietary habits were assessed through the 16-item KIDMED questionnaire, a tool widely used to measure the degree of adherence to the Mediterranean diet (MD) model in children and adolescents. The KIDMED total score ranges between 0 and 12 and defines three categories: poor (≤3), average (4–7) and good (≥8) adherence to MD [32]. In order to assess possible changes in PA levels in the course of the treatment, the Physical Activity Questionnaire for Children (PAQ-C) [33] was administered to all the participants at baseline and at both follow-up measurements. The PAQ-C includes 10 items: the first item is a checklist including several common sports, leisure activities and games; items 2–8 aim at assessing activity during the specific day, including physical education class, recess, lunch, immediately after school, evening and the weekend, with two additional questions to assess overall activity patterns during the week. The ninth item regards the frequency of activities performed each day during the week, and item 10th focuses on child health. Each question, except item 10th, is scored using a scale that ranges from 1 to 5. The mean of the items is used to calculate the final PAQ-C summary score. The test-retest reliability of both KIDMED (κw = 0.591; $95\%$CI 0.485, 0.696) and C-PAQ (males, $r = 0.75$ and females, $r = 0.82$) has been measured in previous studies [33,34]. ## 2.5. Statistical Analyses All the variables examined were checked for normal distribution. According to the data distribution, mean and standard deviation (SD) or median and interquartile ranges (IQR) values were used to describe the variables and their comparison between times. The significance of the changes registered throughout the three times was evaluated through ANOVA or Friedman’s test for related samples. Considering the width of the participants’ age range, the comparisons were performed separately for children (7–11 years) and adolescents (12–17 years). The motor outcomes were also compared between baseline and 12-month follow-up in female and male subgroups by using the Student’s t-test for related samples or the Wilcoxon’s test. Multinomial regression analyses were performed to highlight possible relationships between changes in measured outcomes (VRT, ART, jump height and power) and changes in BMI-SDS, FFM and CPAQ category after 12 months of follow-up in the whole sample. To this aim, all these variables were dichotomized as follows. Increase or no change in BMI-SDS, VRT and ART were expressed with a “0” value, and their decrease was expressed as “1”; reduction or no change in FFM, VJE, VJP, KIDMED and CPAQ scores were coded as “0” while their increase was coded as “1”. All the regression analyses were carried out, controlling for age and gender. ## 3. Results On a total of 248 admitted children and adolescents, 82 youths ($45.1\%$ F, mean age 11.1 ± 2.5, range 7–17.3 years) for whom three measurements were available during the follow-up (at the beginning, after 6 and 12 months) were examined. Table 1 and Table 2 show the changes that occurred in the mean or median values of the examined variables across the three times of follow-ups in the group of children ($$n = 40$$) and adolescents ($$n = 42$$), respectively, with the corresponding p-values. As for the anthropometric parameters, a significant decrease in BMI-SDS and an increase in FFM (kg) was observed across the three times in both children and adolescents. Children also showed a significant decrease in BMI since baseline. With regard to the other parameters, the decrease in VRT and ART was significant only in the adolescents’ group and not in children, while jump parameters improved significantly in both groups. Even the adherence to MD and levels of PA of both children and adolescents significantly increased over the time considered. Figure 1 and Figure 2 show the changes that occurred in reaction times and jump parameters among participants by gender after 1-year follow-up. Although both groups showed improvements in the examined parameters, girls exhibited greater changes in both VRT and ART and jump power but lower changes in jump height than boys. VRT changed significantly between T0 and T12 in both males (Δ = −0.12, $p \leq 0.01$) and females (Δ = −0.11, $p \leq 0.01$), while ART changed significantly in females (Δ = −0.11, $$p \leq 0.01$$) but not in males (Δ = −0.07, $$p \leq 0.057$$). VJE changed significantly in males (Δ = 1.79, $p \leq 0.01$) and females (Δ = 1.19, $p \leq 0.01$), as well as VJP (males Δ = 0.74, $p \leq 0.01$; females Δ = 0.85, $$p \leq 0.01$$). As for the regression analyses, VRT improvement was found to be inversely related to age (OR = 0.285, $95\%$CI 0.098–0.830, $$p \leq 0.021$$) and with changes in FFM (OR = 0.255, $95\%$CI 0.070–0.933, $$p \leq 0.039$$); no relationships were found between changes in ART and those detected in the other parameters. An increase in VJE was found to be associated with BMI-SDS (OR = 0.158, $95\%$CI 0.036–0.695, $$p \leq 0.015$$) and with PA level (OR = 19.102, $95\%$CI 4.442–82.142, $p \leq 0.001$); even the increase in VJP was positively related with the increase in PA (OR = 5.564, $95\%$CI 1.812–17.081, $$p \leq 0.003$$). ## 4. Discussion A multidisciplinary intervention in children with obesity has the main objective of reducing excess weight through a permanent change in eating habits and lifestyle while promoting functional mobility and health-related quality of life. In this study, an improvement in diet, physical activity and weight-related outcomes were found in both children and adolescents after the 1-year weight-loss intervention, along with an improvement in reaction times and jump parameters. As for reaction times, significant improvements were observed in adolescents and in both male and female participants throughout the intervention, suggesting a correlation between weight loss and these outcomes, especially in the higher age class. However, in the regression analyses, only the VRT improvement was found to be related to the increase in fat-free mass. These results are partially in line with those of Moradi et al., who, in 2017, examined the relationship between RT and weight status in a sample of 350 9–12 years-old schoolboys [28]. Among the various RT tasks and obesity indices used, they did not detect significant relationships between ART and BMI, %fat, WC and waist-to-height ratio (WHtR), but just VRT was found to be significantly related with %fat (but not with BMI, WC and WHtR). The inverse relationship found between visual reaction time and age was previously reported in the literature [22,24,35,36,37]. Therefore, it is possible that the stronger difference observed in reaction time changes among adolescents from our sample is related to their different growth stages. Longer follow-up periods and the comparison with a gender and age-matched control group of children with obesity who do not participate in the multidisciplinary treatment can be useful to clearly define the possible effects of the intervention on children’s reaction time. The improvement in jump performance, which is a proxy of leg strength, and the association found between jump elevation and BMI-SDS are in accordance with previous literature in this field. The studies by Lazzer et al. showed similar improvements in lower limb power along with body weight and composition improvements even after short obesity treatment interventions [14,15]. The study by D’Hondt et al. reported that the amount of weight loss achieved by children participating in a multidisciplinary residential treatment for overweight/obesity explained $26.9\%$ of the variance in gross motor skills, including jumping performance [21]. On the basis of this finding, the authors concluded that a multidisciplinary residential treatment and concomitant weight loss could be considered an important means to improve gross motor coordination in children with obesity, which in turn may promote their participation in PA. In fact, participation in PA can be easier in children with a high level of motor competence, while children with a lower level of motor competence tend to avoid movement difficulties and may be engaged in sedentary activities [38]. This aspect should be considered in order to increase adherence to the PA plan proposed throughout the treatment. In our study, the improvement observed in jump height and power was found to be related to the increase in PA level. This further underlines the relationship between motor skills and motivation to PA. This study has some important limitations. First of all, the lack of comparison did not allow us to verify the association between the outcomes and the intervention. Furthermore, it should be considered that the adherence to dietary and PA plans was not objectively monitored but only self-reported. However, it should be noted that both KIDMED and PAQ-C are widely used and have been shown to be adequate for investigating changes in children’s behaviors [38,39]. The results of this investigation may contribute to the definition of the best methods to assess the effectiveness of multidisciplinary obesity treatments besides weight loss. The longitudinal design of the investigation and the analysis of children’s body composition and perceptual-motor competence, a field still scarcely explored, represent other strengths of this study. ## 5. Conclusions The findings of this study showed a significant improvement in motor performance in children undergoing a multidisciplinary obesity treatment, expressed by a decrease in visual and acoustic reaction time and an increase in jump height and power. The improvement in the visual reaction was associated with the increase in fat-free mass, and the improvement in jump elevation was related to the decrease in BMI-SDS, suggesting a direct effect of the modified weight and body composition on these skills. Furthermore, the improvement in jump performance was found to be significantly related to the observed increase in physical activity. These findings underline the role of multidisciplinary obesity treatment in improving youths’ behaviors and related health conditions and also highlight the possible benefits of this type of intervention on youths’ motor competence. Since improvement in motor skills can increase children’s motivation and their adherence to weight loss treatment in the long term, further controlled studies performed on wider samples and for longer periods are needed to confirm these findings. ## References 1. Kumar S., Kelly A.S.. 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--- title: Cryogel System Based on Poly(vinyl alcohol)/Poly(ethylene brassylate-co-squaric acid) Platform with Dual Bioactive Activity authors: - Bianca-Elena-Beatrice Crețu - Alina Gabriela Rusu - Alina Ghilan - Irina Rosca - Loredana Elena Nita - Aurica P. Chiriac journal: Gels year: 2023 pmcid: PMC10048711 doi: 10.3390/gels9030174 license: CC BY 4.0 --- # Cryogel System Based on Poly(vinyl alcohol)/Poly(ethylene brassylate-co-squaric acid) Platform with Dual Bioactive Activity ## Abstract The inability to meet and ensure as many requirements as possible is fully justified by the continuous interest in obtaining new multifunctional materials. A new cryogel system based on poly(vinyl alcohol) (PVA) and poly(ethylene brassylate-co-squaric acid) (PEBSA) obtained by repeated freeze–thaw processes was previously reported and used for the incorporation of an antibacterial essential oil—namely, thymol (Thy). Furthermore, the present study aims to confer antioxidant properties to the PVA/PEBSA_Thy system by encapsulating α-tocopherol (α-Tcp), targeting a double therapeutic effect due to the presence of both bioactive compounds. The amphiphilic nature of the PEBSA copolymer allowed for the encapsulation of both Thy and α-Tcp, via an in situ entrapment method. The new PVA/PEBSA_Thy_α-Tcp systems were characterized in terms of their influence on the composition, network morphology and release profiles, as well as their antimicrobial and antioxidant properties. The study underlined the cumulative antioxidant efficiency of Thy and α-Tcp, which in combination with the PEBSA copolymer have a synergistic effect ($97.1\%$). We believe that the convenient and simple strategy offered in this study increases applicability for these new PVA/PEBSA_Thy_α-Tcp cryogel systems. ## 1. Introduction A wound is defined as the disruption of the anatomical structure and normal function of the skin, caused by various types of trauma, burns, or surgery [1]. As the wound occurs, the healing process begins [2]. During this process, the generation of an excess of reactive oxygen species (ROS) also occurs as part of the defense mechanism against pathogens. ROSs are small molecules derived from unstable oxygen that try to stabilize themselves by capturing the electrons of some molecules in living organisms, implying the appearance of associated complications (dysfunctions at the level of cell membranes, conformational changes in proteins, loss of enzyme roles along with breaking DNA chains) [3,4]. Antioxidants are chemical compounds that can alleviate oxidative stress by donating electrons to other molecules, such as ROSs, and support the wound healing process [5]. Antioxidants can be classified as endogenous, produced naturally in the body as, for example, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and exogenous obtained through the diet, such as carotenoids, vitamin C, vitamin E and flavonoids, among others [6]. Among the tocopherols, α-Tcp is the most abundant form of vitamin E and is considered a powerful fat-soluble antioxidant that protects membrane lipids against oxidation and contributes to the mechanical stabilization of membranes through Van der Waals physical interactions [7]. The administration of α-tocopherol, as well as the other liposoluble vitamins, presents some particularly important challenges due to its low water solubility and stability [8]. Moreover, vitamins are sensitive molecules so they require protection from pro-oxidant factors such as oxygen, UV or high temperatures. In this sense, the encapsulation of vitamins into a polymeric network could be a promising approach to preserve their chemical integrity and effectiveness, but also their controlled release, thus reducing the occurrence of hypervitaminosis syndrome [9]. Conventional hydrogels are commonly used by scientists to overcome the problems mentioned above, but the problems surrounding their implementation on a large scale increase due to the closed and small cavity of hydrogels. Cryogels have received tremendous attention in applications targeting the controlled release of active principles and tissue engineering of the skin, considering their large pore size, rough surface, absorption capacity and rapid swelling [10,11]. PVA is one of the most investigated water-soluble synthetic polymers in obtaining cryogels, with applications as drug carriers, in wound dressings and for tissue engineering due to its biocompatibility, biodegradability and non-toxicity [12]. A new cryogel system based on PVA and PEBSA obtained by repeated freeze–thaw processes was previously reported and used for the incorporation of an antibacterial essential oil—namely, Thy [13]. Thy, chemically known as 2-isopropyl-5-methylphenol, is a natural monoterpenoid phenol which is isolated from Thymus vulgaris and other plants such as Ocimum gratissimum L., Origanum L., Satureja thymbra L. [14]. Various pharmacological properties of thymol have been investigated and reported, including antimicrobial, antifungal, antioxidant, anti-inflammatory, analgesic, and healing activities [15,16,17,18]. PEBSA, a copolymacrolactone system was synthetized from ethylene brassylate (EB) and squaric acid (SA) by the ring-opening copolymerization procedure described before [19,20]. The supramolecular structure and high functionality of PEBSA copolymer, as well as its biocompatibility and good thermal stability, have led to it being recommended as a matrix for the incorporation of hydrophobic bioactive compounds [21,22]. In order to achieve a system with dual effect and activity, specifically antioxidant and antimicrobial, the alfa-tocopherol-thymol bioactive formulation was encapsulated in the new PVA/PEBSA polymer network. The PEBSA copolymer, due to its amphiphilic character, allowed for the encapsulation of hydrophobic bioactive substances such us Thy and α-Tcp, via an entrapment in situ method. Moreover, the hydrophobic affinity of the compounds involved in the system led to a good dispersion of the bioactive molecular agents in the PEBSA polymer network [20]. The newly prepared bioactive complexes were characterized in terms of the influence of the composition on the network morphology and release profiles, as well as the dual bioactive behavior due to the presence of Thy and α-Tcp. To support previous studies, PVA/PEBSA_Thy_α-Tcp systems were prepared using PEBSA with three different ratios between EB and SA comonomers, namely, $\frac{25}{75}$, $\frac{50}{50}$ and $\frac{75}{25.}$ To our knowledge, there have been no reported studies focusing on the synergistic effect between the antimicrobial properties of Thy and the antioxidant properties of α-Tcp associated with biomedical applications. Overall, this study offers a convenient strategy to achieve a PVA/PEBSA cryogel system with dual therapeutic effect due to the presence of both bioactive compounds. We intend to use the PVA/PEBSA_Thy_α-Tcp cryogel system for antimicrobial and wound healing applications, and in this regard, we carry out the necessary studies. ## 2. Results and Discussion The present study aims to obtain cryogels based on PVA/PEBSA incorporating Thy and α-Tcp, targeting dual activity generated by the presence of the two bioactive compounds. Our group envisioned this approach to enhance the efficacy of these new antimicrobial cryogels to be used as wound dressings. The encapsulation of the bioactive compounds into the PEBSA polymeric matrix was realized by an inclusion complexation performed in 1,4-dioxane by entrapping of Thy, of α-Tcp, into the amphiphilic PEBSA network (Figure 1). ## 2.1. Morphological Analysis The internal morphology of the PVA/PEBSA polymer matrix and PVA/PEBSA_Thy_α-Tcp systems were evaluated by SEM. As can be seen in Figure 2A, the SEM micrographs of cryogels based on PVA and PEBSA copolymacrolactone illustrate their three-dimensional network with interconnected honeycomb-like pores and numerous meshes, which can ensure the incorporation of bioactive molecular compounds. The morphology of the PVA/PEBSA_Thy_α-Tcp systems highlights a relatively uniform distribution of the bioactive substances on the surface of the polymer network. The homogeneous distribution of Thy and α-Tcp in the PEBSA polymer network is due to the amphiphilic character of the copolymer and also to the hydrophobic affinity of the compounds involved in the system. Some differences in their morphology result from the cryogel preparation condition namely, the ratio between EB and SA comonomers in the copolymacrolactone system as well as the amount of bioactive compound. Therefore, in the case of the PVA_PEBSA_Thy_α-Tcp sample (Figure 2B–D), with the increase in the EB/SA ratio in the sample, the pore size increases. This is associated with an increase in the number of carbonyl functional groups in SA, which further results in a higher number of hydrogen bonds between the mixing partners. Thus, the structure and pore size of PVA/PEBSA cryogels can be modified by adjusting the ratio between EB and SA comonomers from PEBSA composition. The morphological characterization of the new variants of cryogels is consistent with that recently reported in the literature [13]. ## 2.2. Release Studies The burst release of drugs from therapeutic formulations is not desirable, especially in the treatment of wound infections [23]. Therefore, incorporating these active principles into an effective delivery system, such as a hydrogel, and releasing them in a controlled manner reduces potential side effects. In the reported literature, there are studies that focused on the controlled release of Thy and α-Tcp from a hydrogel polymer matrix—examples are shown in Table 1: Starting from the premise that pH 5.4 is representative of healthy skin which can range between 5.4 and 8 when the deep layers of the skin are exposed following injuries [31], in this study, the cumulative release of the bioactive compounds was investigated at two different pHs: 5.4 and 7.4 (Figure 3). The initial burst release of Thy_α-Tcp complex from PVA_PEBSA$\frac{25}{75}$_Thy_α-Tcp cryogel, $68.71\%$ at pH 5.4 and $64\%$ at pH 7.4, was observed in the first 30 min, while only $39.71\%$ (pH 5.4) and $39.23\%$ (pH 7.4) of bioactive compounds was released from PVA_PEBSA$\frac{75}{25}$_Thy_α-Tcp cryogel at the same time. The release of a smaller amount of the bioactive substance during the burst release step in the case of the PVA_PEBSA$\frac{75}{25}$_Thy_α-Tcp system correlates with a stronger hydrophobic character of PVA_PEBSA$\frac{75}{25}$ matrix due to the higher ratio of EB. Therefore, the SA carbonyl groups ensure the coupling of bioactive compounds in the polymer matrix, while the hydrophobic alkyl chains of EB constitute the shell of the complex. Consequently, increasing the amount of EB comonomer to $75\%$ in the chemical structure of the copolymer will determine the immobilization of the bioactive compounds in the network and their controlled release in a pulsating regime. In order to study the influence of Thy_α-Tcp complex loading in the polymer matrix on the release capacity of the new bioactive compounds, the system with the equimolecular ratio between monomers was selected to vary the amount of bioactive compound (PEBSA$\frac{50}{50}$_2xThy_α-Tcp and PEBSA$\frac{50}{50}$_Thy_2xα-Tcp). The PVA_PEBSA$\frac{50}{50}$_2xThy_α-Tcp and PVA_PEBSA$\frac{50}{50}$_Thy_2xα-Tcp systems present a “burst” release of Thy and α-Tcp attributed to the diffusion of a double amount of bioactive compounds. According to Figure 3, the minimum and maximum release rates of Thy and α-Tcp from the studied systems over 24 h were $94.83\%$ (pH 7.4) for the PVA_PEBSA$\frac{25}{75}$_Thy_α-Tcp sample and $69.22\%$ (pH 5.4) in the case of the PVA_PEBSA$\frac{75}{25}$_Thy_α-Tcp sample. Since the largest number of granulocytes appear after 12–24 h at the injury site, the cells responsible for the immune response against microbial agents, the risk of infection is higher in the first minutes and hours after wounding. Consequently, the first 24 h after the appearance of the injury is the most important time interval to intervene with a material with antimicrobial properties to prevent infection. Therefore, the rate of drug release from hydrogel dressings is a significant factor in preventing infection [32]. ## 2.3. Antimicrobial Activity The antimicrobial activity screening of the newly synthesized bioactive compounds against S. aureus (Gram-positive bacterial strain), C. albicans (fungal strain), and E. coli (Gram-negative bacterial strain) was determined by disk diffusion assay. All the tested samples presented antimicrobial activity against the selected reference strains (as presented in Table 2 and Figure 4, Figure 5 and Figure 6), results that correlate very well with recently reported antimicrobial assays [13]. The samples proved to be very effective especially against fungal strain represented by C. albicans (up to 38 mm of inhibition zone). Moreover, no significant differences were noticed in terms of antimicrobial activity among systems with different ratios between EB/SA comonomers. A smaller zone of inhibition was noticed in case of PVA_PEBSA$\frac{50}{50}$_Thy_2xα-Tcp system against E. coli (19 mm), while the PVA_PEBSA$\frac{50}{50}$_2xThy_α-Tcp system was more effective against all the tested microbial strains (>27 mm of inhibition zone)—Table 2, the efficiency related to the presence of Thy in a higher ratio. Compared to previous results [13], the addition of α-Tcp to the PVA72000_PEBSA_Thy system did not substantially affect the antimicrobial character of the samples. ## 2.4. Antioxidant Efficiency The interest of researchers in identifying new combinations of biomaterials that exhibit antimicrobial, antioxidant, anti-inflammatory, and healing activities for the treatment of wounds as well as their associated complications is increasing. α-Tcp, the most abundant form of vitamin E, is well known for its strong endogenous antioxidant activity by protecting membrane lipids against oxidation and mechanically stabilizing membranes, improving wound healing and skin regeneration [25]. Supplementing the PVA/PEBSA_Thy system with this antioxidant molecule targets a dual therapy and effect with these two bioactive compounds. The DPPH radical scavenging activity of the synthesized bioactive compounds is illustrated in Figure 7. According to the data obtained in this study, all samples showed antioxidant activity. The most evident activity is observed in the case of the PVA_PEBSA$\frac{25}{75}$_Thy_α-Tcp system ($97.1\%$) containing the PEBSA$\frac{25}{75}$ variant. The carbonyl groups in SA have the ability to accept hydrogen; therefore, increasing the content of SA in the matrix determines a better free radical scavenging activity. At the same time, the cumulative antioxidant efficiency of Thy and α-Tcp in combination with the PEBSA copolymer has a remarkable synergistic effect, between $93.2\%$ and $97.1\%$ (Figure 7). In summary, the mixture of the two bioactive compounds and PEBSA produces new cryogels with potential applications as wound dressings whose therapeutic effects are superior to the effects produced by each individual component. ## 3. Conclusions The encapsulation of hydrophobic molecular compounds into a polymer matrix has emerged as a method to modulate the low solubility in water and a promising approach to preserve their chemical integrity, efficacy, but also their controlled release in a pulsating or continuous regime. Three-dimensional scaffolds based on cryogels are strong candidates and of particular interest for this purpose [33]. In this study, our group used this strategy to develop new cryogels with antimicrobial and antioxidant activity based on PVA, PEBSA, Thy, and α-Tcp obtained by a repeated freeze–thaw process. On the one hand, Thy shows antimicrobial properties on a wide spectrum of bacteria (e.g., S. aureus, Bacillus licheniformis, E. coli, P. vulgaris, C. albicans, etc.) [ 16] and on the other hand, the encapsulation of α-Tcp confers antioxidant properties to the PVA/PEBSA_Thy system, targeting a double therapeutic effect due to the presence of both bioactive molecular agents. The new bioactive compounds prepared by encapsulation of Thy and α-Tcp into the PVA/PEBSA system were investigated from the point of view of the influence of the composition on the network morphology, release profiles, antimicrobial and antioxidant dual activity. SEM micrographs of the PVA/PEBSA polymer matrix illustrated their three-dimensional structure with interconnected pores and numerous meshes, which can ensure the incorporation of small molecular compounds. The morphology of PVA/PEBSA_Thy_α-Tcp systems highlights a relatively uniform distribution of the bioactive substances on the surface of the polymer network. The homogeneous distribution of Thy and α-Tcp into the PEBSA polymer network is due to the amphiphilic character of the copolymer and also to the hydrophobic affinity of the compounds involved in the system. Thy_α-Tcp release profiles from polymeric cryogels confirm the ability of PVA/PEBSA system to encapsulate these bioactive compounds. The lower release rate of Thy and α-Tcp during the burst release step in the case of the PVA_PEBSA$\frac{75}{25}$_Thy_α-Tcp system ($39.71\%$ at pH 5.4 and $39.23\%$ at pH 7.4) correlates with a stronger hydrophobic character of the PVA_PEBSA$\frac{75}{25}$ matrix (due to a higher ratio of EB), determining the immobilization of the bioactive compounds in the network and their controlled release. The new PVA/PEBSA_Thy_α-Tcp systems proved antimicrobial activity against S. aureus (Gram-positive bacterial strain), C. albicans (fungal strain), and E. coli (Gram-negative bacterial strain). The study also underlined the cumulative antioxidant efficiency of Thy and α-Tcp, which in combination with the PEBSA copolymer have a synergistic effect ($97.1\%$). However, this high potential of the investigated systems needs to be extensively evaluated by additional studies, in vitro and in vivo, with focus on possible cytotoxicity concerns, as well as their applicability in the management of skin wounds. The design of multifunctional hydrogel dressings with good adaptability to wounds and with painless on-demand removal property to avoid bacterial colonization remains a major problem to be solved [34,35]. ## 4.1. Materials Ethylene brassylate (EB, 1,4-dioxacycloheptadecane-5,17-dione, C15H26O4, Mw = 270.36 g/mol, purity of $95.0\%$), squaric acid (SA, 3,4-dihydroxy-3-cyclobutene-1,2-dione, H2C4O4, Mw = 114.06 g/mol, purity > $99.0\%$), (+)-α-Tocopherol (α-Tcp), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and 1,4-dioxane (purity ≥ $99.0\%$) were all purchased from Sigma-Aldrich (Darmstadt, Germany), poly(vinyl alcohol) (PVA, Mw = 72,000 g/mol, $98\%$ hydrolyzed) was acquired from Merck (Hohenbrunn, Germany), thymol (Thy, 2-isopropyl-5-methylphenol, C10H14O) was obtained from Alfa Aesar (Kandel, Germany), anhydrous 1-hexanol was purchased from Across-Organics (Geel, Belgium), monosodium phosphate (NaH2PO4 × 2H2O). Disodium phosphate (Na2H2PO4 × 7H2O) was procured from Chemical Company (Iasi, Romania) and ethanol (absolute, ≥$99.8\%$) from Honeywell (Seelze, Germany). All chemicals were used as received without further purification. ## 4.2. Preparation of Cryogels by In Situ Entrapment of Thymol and α-Tocopherol The cryogels were individually obtained by mixing proper ratios of PVA and PEBSA_Thy_α-Tcp complex solutions (Table 3), which were poured into molds and then subjected to three consecutive freeze–thaw cycles, respectively, freezing for 18 h at −20 °C followed by thawing for 8 h at 25 °C (ambient temperature) [13]. Briefly, PEBSA was synthesized as previously described [19] by a polycondensation procedure of EB macrolactone after ring-opening with SA. The new bioactive compound was obtained by the initial preparation of the PEBSA_Thy_α-Tcp complex produced by mixing PEBSA (0.066 g/mL in 1,4-dioxane) with different amounts of Thy and α-Tcp to obtain the desired PEBSA/Thy/α-Tcp mass ratio (either $\frac{1}{1}$/1, $\frac{1}{2}$/1, or $\frac{1}{1}$/2 w/w/w). Then, the obtained complex was mixed with the PVA solution ($4\%$ w/v) in a volumetric ratio of $\frac{2}{1.}$ The synthesized samples were frozen with liquid nitrogen and lyophilized for 24 h at −55 °C (Alpha 1-2LD Plus, Martin Christ, Germany) for further characterization. ## 4.3.1. Morphological Analysis The morphology in the cross-sections of the freeze-dried samples was observed by scanning electron microscopy (SEM Quanta 200, FEI Company, Hillsboro, OR, USA). The instrument operated with secondary electrons at 20 kV in low-vacuum mode, without any coating. Before analysis, the samples were fixed on aluminum stubs with double-adhesive carbon tape. ## 4.3.2. Release Studies To study the in vitro release behavior of the bioactive compounds, each sample was weighed (20 mg) and incubated in 10 mL of PBS, 0.01 M, at a constant temperature of 37 °C for 24 h. The release profiles of the bioactive substances were measured under different conditions using buffer solutions of pH 5.4 and 7.4 to simulate the pH of normal healthy skin and, respectively, the pH of injured skin. At predetermined time intervals, 2 mL of each sample was extracted and analyzed at 283 nm using a UV-VIS spectrophotometer (Jenway 6305, Stone, Staffordshire, United Kingdom). The cumulative release of Thy and α-Tcp was calculated based on the calibration curves determined at the same wavelengths. ## 4.3.3. Antimicrobial Activity The antimicrobial activity of the PVA/PEBSA_Thy_α-Tcp systems was determined using a disk diffusion assay [36,37] against three different reference strains: Gram-positive bacterial strain, Staphylococcus aureus ATCC25923 (S. aureus); Gram-negative bacterial strain, Escherichia coli ATCC25922 (E. coli); and fungal strain, Candida albicans ATCC10231 (C. albicans). All microorganisms were stored at −80 °C in $20\%$ glycerol. The bacterial strains were refreshed on trypticase soy agar (TSA) at 37 °C and the yeast strain was refreshed on Sabouraud dextrose agar (SDA) at 37 °C. Microbial suspensions were prepared with these cultures in sterile solution to obtain turbidity optically comparable to that of 0.5 McFarland standards. Volumes of 0.1 mL from each inoculum were spread onto TSA/SDA plates, and then the sterilized samples of 10 mm and 25 mg each were added. To evaluate the antimicrobial properties, the growth inhibition was measured under standard conditions after 24 h of incubation at 37 °C. All tests were carried out in triplicate for each sample. After incubation, the samples were analyzed with SCAN1200®, version 8.6.10.0 (Interscience, Saint Nom la Brétèche - FRANCE). ## 4.3.4. Antioxidant Efficiency The free radical scavenging activity of the bioactive compounds was evaluated by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay according to the methodology described by Brand-Williams et al. [ 38]. Briefly, 3 mL of ethanol and 20 mg of the sample were added to 0.3 mL of DPPH stock solution 0.5 mM in absolute ethanol. The control solution was prepared by mixing ethanol (3 mL) and DPPH stock solution (0.3 mL). The reaction mixture was incubated in a dark place at room temperature for 30 min. The changes in color, from intense violet to light yellow, were recorded spectrophotometrically at 517 nm (Jenway 6305 UV–VIS Spectrophotometer, Stone, Staffordshire, UK). The percentage of DPPH radical scavenging activity was calculated by the following equation:% DPPH radical scavenging activity=AC− ASAC×100 where Ac is the absorbance of the control DPPH solution and AS is the absorbance of the DPPH solution containing samples; the values reported for each sample represents the mean of three independent measurements. ## 4.3.5. Statistical Analysis All experimental data were performed in triplicate and the results were expressed as mean ± standard error of the mean. 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--- title: Taste Preference-Related Genetic Polymorphisms Modify Alcohol Consumption Behavior of the Hungarian General and Roma Populations authors: - Ali Abbas Mohammad Kurshed - Ferenc Vincze - Péter Pikó - Zsigmond Kósa - János Sándor - Róza Ádány - Judit Diószegi journal: Genes year: 2023 pmcid: PMC10048713 doi: 10.3390/genes14030666 license: CC BY 4.0 --- # Taste Preference-Related Genetic Polymorphisms Modify Alcohol Consumption Behavior of the Hungarian General and Roma Populations ## Abstract Harmful alcohol consumption has been considered a major public health issue globally, with the amounts of alcohol drunk being highest in the WHO European Region including Hungary. Alcohol consumption behaviors are complex human traits influenced by environmental factors and numerous genes. Beyond alcohol metabolization and neurotransmitter gene polymorphisms, taste preference-related genetic variants may also mediate alcohol consumption behaviors. Applying the Alcohol Use Disorders Identification Test (AUDIT) we aimed to elucidate the underlying genetic determinants of alcohol consumption patterns considering taste preference gene polymorphisms (TAS1R3 rs307355, TAS2R38 rs713598, TAS2R19 rs10772420 and CA6 rs2274333) in the Hungarian general (HG) and Roma (HR) populations. Alcohol consumption assessment was available for 410 HG and 387 HR individuals with 405 HG and 364 HR DNA samples being obtained for genotyping. No significant associations were found between TAS1R3 rs307355, TAS2R19 rs10772420, and CA6 rs2274333 polymorphisms and alcohol consumption phenotypes. Significant associations were identified between TAS2R38 rs713598 and the number of standard drinks consumed in the HG sample (genotype GG negatively correlated with the number of standard drinks; coef: −0.136, $$p \leq 0.028$$) and the prevalence of having six or more drinks among Roma (a negative correlation was identified in the recessive model; genotype GG, coef: −0.170, $$p \leq 0.049$$), although, none of these findings passed the Bonferroni-corrected probability criterion ($p \leq 0.05$). Nevertheless, our findings may suggest that alcohol consumption is partially driven by genetically determined taste preferences in our study populations. Further studies are required to strengthen the findings and to understand the drivers of alcohol consumption behavior in more depth. ## 1. Introduction Hazardous drinking is a significant public health problem contributing to the development of more than 200 diseases and injuries [1] and resulting in 1.78 million deaths in 2020 worldwide [2]. In addition, among people 25–49 years of age, alcohol use was found to be the most important risk factor at the global level [3]. Among the many health and public health challenges of the COVID-19 crisis, an increased burden of alcohol consumption evolved during the pandemic situation [4]. Alcohol-related problems do not only arise at the individual level, but harm to others is also considered a substantial problem [1]. Alcohol-related disease burden affects populations disproportionally, the European Region together with Hungary being the most heavily affected [5]. Although alcohol consumption showed a decreasing trend in Hungary [5], in 2019 consumption levels were still above the OECD average and the country was among those nations, which reported consumption over 11 L (calculated for pure alcohol) per capita per year [6]. Furthermore, heavy alcohol use, alcohol use disorder, and dependence are still considered of public health significance [7] (alcohol use disorders: Hungary $21.2\%$ vs. Europe $8.8\%$; dependence: Hungary $9.4\%$ vs. Europe $3.7\%$) [5] and Hungary can be characterized with the highest standardized rates for alcohol-attributable mortality in Europe [8]. Alcohol consumption patterns and related harm vary not only across countries but also within the same country among ethnic groups [9], including the most disadvantaged Roma population of Europe and Hungary [10,11,12,13,14,15,16,17,18,19,20,21,22]. This minority population is mainly accumulated in Central and Eastern Europe [23] with a representation of over $5\%$ of the total population [24] ($8.9\%$ of the total population, 876,000 individuals in 2013 in Hungary [25]) and faces discrimination, several barriers when seeking healthcare services, and poorer health outcomes compared to mainstream populations [26,27,28,29,30,31,32,33]. Alcohol use disorder (AUD) being a complex human trait shows a moderate heritability estimate of 0.49, though familial aggregation may also be due to shared environmental effects [34]. AUD and other alcohol consumption phenotypes are considered distinct but related phenotypes [35] with some overlap of genetic background [36]. These alcohol-related phenotypes can be considered as quantitative traits often measured by varying phenotype assessment methods [35] and are influenced by numerous genetic polymorphisms. The most extensively studied genetic variants regarding AUD and alcohol consumption are involved in the breakdown of alcohol (alcohol and aldehyde dehydrogenase-related genes and variants -ADH and ALDH), especially certain ADH1B and ALDH2 polymorphisms showing the largest effects in Asian populations. Furthermore, polymorphisms of several neurotransmitter-related genes affected by alcohol (i.e., receptors, enzymes, and solute carriers of the cholinergic, dopamine, GABA, serotonin, glutamate, and opioid pathways) have been also subject to several studies [35]. Research suggests that oral sensations evoked by consumed beverages may also determine food and alcohol preferences and intake [37,38]. Although five basic taste qualities (bitter, sweet, sour, salty, umami) and the recently identified fat taste exist, bitter and sweet sensitivity has been found to influence alcohol consumption and preferences, though methodological difficulties are not easy to overcome when summarizing the results. Several studies rely on quinine bitterness as a measure for bitterness and others suggest PROP (6-n-propylthiouracil) taster status to be used for orosensory responsiveness for bitter and also use it as a marker for bitter sensitivity and preference. Several research groups found associations between PROP responsiveness and alcohol consumption behaviors [37,38]. Furthermore, wine/alcohol bitterness was also found to be associated with TAS2R38 genetic variants, which has been widely investigated in relation to bitter and sweet taste preferences [38]. It was also shown that sweet-likers may be at an enhanced risk for the development of alcohol use disorders, which may be in connection with the sugar content of alcoholic beverages associated with the human neural reward system [37]. Although less extensively studied compared to alcohol metabolizing gene polymorphism and genetic variants related to neurotransmitters, whose levels are altered through drinking of alcohol, taste preference-related genetic variants may also influence alcohol consumption patterns. As investigated in the literature, most of the studies focus on TAS2R38 variants rs713598, rs1726866 and rs10246939 [39,40,41,42,43,44,45,46,47,48,49,50,51,52,53] followed by mainly other TAS2R gene [43,45,46,54,55] and gustducin (CA6) variants [45,48]. Although the results related to single nucleotide polymorphisms (SNPs) of taste preference genes and alcohol consumption patterns were found to be conflicting and/or the number of studies in the literature was scarce [56], it may be still hypothesized that genetic polymorphisms related to bitter and sweet taste preferences may mediate alcohol consumption patterns in some way. The number of genetic association studies investigating alcohol consumption behaviors of Roma communities in comparison with the relevant mainstream populations of different countries is also very limited. One study in Hungary found the ADH1B rs1229984 (carrying the ADH1B2 allele) to decrease drinking frequency, furthermore, it was associated with lower odds for having more positive answers on the CAGE screening tool (Cut-down, Annoyed, Guilty, Eye-opener) and also for positive CAGE screening status [57]. In addition, the 272Gln/35Val allele (ADH1C rs1693482/rs698) homozygosity was demonstrated to increase the risk of excessive and problem drinking among men aged 45–64 years [58]. Another study analyzing the distribution and combined effect of alcohol metabolism and neurotransmitter gene polymorphisms in the general Hungarian and Roma populations found no over-representation of genetic alterations predisposing to alcohol dependence and lower genetic risk scores in the minority population [59]. Furthermore, Hubáček et al. identified ADH1B rs1229984 genotype frequencies in the Czech Roma population corresponding with frequencies of North India/Central Asia [60]. On the other hand, alcohol consumption phenotypes in relation to taste preference-related genetic variants in Roma populations in comparison with majority populations have not been investigated before [56]. The majority of genetic association studies investigating taste preference gene polymorphisms focused on drinking frequency and/or quantity [56]. Among these, the only study, which characterized alcohol consumption with the Alcohol Use Disorders Identification Test (AUDIT) used the first three questions of the questionnaire [46]. In our past work we characterized the alcohol consumption patterns of HG and HR populations and found no differences in risky alcohol use based on the AUDIT total scores between Roma and non-Roma [61]. Therefore, our present work aimed to elucidate the underlying genetic determinants of alcohol consumption patterns considering taste preference genetic variants in the Hungarian* general (HG) and Roma (HR) populations using the first three questions of the AUDIT. The potential genetically determined taste-driven preferences behind Hungary’s alcohol consumption levels should be considered when targeting alcohol-related harm considering also the possible ethnic-specific effect of these variants. ## 2.1. Study Design Data collection was implemented in mid-2018 within the framework of a three pillar-based) comparative health survey involving physical examinations, blood sample collection, and questionnaire surveys [62]. Sampling of the study populations was based on the pre-set principle that if someone was unavailable to be reached, it was then allowed to include another individual, but it was not permissible to recruit another subject in place of someone, who refused to participate in the study. It was planned to include 500 people in both study samples. Practice nurses took the questionnaires of the survey in the Hungarian general population, meanwhile among Roma, this work was assigned to Roma university students. Blood samples were taken in General Practitioners’ (GPs) offices for subsequent genetic analysis. ## 2.1.1. Sample Representative of the Hungarian General Population of Northeast Hungary The Hungarian reference sample was obtained from a population-based registry. This program, called the General Practitioners’ Morbidity Sentinel Stations Programme (GPMSSP) has been operating since 1998 to monitor the incidence and prevalence of chronic non-communicable diseases of great public health importance. The source population of this registry encompasses all individuals belonging to the practices of the 59 general practitioners (GPs) participating in the program [63,64]. Individuals in our study were randomly drawn from the GPMSSP, who were 20–64 years of age, not institutional residents and were registered by the participating primary care providers of Borsod-Abaúj-Zemplén and Szabolcs-Szatmár-Bereg counties of the northeastern part of the country. Based on the study design 25 subjects from each 20 randomly selected GP practices were to be involved in our research. Due to the refusal of two GPs, 450 subjects from the practices of the remaining [18] GPs were available in the final sample. Health behavior surveys were conducted during a health visit in the GPs’ practices by practice nurses. ## 2.1.2. Sample Representative of Hungarian Roma of Northeast Hungary Living in Segregated Colonies Participants of Roma segregated colonies from the same two counties of Northeast Hungary (Hajdú-Bihar and Szabolcs-Szamár-Bereg counties) were enrolled by a stratified multistep random method. Prior to this research, during a previously conducted environmental survey, segregated colonies, where the population size exceeded 100 individuals, were identified. The ethnicity of inhabitants in this investigation was confirmed by self-declaration [65]. After the validation of the colony registry database, 20 colonies were randomly chosen, and subsequently from each colony 25 households were randomly drawn. One person aged 20–64 years was enrolled by using a random table from each household yielding 500 subjects in the sample. Interviews were delivered by Roma university students, who had previously received appropriate training. ## 2.2. Alcohol Consumption Behavior Assessment Alcohol consumption was assessed with the AUDIT questionnaire. The impact of SNPs in taste preference genes was evaluated on responses to the first three questions from this screening tool: “How often do you have a drink containing alcohol?”; “ How many drinks containing alcohol do you have on a typical day when you are drinking?”; and “How often do you have six or more drinks on one occasion?” [ 46]. The AUDIT was provided in an interview version. ## 2.3. Selection of Single Nucleotide Polymorphisms Systematic literature search was carried out in order to identify the most relevant single nucleotide polymorphisms (SNPs) related to taste preference genes, which may influence alcohol consumption behavior [56]. Based on this search, those polymorphisms were selected to include in this study, whose effect had been extensively studied in relation to bitter and sweet taste preference/perception [38] and may also be relevant when investigating alcohol consumption [56]. Altogether four SNPs were selected: TAS1R3 rs307355, TAS2R38 rs713598, TAS2R19 rs10772420, and CA6 rs2274333. The effect of these variants on alcohol consumption behaviors and taste-related phenotypes is summarized in Table 1. ## 2.4. DNA Preparation and Genotype Assessment DNA isolation was performed using the MagNA Pure LC DNA Isolation Kit—Large Volume (Roche Diagnostics, Mannheim, Germany) following the manufacturer’s instructions, for which 500-μL aliquots of EDTA-anticoagulated blood samples were prepared. Extracted DNA samples were eluted in 200 μL MagNA Pure LC DNA Isolation Kit-Large Volume Elution Buffer. The Mutation Analysis Core Facility (MAF) of Clinical Research Center, Karolinska University Hospital (Stockholm, Sweden) provided the genotyping of SNPs of interest (and quality control) applying the Mass Array platform with iPLEX Gold Chemistry [104]. Successful genotyping rate exceeded 98 percent. ## 2.5. Statistical Analysis Data analysis was carried out using the STATA 10.0 Statistical software (StataCorp LP, College Station, TX, USA). Comparison of sociodemographic characteristics and alcohol consumption frequencies were evaluated by chi-square and Fisher’s exact tests. Hardy–Weinberg equilibrium (HWE) was estimated using “hwsnp” [105] and allele frequencies by “genhw” [106] function in STATA. To test the significance of differences in the allele and genotype frequencies between the two samples the chi-square test was applied. The association between the first three questions of the AUDIT questionnaire and selected taste preference genetic polymorphisms was conducted by using STATA’s “qtlsnp” command [105,107] following dominant and recessive models, which were defined according to minor alleles (covariates: gender, age, marital status) in HG and HR populations separately yielding nominal p-values. Nominally significant p-values (<0.05) of the initial analyses were Bonferroni-corrected as well, since each SNP was tested for multiple associations in the two sample populations, in which the nominal p-values were multiplied by the total number of tests performed. Aggregate effect of genetic polymorphisms on alcohol consumption phenotypes was analyzed by summing the number of minor alleles of all four polymorphisms, i.e., calculating the unweighted genetic risk score. ## 3. Results Alcohol consumption assessment was available for 410 HG and 387 HR individuals, and 405 HG and 364 HR DNA samples were available for genotyping, respectively. No significant differences were found regarding the mean age of the two study populations (HG: 44.3 ± 12.3 years, HR: 42.8 ± 12.1 years, $$p \leq 0.075$$). The proportion of men was significantly lower among Roma than in the reference general population sample (0.26 vs. 0.44, $p \leq 0.001$). Being Roma was associated with lower educational attainment, higher unemployment rate, and less favorable self-perceived financial status ($p \leq 0.001$) but not with marital status ($$p \leq 0.240$$) according to the chi-square test. Further details on study population characteristics are summarized in Table S1. Analysis of drinking categories of the two populations according to the 1st three questions of the AUDIT questionnaire indicate that Roma consume alcohol less frequently (the crude frequency of 2–3 times a week or more was significantly lower in the HR sample ($5.47\%$), than in the general one ($12.75\%$)), but no other differences were observed (Table S2). ## 3.1. Allele and Genotype Comparisons between the Study Populations Selected SNPs did not deviate significantly from the Hardy–Weinberg equilibrium in our study populations (Table S3). The genotype and allele frequencies (Table 2 and Table S4) did not show significant differences ($p \leq 0.05$) when comparing the two study samples. ## 3.2. Association of SNPs with Alcohol Consumption Phenotypes In our present study we could not identify significant associations between TAS1R3 rs307355, TAS2R19 rs10772420, and CA6 rs2274333 polymorphisms and the alcohol consumption phenotypes analyzed. In the initial analyses significant associations were found between TAS2R38 rs713598 and the number of standard drinks containing alcohol consumed in the HG sample. Among Roma, TAS2R38 rs713598 predicted the prevalence of having six or more drinks on one occasion. However, none of these findings passed the Bonferroni-corrected probability criterion. All results of the association analyses are presented in Table S5. Significant associations of the initial analyses are depicted in more detail in Table 3. ## 3.2.1. Sample Representative of the Hungarian General Population The nonsynonymous coding TAS2R38 (rs713598, Ala49Pro) SNP was observed to influence the number of standard drinks containing alcohol in the recessive model. Genotype GG negatively correlated with the number of standard drinks (coef: −0.136, $$p \leq 0.028$$, Table 3). However, after correcting for multiple comparisons applying the Bonferroni method, this result did not remain significant. ## 3.2.2. Sample Representative of Hungarian Roma Living in Segregated Colonies A significant association was identified between the variant rs713598 of TAS2R38 and the prevalence of having six or more drinks on one occasion. Similar to the HG sample regarding the number of standard drinks, a negative correlation was identified in the recessive model (genotype GG, coef: −0.170, $$p \leq 0.049$$, Table 3) albeit, this association did not persist after the Bonferroni correction. ## 3.3. Aggregated Effect of SNPs on Alcohol Consumption Phenotypes Summation of the number of minor alleles of the four polymorphisms included in our study did not show any significant association ($p \leq 0.05$) with either of the alcohol consumption phenotypes in either of the study samples. ## 4. Discussion This study aimed to explore possible associations of the most relevant taste preference-related genetic variants with alcohol consumption behavior in the Hungarian general and Roma populations. To our knowledge this is the first research investigating the effect of these genetic variants on drinking patterns in the Roma population in comparison with the mainstream population. Our results indicate TAS2R38 rs713598 have an impact on two different quantitative measures of alcohol consumption (number of drinks consumed and frequency of heavy drinking, respectively) in the HG and HR groups. This variant is the most extensively studied when considering bitter and even sweet taste preferences [38]. The variant rs713598 is one of the three functional variants located at the TAS2R38 locus (rs713598, rs1726866, rs10246939) determining certain bitter-tasting phenotypes. Regarding the PROP supertaster–taster–non-taster categories, by location, rs713598 (P/A) is the first one, rs1726866 (A/V) the second, and rs1024693 (V/I) the third. According to this, PAV (proline–alanine–valine) homozygotes can be characterized as tasters, which is also considered the dominant haplotype. AVI (alanine–valine–isoleucine) homozygotes are considered as insensitive (non-tasters) when considering the ability to taste such bitter substances. Heterozygotes possess moderate sensitivity to PROP and PTC (phenylthiocarbamide). It was demonstrated that rs713598 holds the greatest impact on bitter taste signal transduction, while rs1726866 holds less prominent effects, and rs10246939 polymorphism has eventually no detectable [108,109] effect at all. When searching the literature regarding the association of taste preference-related gene polymorphisms and alcohol consumption phenotypes, similar to bitter taste perception and preference, TAS2R38 variants (rs713598, rs1726866, rs10246939) have been mainly investigated. However, the findings on the effect of these variants are contradictory [56]. Some of the findings, but not all, may suggest that individuals with taster genotypes/haplotypes consume less alcohol, however phenotype assessment methods and study populations vary widely among studies [56], although several studies failed to find an association between these genetic variants and alcohol consumption phenotypes [56]. In contrast to the studies, where individuals with higher bitterness perception are less likely to consume alcohol (the only study using the first three questions from AUDIT as measures for alcohol consumption, also found the major “C” allele to decrease alcohol consumption, although not in a general population sample but in a head and neck cancer cohort), our results suggest negative correlations (at the nominal significance level) in the recessive model (defined according to minor allele), indicating that individuals with genotype GG (non-tasters) consume less alcohol. In line with our results, one study reported similar findings, i.e., tasters consuming more alcohol, while also indicating this was not necessarily inconsistent with other research findings. This research suggests that there could be other factors promoting alcohol consumption among subjects with an enhanced ability to taste bitterness frequency [45], e.g., wine consumption may be associated with increased PROP bitterness perception [110,111]. Similar factors may explain our findings as well, since AUDIT results do not differentiate between drinks having different taste profiles. The aforementioned findings showed an ethnicity-dependent pattern in our study in some way. In the HR population this variant was associated with the number of standard drinks consumed and among Roma with the prevalence of having six or more drinks on one occasion. Both questions refer to quantitative measures of alcohol consumption although from a slightly different approach. One potential reason for this finding could be that taste perception and preference may influence alcohol consumption differently in these populations in some respects. Furthermore, it is also possible that the taste profile of alcohol consumed by subjects in the two study samples also differed. From the genetic point of view other possible explanations also exist. According to the literature, ethnic specific findings were observed in several genetic association studies [112,113,114,115,116,117,118,119,120,121,122], even when considering taste preference-related genetic polymorphisms [123]. The reasons behind this phenomenon may be related to some ethnic variation in linkage disequilibrium (LD) [116,118], where the effect of genetic polymorphisms under investigation could be linked to other real predisposing genetic variants showing different strengths for associations across ethnic groups [119]. So, the effect of the investigated genetic variants might be diluted or masked by other sometimes even yet unidentified susceptibility genes, actually being responsible for the development of phenotypes of interest [114]. It cannot be excluded also that certain alleles act differently in certain populations [118,119]. Considering different alcohol consumption-related phenotypes it should be noted that these various phenotypes may encompass different genetic backgrounds. In our present study no significant associations were observed for TAS1R3 rs307355, CA6 rs2274333, and TAS2R19 rs10772420 polymorphisms. The rs307355 polymorphism of TAS1R3 was found previously to influence taste sensitivity to sucrose (“T” alleles indicating reduced sensitivity) [38] and one study identified this variant as predicting some alcohol consumption-related phenotypes [43]. This variant is located in the 5′UTR (untranslated) region of TAS1R3, and leads to a cytosine to thymine substitution, potentially influencing the function of the regulatory element and gene transcription [66], and contributes to alterations in sweet and alcohol perception, although this relationship was not verified in our present study. The research on carbonic anhydrase VI (also called gustin), a zinc-metalloprotein, which is secreted by the salivary glands [124,125] also suggests it to be a trophic factor for the development and growth of taste buds [126]. The variant itself leads to an amino acid substitution (Ser90Gly) and is supposed to influence also the formation and function of fungiform papillae on the anterior tongue surface [127], potentially having an effect on PROP sensitivity. Although investigated by several research groups, the CA6 rs2274333 yielded equivocal findings considering bitter taste preference [38] showed no correlation with alcohol consumption [56], which is in line with our findings. The TAS2R19 rs10772420 variant, which codes for an arginine-to-cysteine substitution at amino acid 299, was previously shown to be associated with the preference, intensity, detection threshold of bitter tasting compounds and preference of grape-fruit juice [38], although the possibility was raised that these findings may be due to strong LD between TAS2R19 and nearby TAS2R genes [100,128]. Supporting our findings, none of the studies investigating the relationship of this variant on various alcohol consumption phenotypes could identify any association with consumption patterns [56]. Due to the limited findings in the literature, and the different measures of alcohol consumption used [35], additional investigations should be carried out to further explore the effect of these polymorphisms on alcohol consumption behaviors. Several potential limitations need to be recognized when interpreting the results of our study. Human subjects perceive alcoholic beverages as a combination of sweet and bitter tastes [129] with certain beverages having different taste profiles [130], although the main taste modality of alcohol consumed has not been investigated in our research, which may impact our findings. Furthermore, alcohol consumption is a subject liable to underreporting [131] even if it is estimated by using the AUDIT tool [132]. In addition, the effectiveness of the AUDIT tool may vary in some ethnic groups/minorities [133]. Research also suggests that Roma people may be more prone to please research investigators more than individuals of mainstream populations, potentially influencing questionnaire results [134,135,136]. Furthermore, the AUDIT was provided in an interview version in our study. Roma people are already subject to negative stereotypes, which may influence their answers on alcohol consumption, and they may rather answer questions in a manner that will be not viewed unfavorably by others. The potential difficulties in understanding the questions of the AUDIT, were addressed by having Roma students as interviewers. In addition, the AUDIT does not provide a comprehensive view on lifetime alcohol use and problems, and no information is available on the underlying causes behind abstinence, which could even be attributed to self-decision due to alcohol dependence [46]. Other possible limitations of our study could be attributed to characteristics of the Roma study population, which was not representative of the country’s overall Roma population, only representative of Roma in Northeast Hungary (where Roma are mainly accumulated), who live in segregated colonies. Ethnicity was based on self-declaration and since some Roma may be unwilling to self-report Roma ethnicity [137], the HG sample may therefore have also included some Roma individuals. In addition, only subjects aged 20–64 years were enrolled in our study. Individuals older than 64 were not considered, since in our previous Roma surveys the representation of persons aged 65 years and older was as low as 3–$4\%$ [33,138,139]. Furthermore, the representation of women among Roma was higher than that of the non-Roma population. This is in line with our previous surveys in similar settings of the country [139] and also to a cross-sectional study conducted in Slovakia [140]. This may be in connection with the time of data collection, which occurred during the day, when in most Roma households in this region (Northeast Hungary) women resided at home, while men travelled for public work [62]. ## 5. Conclusions Alcohol consumption is a complex trait influenced by numerous genes. This research is the first comparative study investigating the potential associations of taste preference-related genetic polymorphisms with drinking behaviors of the Hungarian general and Roma populations. We observed some ethnicity-specific associations between genetic variations in the TAS2R38 receptor genes and certain aspects of alcohol consumption. Nevertheless, our initial results did not remain significant after correcting for multiple testing, but still our findings should be considered interesting [46,141,142] in raising the idea that alcohol consumption may be partially driven by genetically determined taste preferences in our study populations. 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--- title: Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image authors: - Yuanfeng Chen - Li Liu - Yuan Rao - Xiaodan Zhang - Wu Zhang - Xiu Jin journal: Foods year: 2023 pmcid: PMC10048714 doi: 10.3390/foods12061178 license: CC BY 4.0 --- # Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image ## Abstract The “Dangshan” pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies. The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of “Dangshan” pears. This paper employs the merging of NIRS features and image features for the detection of “Dangshan” pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the “Dangshan” pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures. ## 1. Introduction Pear (Pyrus spp.) is a widely grown and consumed fruit [1,2]. There are many distinct types of pears, and they have varied properties [3,4]. Among them, the “Dangshan” pear is one of the most popular types in China [5]. The “Dangshan” pear is popular because it has the advantage of having thin and juicy skin. However, fruit chaffing disease of the “Dangshan” pear has caused large losses to fruit farmers, and this disease often occurs in “Dangshan” pear farming. Woolliness response disease is a physiological disease [6]. This disease is connected to a lack of nutrients or a reduction in root uptake in “Dangshan” pears, in which the deficient nutrients are largely boron, calcium, and water. In the absence of calcium, iron, and boron or in the presence of reduced root uptake, the fruit is encouraged to ripen quicker, and fruit hardness is reduced. This results in the development of woolliness response disease in the fruit. To prevent the occurrence of woolliness response disease in “Dangshan” pears, effective and accurate detection methods have been researched, and strategies have been explored mainly around the causes of woolliness response disease. The traditional detection of mineral nutrients is largely based on laboratory physicochemical analysis, including inductively coupled plasma–mass spectrometry (ICP–MS), atomic absorption spectrometry, and UV-VIS spectrophotometry measures [7,8,9]. Although the results of these approaches are quite accurate, they have the disadvantages of destructive sampling and being time-consuming, labor-intensive, and costly, and these characteristics bring numerous restrictions to the study of mineral nutrition in pear fruit. Among the numerous non-destructive testing techniques, near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) are commonly utilized. Several studies have employed NIRS and CVS techniques to diagnose diseases in many agricultural products [10]. For example, YanYu et al. [ 11] established a tool for monitoring fruit quality from a combination of NIRS and chemical models and then utilized this tool to explore the construction of a generic model that, among other things, predicts the Soluble Solids Content (SSC) of thin-skinned fruits with similar physicochemical properties. Lei-Ming Yuan et al. [ 12] employed vis-NIRS technology paired with a bias fusion modeling strategy for the noninvasive assessment of “Yunhe” pears, which improved the loss of spectral information in the optimized PLS model. Cavaco et al. [ 13] proposed a segmented partial least squares (PLS) prediction model for the hardness of “Rocha” pears in combination with a vis-NIRS segmentation model, which was used to predict the hardness of the fruit during ripening under shelf-life conditions. Pereira et al. [ 14] devised a color-image-processing method that employed a combination of digital photography and random forest to anticipate the ripeness of papaya fruits, evaluating them on the basis of flesh firmness. Zhu et al. combined process characteristics and image information to evaluate the quality of tea leaves [15]. Shumian Chen et al. developed a machine vision system for the detection of defective rice grains [16]. Near-infrared spectroscopy (NIRS) detection methods have the advantage of being nondestructive, convenient, environmentally friendly, and safe [17]. The method’s coverage of molecular absorption covers frequency combinations and double-frequency absorption of hydrogen-containing groups or other chemical bonds in many organic compounds, mainly C-H, N-H, S-H, O-H, and others. Therefore, the spectral profile produced using NIR spectroscopy may then be utilized to better reflect information on the organic matter of the hydrogen-containing groups in the sample under test and information on the composition of several other biochemical structures [18]. Machine vision technology has the advantages of being nondestructive, rapid, efficient, and objective [19,20]. This technique uses optical systems and image-processing equipment to replicate human vision. The technology extracts information from the acquired target image and processes it to obtain the information required for the object to be detected and then analyzes it. Therefore, the image information produced by machine vision technology can properly and objectively depict the appearance features of “Dangshan” pears, and this advantage plays a significant part in the woolliness response disease of “Dangshan” pears. These studies detected diseases based on only a single aspect of NIRS or CVS. However, the NIRS and CVS approaches can only acquire the main components and appearance of the samples separately and cannot obtain complete information on the quality of “Dangshan” pears. Therefore, if NIRS features or CVS features are fused for disease diagnosis, this may improve the accuracy of diagnosing woolliness response disease. Feature-level fusion procedures make it possible to study sample features fully, and several studies have validated the use of feature-level fusion [21,22]. At the same time, this research used several forms of data-fusion algorithms to merge information from multiple detection techniques to produce better sample characterization and enhanced identification. Therefore, to effectively diagnose the woolliness response disease of “Dangshan” pears, researchers have concentrated on multi-technology integration to address the drawbacks of utilizing a single form of technology. Studies have performed data fusion by merging data from numerous different sources. A hybrid method was devised by Miao et al. to distinguish nine species of ginseng [23]. Fun-Li Xu et al. [ 24] used CVS and HSI techniques to accomplish quick, nondestructive detection of frostbite in frozen salmon fillets. Currently, in integrated spectroscopic and image techniques, spectral detection methods are generally used in hyperspectral techniques. Hyperspectral imaging (HSI) provides images in which each pixel contains spectral information to reflect the chemical properties of a particular region [25]. However, we discovered a few studies integrating NIRS and CVS for disease detection in fruit. Hyperspectral image techniques have disadvantages such as high cost and limited accessibility, which have become a not insignificant impediment in the development of disease diagnostic approaches for “Dangshan” pears. NIRS technology has the advantages of low cost and ease of field use [26,27]. Therefore, this work combines the application of NIRS and CVS systems with a feature-level fusion strategy to explore a method that can increase the accuracy of illness identification in “Dangshan” pears. This study created a nondestructive, objective, and accurate approach for the diagnosis of chaffing diseases in “Dangshan” pears. The method incorporates a combination of an NIRS system and a CVS. We extracted the NIRS characteristics by modeling the machine-learning approach. CVS features are retrieved by employing a convolutional neural network. We then compare the classification results obtained using only NIRS features, only CVS features, and a fusion of NIRS and CVS features to determine the best method for “Dangshan” pear woolliness response disease identification and to explore and analyze the effects of different-layer fusion modeling for the CVS feature model. This study intends to provide a theoretical basis and innovative concepts for developing innovative technologies for the woolliness response disease of “Dangshan” pear. The remainder of this study is separated into three sections. In Section 2, the spectral and image-data-gathering methods for “Dangshan” pears and the accompanying machine-learning methods, deep-learning methods, and feature-level fusion approaches are discussed. Section 3 covers not only the performance of the single-feature model and the feature-level fusion model but also evaluates and analyses the fusion effects of different layers of the convolutional neural network. Section 4 discusses the conclusions of this investigation. ## 2.1. Samples In this investigation, 480 samples of “Dangshan” pear trees were collected and classified for this area in Yeji District, Liuan City, Anhui Province. The plants in this area have the advantages of uniform growth and robustness. However, the unpredictable weather in the region has led to frequent natural disasters such as floods and droughts in the area. As a result, local agricultural production has also suffered greatly. In early September 2022, some of the “Dangshan” pear trees in the test site developed obvious chaffing symptoms and were called sick trees. The other section of the test location exhibited no evidence of chaffing, and the fruit was in normal condition; thus, these trees were called healthy trees. One diseased tree and one healthy tree of “Dangshan” pear were meticulously selected in the test site, 240 infected fruits were picked from the diseased tree, and subsequently, 240 normal fruits were picked from the healthy tree. As shown in Figure 1, the main symptoms of the disease are dark yellow fruit color, reduced fruit hardness, and symptoms spreading inwards from near the skin. After completing the picking, the fruit was carried back to the laboratory to be utilized as test samples. The surface of the pear fruit was washed and wiped clean before being numbered for use. ## 2.2. Data Acquisition Instruments As shown in Figure 2a, the spectrum data were acquired using a handheld miniature NIR spectrometer in the spectral range of 900–1700 nm with 228 bands, a spectral resolution of 3.89 nm, and a signal-to-noise ratio (SNR) of 5000:1. The product model of this instrument was the NIR-S-G1, which was created by Shenzhen Puyan Network Technology Co., Ltd. (Shenzhen, China). Before collecting the spectral data, the instrument needed to be connected via Bluetooth to the app “Instagram” on a mobile phone. Before each “Dangshan” pear measurement, the spectrometer was calibrated with a standard white and dark reference. The instrument was placed close to the calibration whiteboard, and the light emitted shone on the whiteboard and was reflected into the spectrometer, which captured and recorded the brightness value (W) of the whiteboard; we turned off the emitted light of the instrument and recorded the brightness value (B) on the blackboard. After calibration, the instrument was utilized to gather spectral data on the surface of the pears, with the instrument’s light source window close to the “Dangshan” pear sample to obtain the reflected light being recorded as the luminance value (R) on the pear surface. The spectral reflectance of the sample was determined using Equation [1]. [ 1]R=(I−B)(W−B)×$100\%$ As shown in Figure 2b, the equipment used to collect image data in this test was an “EOS 90D” digital camera manufactured by Canon Inc. (Tokyo, Japan). Canon is a leading Japanese integrated group that produces imaging and information products worldwide. The camera is an autofocus/auto-exposure single-lens reflex digital camera with a built-in flash. It contains a total of 34.4 million pixels and has a CMOS sensor type and a maximum resolution of 6000 × 4000. The spectral-data-gathering method is presented in Figure 3a. Before the spectral data was collected, an ellipse was defined with a pencil at the equator on the surface of the typical fruit rind; the short axis of this ellipse was approximately 3 cm, and the long axis was approximately 5 cm. The area within this ellipse was utilized as the range for spectral-data acquisition; the ends of the central axis of the area and the center section were used as the range for spectral acquisition. The scanning window on the front of the compact handheld spectrometer was placed immediately within the specified range, followed by five scans in each sample area. At the end of the scan, each data file was named according to the sample number. Finally, the reflectance spectral data averaged over the five scans of each area were used as the original modeling spectral data. The image-data-acquisition method is presented in Figure 3b. The procedure of image-data gathering was to position the “Dangshan” pear on a white background under natural light; the camera was kept at the same height as the “Dangshan” pear and approximately 30 cm distant from the front of the “Dangshan” pear. The image resolution was set to RAW (6960 × 4640), approximately 32.3 megapixels; the focus mode was single autofocus, the scene mode was macro, the sensitivity was still-image shooting, the parameters were established, and the shooting began. The normal fruit was photographed as a normal sample, and the infected fruit was photographed as a diseased sample. ## 2.3. Machine-Learning Methods for Near-Infrared Spectroscopy Partial Least Squares Discriminant Analysis (PLSDA), support vector machines, random forests, and Boost-like approaches are utilized mainly in supervised machine-learning models for NIR spectroscopy. Among these, PLSDA technique is a chemometric tool that utilizes a PLS algorithm in modeling differences between defined sample classes, as such, allowing for the discrimination of samples within these groups [28]. SVM is a frequently used supervised classification-learning algorithm [29]. Its basic idea is to identify the most recognizable hyperplane by maximizing the edge distance between the nearest points in each class. RF (random forest) is an algorithm that combines Breiman’s bagging idea [30] with Ho’s random subspace approach [31]. They are generated based on decision trees that are trained using segments of the dataset and randomly selected segments of the feature set. AdaBoost (short for adaptive boosting) is based on the premise that a set of weak classifiers can yield a strong classifier. In this scenario, the weak classifiers are combined linearly but modified by the coefficients gained during training. The selection of weak classifiers focuses on examples that are more challenging to categorize. In this repeating procedure, the coefficients of the weak classifiers correspond to the classifier errors on the dataset. The coefficients of the weak classifiers with the fewest errors are enhanced. Strong classifiers group all these weak classifiers based on significance coefficients. XGBoost is a boosting method that transforms weak learners into strong learners [32]. As a boosted tree model, XGBoost is a powerful classifier consisting of many single tree models. In addition, at the model level, one of the generally used modeling approaches is the multilayer perceptron (MLP), an artificial neural network model (ANN) consisting of many layers for which the network structure might be a feed-forward or feedback network structure. The model comprises an input layer, a hidden layer, and an output layer [33]. The MLP with one hidden layer is the simplest model with a network structure. The structure of an MLP involves the number of layers, the number of neurons, the transfer function of each neuron, and how the layers are coupled, depending on the type of problem [34]. Each neuron of an MLP has its own weight. A neuron can have any number of inputs from 1 to n, where n (an integer) is the total number of inputs. The inputs are denoted as x1,x2,x3…xn; the corresponding weights are denoted as w1,w2,w3…wn; and the output is represented as a = x1w1,x2w2,x3w3…xnwn. The simple structure of node I in the MLP with k inputs from nodes {1,2,…k} with k input arcs from nodes {1,2,…k}, for which the associated weights and input values are w1i…wki, and x1i…xki. The dashed lines show the values propagated through the network. Predictions are shown by the yi values. Different activation functions (fi) are applied to the input values to flow through the network. In the later stage of feature fusion, we designed the MLP with simply an input layer and a hidden layer for NIR spectral feature extraction, with weights initialized using a uniform distribution. The output layer utilizes the sigmoid activation function, and the remaining layers use the ReLU activation function. ## 2.4. Deep Neural Network Methods CNNs have a vital role and significance in the field of computer vision. The structure of a convolutional neural network (CNN) has a standard structure consisting of alternating convolutional layers and a pooling layer following the convolutional layers. Then, based on the standard structure, the CNN performs fully connected classification [35]. In this scenario, the fully connected classification is performed by a fully connected output layer and a SoftMax classifier. The fully connected output layer is equivalent to a simple logistic regression using the equivalent of a standard MLP but without any hidden layers. SoftMax classifiers are typically trained using a backpropagation algorithm to find the weights and biases that minimize a certain loss function and then to map any input as close as possible to the target output. The two-dimensional convolution operation of a CNN is formulated as follows:[2]Xkl=f∑i∈MjXi−1∗kijl+bjl where Xkl is the kth feature map of layer l, Mj represents the input map part, kijl is the learnable kernel, f(⋅) represents the activation function, and bjl is the bias term of the kth feature map of layer l. Since this study will examine and analyze fusion models for distinct layers, we will execute image-feature extraction for the given layers as input to the feature-fusion process. CNNs are commonly used due to their excellent effectiveness. Some of the popular architectures studied in this study are discussed below. VGGNet is a deep architecture that can extract features at low spatial resolution [36].There are two variants of VGGNet: VGG16 (with 16 weight layers) and VGG19 (with 19 weight layers). The VGG16 architecture contains 16 weight layers. It also contains five convolution blocks and two fully connected layers. VGG19 differs from the VGG-16 model in that it is planned to start with five convolution blocks followed by three fully connected layers. Similar to VGG16, these convolutional layers utilize a 3-kernel with a step size of 1 and a padding of 1; therefore, the dimensionality of the feature mapping will be the same as that of the previous layers. A deep residual network named ResNet was proposed by He et al. [ 37]. ResNet consists of sequentially ordered convolutional, pooling, activation, and fully connected layers. ResNet includes multiple architectures, including ResNet 50 and ResNet 101. ResNet 50 comprises a backbone for input, four stages, and an output layer [38]. The ResNet101 network differs somewhat from ResNet50 in that there are 104 convolutional layers in the ResNet101 network, and there are 33 block layers alongside them. In addition, the output of the previous block is used as the residual connections directly above these 29 blocks. To receive the input from the other blocks, these residual connections are exploited at the termination of each block using the starting value of the sigma operator. The ResNet101 network is organized into six basic sections, namely, the input module, four different structured blocks, and the output block. The building blocks of the network model are essentially residual block structures. Each layer of ResNet101 uses the ReLU activation function and incorporates batch normalization units to improve the adaptability of the model. The ADAM optimizer was also employed to improve the accuracy of the network recognition. In the later stage of fusion modeling of different layers, we divided ResNet101 into five layers (Layer 1, Layer 2, Layer 3, Layer 4, and Layer 5) for image-feature extraction of different layers. The parameters and 2D output dimensions of different layers of the ResNet101 network are provided in Table 1. The Xception architecture, which was developed by Francois [39], is a linear stack of deeply separable convolutional layers with residual connections [40]. It is an upgraded model of InceptionV3. One of the building blocks of the Inception network is the Inception module, which collects parallel routes with differing perceptual field widths and actions in a feature-mapping stack [41]. After the amazing success of the Inception network, GoogLeNet (Inception V1) was changed into InceptionV2, Inception V3, and Inception-ResNet. Xception consists of 36 convolutional layers separated into three primary flows, namely, Entry flow, Middle flow, and Exit flow. Images from the training set are first transmitted to the Entry flow, which builds feature maps. These feature maps are then further input into the Middle flow (repeated eight times). Finally, the feature maps of the Exit flow create 2048-dimensional vectors. Xception substitutes the typical initial module with a depth-separable convolution (separate spatial convolution on each input channel), followed by point-specific convolution (1 × 1 convolution) [39]. In a later stage of fusion modeling of distinct layers, we divided Xception into three layers (Entry flow, Middle flow, and Exit flow) for image feature extraction in different layers. The architecture of the many layers of Xception is represented in Figure 4. DenseNet201 is a convolutional neural network with a depth of 201 layers [42]. Each of these layers is connected using a feed-forward technique. The feature maps of all previous layers are utilized as input to each layer, and their feature maps are used as input in all subsequent layers. This architecture has a dense connectivity structure, hence the name dense convolutional neural network. DenseNet201 increases feature propagation, stimulates feature reuse, and significantly reduces the number of parameters [43]. ## 2.5. Near-Infrared Spectroscopy and Visual Image Feature Fusion Methods In this work, a feature-level fusion strategy was investigated, such that we applied spectral and image feature depth fusion methods for spectral and shape characteristics to diagnose woolliness response disease in “Dangshan” pears. A description of the spectral and image feature depth fusion method is presented in Figure 5. The extraction of spectral features is divided into two steps. [ 1] The procedure of spectral data acquisition is the average of reflectance spectral data from five scans of each region of the sample as the original modeled spectral data. [ 2] The input layer of the MLP takes the spectral data as input and performs the extraction of NIR spectral features as input features for feature-level fusion. The image features are extracted in four steps. [ 1] The image acquisition process is as follows. The image is taken by placing the camera approximately 30 cm in front and keeping it on a horizontal plane with the fruit, and the image is used as the raw image data. [ 2] The input layer of the CNN is connected to the image sample as the input signal. [ 3] Local features are extracted via convolutional operations of local perceptual fields in the CNN convolutional layer. CNN extracts the image signal by reducing duplicate information, thereby revealing the potential information in the image signal. [ 4] The image features extracted from the different layers of the network are output, which are subsequently used as input features for feature-level fusion. The details of the feature-level fusion are as follows: the image feature vector is extracted via the CNN feature-extraction method, and then the spectral feature vector is extracted via the NIRS feature-extraction method. The extracted spectral feature map is a one-dimensional vector. The extracted image feature map is placed in a flattening layer and turned into a one-dimensional vector. The equation for the flattening operation is as follows. [ 3]Height,Width,Channel→Height×Width×Channel The one-dimensional vectors are then stitched together to obtain the fused data with the equation shown below:[4]Ff=AppendFa,Fb where Ff denotes the merged data, Fa denotes the 1D feature map of the image sample, Fb denotes the 1D feature map of the spectrum, and Append· denotes the fusion function that stitches the Fb vector after the Fa vector. In this work, the feasibility of near-infrared reflectance spectroscopy (NIRS) features and image features for diagnosing woolliness response diseases in “Dangshan” pears was verified. The convolution depth of the visual image and the number of neurons in the spectral neural network were both analyzed and explored. Finally, the feasibility of the feature-level fusion technique was verified for NIRS features and image features, and the influence of fusion models on different layers of the network was explored and analyzed for convolutional gods. ## 2.6. Evaluation This research employs confusion matrices to compare the performance of various networks, which reveal the number of accurate and incorrect predictions for each class in a given dataset. In addition, the performance of the model is tested in this study using four metrics—accuracy, precision, recall, and F1 (the cumulative average of precision and sensitivity)—according to the following equations. [ 5]Accuracy=TP+TNTP+TN+FN+FP [6]Precision=TPTP+FP [7]Recall=TPTP+FN [8]F1=2TP2TP+FP+FN where TP (true positive) is the number of positives correctly classified; TN (true negative) is the number of negatives correctly classified; FP (false positive) is the number of negatives incorrectly classified; and FN (false negative) is the number of positives incorrectly classified. ## 3.1. Division of the Training and Validation Sets In this study, the initial data comprised the following two types: healthy and diseased. Table 2 displays the number of these samples, with a total of 480 sample data. The sample set was divided based on a 7:3 ratio, where $70\%$ (336 sample data points) was used to train the network, and the remaining $30\%$ (144 sample data points) was utilized to validate the trained network. This experimental device has 32 GB of memory, and the experiments utilize the TensorFlow framework to build deep-learning model structures and run on an NVIDIA RTX 3060 GPU platform in the python environment. ## 3.2. No-Fusion Separate Modeling Evaluation The number of hidden layer nodes in MLP is a structurally sensitive parameter, which means that a very low number of nodes may lead to poor training, while a high number of nodes might lead to overfitting [32]. Therefore, we picked ten groups of hidden layer nodes from 10 to 100 in 10 steps of traversal to develop the MLP model matching the number of hidden layer nodes. The MLP model was denoted as MLP X, where X specifies the number of hidden layer nodes. The validation set traversal results of the classification models with varied numbers of hidden layer nodes are given in Figure 6. When the MLP classification models built with different numbers of hidden layer nodes are compared, the results show that model MLP_90, which was built with 90 hidden layer nodes, had the best fit. To validate the feasibility of spectral features to diagnose woolliness response diseases in “Dangshan” pears, we employed a support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and other machine-learning methods to develop models. A grid search approach is utilized to select the best hyperparameters for these machine-learning models. Details of the final parameters of the machine-learning models are presented in Table 2. The validation set results for all machine-learning models are provided in Table 3. By comparing the validation set results of these models, it is shown that MLP had the best modeling performance. The ideal MLP model MLP_90 has accuracy (0.611), precision (0.614), recall (0.611), and F1 (0.608) on the validation set. The results reveal that MLP significantly outperformed other machine-learning models. MLP_90 was the best among the classification models generated using MLP with varied numbers of hidden layer nodes. In the extraction of image features, this paper uses VGG16, VGG19, ResNet50, ResNet101, Xception, and DenseNet201 to perform comparisons with other models. The optimum model for image feature classification models is explored and analyzed. Migratory learning methods such as freeze layers and fine-tuning are employed to extract significant features. In this study, fine-tuning of transfer learning was employed to enhance the efficacy of the CNN architecture and replace the last layer of the pretrained training-only model. The training accuracy curves for the six image feature classification models are given in Figure 7a, with DenseNet201 converging as the fastest and the slowest model being VGG19. The training loss curves in Figure 7b reveal that DenseNet201 had the least training loss. The validation accuracy curves are given in Figure 7c, with ResNet101 converging the fastest and VGG19 converging the slowest. The validation loss curve in Figure 7d demonstrates that ResNet50 had the lowest value of validation loss. The results of the six image feature classification models on the validation set are reported in Table 4. These results suggest that the Xception model outperformed the other models on the validation set in terms of accuracy (0.840), precision (0.879), recall (0.840), and F1 (0.836). The results reveal that Xception works best among the six convolutional neural network methods in this paper. In summary, this section validates the feasibility of spectral features and image features in recognizing woolliness response diseases of “Dangshan” pears. The results of the classification models produced by the two features were compared and analyzed. Among the classification models for spectral features, MLP was the best model with the highest accuracy, precision, recall, and F1. Among these, MLP_90 was the best model with the highest validation accuracy among the models developed with the varied number of hidden layer nodes of MLP. The spectral data used in this study resulted in unsatisfactory extraction of spectral features due to light-scattering effects [44]. Although effective preprocessing can essentially eliminate the light-scattering effect, finding the best spectral preprocessing method for different models is a complex process. To solve this challenge of how to choose the best preprocessing method, in the algorithm proposed in this paper, the spectral data are selected without preprocessing, and the spectral features extracted from the original spectral data are used directly, and then the spectral features are fused with the image features for modeling, thus enabling an end-to-end approach. Therefore, all six models outperformed the spectral feature classification model in terms of validation in the classification model of image features. The training accuracy curves of the six models were more or less the same, with the training accuracy exceeding $90\%$. In addition, the validation accuracy of all six models was above $75\%$. According to a detailed examination of the results, Xception had the best validation accuracy while maintaining the best training accuracy and training loss. This implies that the Xception model is the best model among the six image feature classification models and has greater learning ability for the identification of woolliness response diseases in “Dangshan”. ## 3.3. Modeling of Spectral and Image Fusion Features In this study, the image feature vector was first extracted using the CNN feature extraction method, and then the spectral feature vector was extracted using the NIRS feature extraction method. The two features were then concatenated to generate a multidimensional vector. This vector was used as the input to the prediction layer. The final output of the prediction layer was employed as the score of two pear classes, where the class with the greatest score was regarded as the acknowledged class of pears. The spectral feature model used MLP to extract NIR spectral features, and the image feature network model employed DenseNet201, ResNet50, ResNet101, VGG16, VGG19, and Xception migration learning models to extract image features. This study compared and analyzed the model effects of combining different spectral and image models for fusion modeling. In this case, the spectral models were MLP models built with different numbers of hidden layer nodes, and the image models were different convolutional neural classification networks. In this paper, ten different models of MLP (MLP_10, MLP_20, MLP_30, MLP_40, MLP_50, MLP_60, MLP_70, MLP_80, MLP_90, and MLP_100) were selected to determine the most suitable MLP models for fusion. The classical ResNet and VGG were then used in this study to initially identify the most suitable MLP models for fusion. The accuracy of the training model is not necessarily positively correlated with the number of model layers. This is because as the number of network layers increases, the network accuracy appears to saturate and decreases. Therefore, VGG16 is preferred between the two models VGG16 and VGG19; ResNet50 is preferred between the two models ResNet50 and ResNet101. In this subsection, VGG16 and ResNet50 were used to model the fusion with different MLP models, where the model with the best results was the most suitable model. The accuracy and F1 of the fusion modeling of the two convolutional network models with the MLP are shown in Figure 8. The results show that MLP_30_VGG16 and MLP_30_ResNet50 had the best validation results with the highest accuracy and F1. Therefore, the MLP_30 model with 30 nodes in the hidden layer is the most suitable model for feature fusion. After the ideal number of hidden layer nodes was determined for the MLP model to be 30, the optimal model combining NIR reflectance spectral features and image feature fusion modeling was further examined. The training accuracy curves of the six fusion models are given in Figure 9a, with MLP_30_VGG19 converging the fastest and MLP_30_VGG16 the slowest. The value of the latter was the worst. The training loss curve in Figure 9b shows that MLP_30_VGG19 had the smallest training loss value. The validation accuracy curves are given in Figure 9c, with MLP_30_ResNet101 converging the fastest and MLP_30_DenseNet201 converging the slowest. The validation loss curve in Figure 9d reveals that MLP_30_Xception had the lowest value of validation loss. The results of the six fusion models on the validation set are reported in Table 5. Among these, the best modeling results were discovered for MLP_30_Xception, which had the highest accuracy (0. 972), precision (0. 974), recall (0. 972), and F1 (0. 972). In addition, MLP_30_ResNet101 also performed well, with good accuracy (0. 965), precision (0. 966), recall (0. 965), and F1 (0. 965). The results reveal that the combination of MLP and ResNet101 is the superior combination, and the combination of MLP and Xception is the best combination. ## 3.4. Optimization of Fusion Models for Different Depth Feature Layers of Visual Images MLP shows outstanding performance with ResNet101 and Xception for fusion modeling. However, utilizing image features extracted from different layers of the network and fusing them will cause the models that they build to have different modeling effects. Therefore, this study uses ResNet101 and Xception to further evaluate the performance of convolutional neural networks with different layers for fusion modeling. Five alternative sets of layers (layer 1, layer 2, layer 3, layer 4, and layer 5) of ResNet101 were selected for feature-level fusion with MLP_30, and five models were built to explore the best-fused layers of ResNet101. The distinct fusion layer models are named MLP_30_ResNet101_X, where X is the different layers of ResNet101. At the same time, three alternative sets of layers of Xception (Entry flow, Middle flow, and Exit flow) were selected to be fused with MLP_30 at the feature level, and three models were built to explore the best fusion layers of Xception. The different fusion layer models are named MLP_30_Xception_Y, where Y is the different layers of Xception. The training accuracy curves of ResNet101 for five alternative sets of layer fusion modeling are given in Figure 10a, with all five models obtaining training accuracies above $85\%$. The validation accuracy curves in Figure 10b show that MLP_30_ResNet101_layer 5 had superior recognition results compared to the other four models. The training accuracy curves for the three major process fusion models of Xception are presented in Figure 10c, with all three models having training accuracies of over $95\%$. The validation accuracy curves in Figure 10d show that MLP_30_Xception_Exitflow had superior recognition results compared to the other two models. The results of simulating the fusion of multiple layers of the convolutional network with spectral features are displayed in Table 6. By comparing the results of the feature-level fusion of MLP_30 with five different layers of ResNet101 and three different processes of Xception, correspondingly, it was discovered that the MLP_30_ResNet101_layer5 model had the highest accuracy (0.917), precision (0.920), recall (0.951), and F1 (0.917). Precision (0.920), recall (0.951), and F1 (0.917). Additionally, for comparison, the MLP_30_Xception_Exitflow model was determined to have the highest accuracy (0.951), precision (0.956), recall (0.951), and F1 (0.951). The results show that MLP_30_ResNet101_layer 5 had better recognition than the other four models of ResNet101, while MLP_30_Xception_Exitflow had better recognition than the other two models of Xception. In summary, this section verifies that the models created with different layer functions in ResNet101 and Xception had varying performances. Among these, MLP_30_ResNet101_layer5 had the best model performance with the highest validation accuracy among the five sets of different layer fusion models of ResNet101. MLP_30_Xception_Exitflow had the best model performance with the highest validation accuracy among the three primary process fusion models of Xception. ## 3.5. Optimal Model Analysis and Comparison For the comparison in this section, we selected the optimal models MLP_90 and Xception among the spectral and image feature classification models and selected the two superior models MLP_30_ResNet101_layer5 and MLP_30_Xception_Exitflow for the fused features of infrared spectral features and image features. The accuracy comparison of the four models on the validation set is provided in Table 7. Among these, the MLP_30_Xception_Exitflow model, which used a feature-level fusion technique, exhibited good performance with the greatest accuracy (0.951), precision (0.956), recall (0.951), and F1 (0.951). The classification confusion matrix for the four optimal models is presented in Figure 11. The results showed that the MLP_30_Xception_Exitflow network model had the highest validation accuracy. In the MLP_90 network model, 22 “Dangshan” pear diseased samples were incorrectly predicted to be “Dangshan” pear healthy samples, and 34 “Dangshan” pear healthy samples were incorrectly predicted to be “Dangshan” pear diseased samples. In the Xception network model, 23 “Dangshan” pear healthy samples were incorrectly predicted to be “Dangshan” pear diseased samples. Three samples of “Dangshan” pear diseased in the MLP_30_ResNet101_layer5 network model were incorrectly predicted to be “Dangshan” pear healthy samples, and nine samples of “Dangshan” pear healthy samples were incorrectly predicted to be “Dangshan” pear diseased samples. In the MLP_30_Xception_Exitflow network model, seven samples were incorrectly predicted to be healthy samples. In summary, in the comparison of the four superior models, MLP_90, ResNet101, MLP_30_ResNet101_layer5, and MLP_30_Xception_Exitflow, the MLP_30_Xception_Exitflow model, after employing the feature-level fusion technique, obtained the best classification results. The MLP_30_Xception_Exitflow model had the highest accuracy (0.951), precision (0.956), recall (0.951), and F1 (0.951). The combination of the MLP classification model and the Xception convolutional neural classification network with the fusion of the NIR spectral features and image features extracted separately was the best combination. ## 4. Conclusions The fast and precise diagnosis of “Dangshan” pear woolliness response disease is vital, as it is a physiological disease that has a substantial impact on the quality of “Dangshan” pears. This research indicates that it is feasible to apply near-infrared reflectance spectroscopy (NIRS) features and image features to diagnose woolliness response disease in “Dangshan” pears. The experiments first acquired information on the chemical composition and appearance of the samples via NIRS and CVS techniques, respectively, and then used machine-learning and deep-learning methods for diagnostic classification. These findings imply that the feature-level fusion technique can utilize the advantages of NIRS and CVS features to gain more extensive sample information compared to single-feature models and consequently improve the accuracy of recognizing “Dangshan” pear diseases to a greater extent. Then, to explore the effect of different depth feature layers of visual pictures on fusion modeling, experiments were performed to model the fusion of CVS features extracted via different layers of convolutional neural networks with NIRS features. The results show that the fusion modeling of the feature layer with the highest depth of the visual image has a more accurate classification performance. In this study, the combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with the accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. In summary, the use of near-infrared reflectance spectroscopy (NIRS) features and image feature fusion for the identification of “Dangshan” pear woolliness response disease is a promising method for disease diagnosis and provides a broad perspective in the field of fusion for agricultural disease diagnosis. It can provide new ideas for achieving fast, reliable, and nondestructive quality control instruments for various agricultural products. ## References 1. Zeng W., Qiao X., Li Q., Liu C., Wu J., Yin H., Zhang S.. 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--- title: 'Depression in Central and Eastern Europe: How Much It Costs? Cost of Depression in Romania' authors: - Miorita Melina Iordache - Costin Octavian Sorici - Kamer Ainur Aivaz - Elena Carmen Lupu - Andrei Dumitru - Cristina Tocia - Eugen Dumitru journal: Healthcare year: 2023 pmcid: PMC10048715 doi: 10.3390/healthcare11060921 license: CC BY 4.0 --- # Depression in Central and Eastern Europe: How Much It Costs? Cost of Depression in Romania ## Abstract Objective: The present study aims to estimate the public cost of depression in Romania during a seven-year time span to complement existing papers with data from Central and Eastern Europe and to identify and propose measures that allow efficient use of funds. Methods: We used data collected from the National Health Insurance System to analyze the main components of the cost. Findings: Indirect costs exceed direct costs. Within the direct costs, hospitalization and medicines still have an important share but are decreasing due to the intervention of outpatient services such as psychiatrists and psychotherapists. Conclusion: Since the goal is mental health, it is necessary to act early and quickly to decrease the burden in the long run. Annually, the mean direct cost of depression per patient is EUR 143 (part of it is represented by hospitalization, i.e., EUR 67, and psychotherapy, i.e., EUR 5), the mean cost of sick leaves per patient is EUR 273, and the total cost per patient is EUR 5553. Indirect costs (cost of disability and lost productive years) represent $97.17\%$ of the total cost. An integrated approach to early diagnosis, effective treatment, monitoring, and prevention as well as included economic and social programs are needed to optimize indirect costs. ## 1. Introduction The World Health Organization (WHO) defines mental health as a way in which people can be active and creative [1]. Among mental health disorders, depression is a global problem. Although efforts are made, there is no progress in reducing the burden since 1990 [2,3]. The European Brain Council Value of Treatment study estimated that only $52\%$ of cases of depression are diagnosed, $62\%$ receive treatment, $33\%$ have good results, and another $33\%$ have poor results. Only $12\%$ receiving treatment referred to a psychiatrist/specialist [4]. Globally, only $30\%$ of patients with depression receive treatment, and of these, $40\%$ receive adequate treatment [5]. Coordination between governments, the health community, and citizens is needed [3] because “for every dollar invested in extended treatment for depression and anxiety, there is a return of $4 in better health and productivity” according to the Lancet’s mental health initiative [6]. In Europe, ESEMeD identified major depression as the most common mental disorder [7]. Data from 2021 show that depression has a high prevalence ($6.38\%$), the highest prevalence being found in countries with high incomes. There is variability in prevalence and large differences between countries reaching the ratio 4:1 [8]. For example, in 2010 in Spain, Vieta found a prevalence of $4.75\%$ and a public cost of EUR 3255/year/patient [9]. In 2000, Thomas found a total cost for England estimated at GBP 370 million, an increase compared to previous estimates [10]. In Germany, hospital care was the main component ($43.9\%$ of the total) [11]. In 2021, the EPICO Study showed that the societal cost of depressive disorders was estimated at EUR 6145 million. The average cost per patient/year was EUR 3402 [9,12]. For Central and Eastern Europe, studies are lacking [13]. The region has similarities from the communist period. Even if progress has been made, the development of mental health systems is ineffective [14]. The Romanian healthcare system is based on a social health insurance model in which the state’s role is significant. It allows insured persons access to a complete package of services, while uninsured persons are only entitled to a minimum set of services. In practice, only about $89\%$ of the Romanian population was covered by the social health insurance system in 2017; approximately 17,000,000 are insured persons out of a total of 19,587,000 inhabitants. Healthcare services are provided in 41 counties and the capital Bucharest, following the norms established at the central level. The county Health Insurance Houses have contracts with medical service providers (general practitioners/family doctors, specialist doctors, laboratories, hospitals, home care providers, etc.) at the local level. In addition, healthcare providers can be paid by the Ministry of Health under national health programs [15]. The present study aims to estimate the public cost of depression in Romania during a seven year-time span, to complement existing papers with data from Central and Eastern Europe, and to identify and propose public policy measures that allow more efficient use of funds. ## 2. Materials and Methods Our study is a retrospective analysis that used electronic data on health insurance claims at the National Health Insurance House (NHIH). All accessed data are represented by the services and their associated costs reimbursed for hospital care, outpatient care, psychotherapy, general practice, the costs of sick leaves, and the number of deaths related to depression. Additional information, i.e., the age and gender of the insured persons, was provided. ## 2.1. Sample Size and Participants A total of 2,504,792 patient cases diagnosed and paid within the public health insurance system during 2015–2021 were used. Inclusion criteria: patients >18 years old were diagnosed in primary care, outpatient, and hospital care, and diagnosed with depression during 2015–2021. Exclusion criteria: patients <18 years old, patients who paid for consultations or medication, patients who addressed the private medical system, patients who received an incorrect diagnosis, or who did not receive treatment were not included. Other types of mood disorders were not included in the study. The diagnosis of depression (depressive disorders) is according to the International Classification of Diseases ICD 10 [16], which includes F32 (depressive episode with three severity types (mild (F32.0), moderate (F32.1), or severe (F32.2 and F32.3), F32.8 (other depressive episode), F32.9 (depressive episode unspecified), and F33 (recurrent depressive disorder) with repeated episodes similar to the depressive episode (mild (F32.0), moderate (F32.1), or severe (F32.2 and F32.3), F33.4 (in remission), F33.8 (unspecified), and F33.9 without episodes of mania. ## 2.2. Definition of Cost Variables Depression’s cost has been defined as the sum of the direct costs (DC)—the costs of resources used to treat the disease—and indirect costs (IC)—the number of resources lost due to the disease that can be attributed to this diagnosis [17]. ## 2.3. Direct Costs The DCs sources are the hospital, outpatient, psychotherapy, primary care, and medication. From the payer’s perspective, the DCs are real costs calculated yearly per person. The ICs include morbidity (sick leave costs—SL) and mortality (cost of productivity loss—CPL). ## 2.3.1. Primary Care Consultations An insured person must first contact his general practitioner doctor. The cost of this consultation is obtained by multiplying the number of consultations with the annually indexed cost point. ## 2.3.2. Outpatient System: Psychiatric Consultations and Psychotherapy The outpatient consultations include the consultations provided by the psychiatrists and the related service providers, counseling, and psychotherapy offered by psychotherapists for the disease codes ICD 10 F32 and F33 and were analyzed separately. Their cost is obtained by multiplying the number of consultations with the cost points given to each service and the annually indexed cost point value. ## 2.3.3. Hospitalization Hospitalization includes all expenses necessary for resolving individual cases, including medicines, sanitary materials, laboratory investigations, and imaging. Medical services can be provided by day hospitalization, with a maximum duration of 12 h, or by continuous hospitalization, which involves a duration of hospitalization of more than 12 h, according to the NHIH framework contract [18]. The cost of hospitalization is obtained by multiplying the number of hospitalization days with the reference cost per hospitalization day. ## 2.3.4. Medication The costs of medical prescriptions are given by the net cost of antidepressant drugs prescribed and reimbursed. ## Morbidity Cost Morbidity cost is represented by the total number of sick leaves (SL) given for the incapacity of working with the disease codes F32 and F33, multiplied by the working day costs specific to each insured person. The SL cost is assured from the NHIH budget. Mortality data quantification was performed using the years of potential productive life lost (YPPLL) and the cost of productivity loss (CPL) indicators [19]. YPPLL is an impact indicator that measures the socio-economic burden of premature deaths, thus estimating the average period the person would have experienced if he had not died due to illness or comorbidity [20]. We used data provided by the National Institute of Statistics (NIS) regarding the percentage of deaths, by year and age category, in the general population. We then kept the same percentage, applied to the number of deaths (ND) associated with depression or comorbid depression, by years and age categories, provided by the NHIH. When calculating YPPLL, we used 9 age classes of 5 years each, from 18 to 63 years for women and 65 for men, as limits for the productive life period. We have thus calculated the time interval lost between the moment of death (considered the median year of the specific age group) and the maximum limit set of the productive life interval. We multiplied the obtained YPPLL with the gross national product (GNP) per capita of the year in which the death occurred, which is supposed to remain constant throughout the entire productive life of the individual. The cost of productivity loss was calculated using the formula:CPL=∑$i = 19$NDi×YPPLLi×GNP where CPL—cost of productivity loss; NDi—number of deaths associated with depression or comorbid depression for each age class of 5 years (i); YPPLLi—years of potential productive life lost for each age class of 5 years (i); GNP—gross national product per capita of the year when death occurred. ## 2.5. Statistical Analysis For the statistical analysis of the mean differences between the variables, the Student’s t-test was used, using the GraphPad software (Addinsoft Software 9, Inc., San Diego, CA, USA) [21]. For gender variable, to compare multiple proportion, K proportion test with Marascuilo procedure was used. Descriptive statistics was performed for all continuous variables (Supplementary Material). We used the principal component analysis (PCA) to achieve the following: (i) highlight the correlations between the considered variables, (ii) infer the similarities, respectively, the differences between the statistical units (years) considered by all the recorded variables, and (iii) explain the similarities, respectively the differences between individuals. For this purpose, the results obtained for the statistical units (years) are associated with the results obtained for the statistical variables. For this purpose, XLSTAT software for Excel 2021 [22] was used. The value of $p \leq 0.05$ was considered statistically significant. The correlation matrix shows the values of the Pearson’s correlation coefficients between variables, considered two by two. This coefficient analysis allows the study of the link intensity and the evaluation of the possibility of applying the analysis of the main components. High values of these coefficients (over +0.7 or less than −0.7) show a strong correlation between the considered variables. If the value of these coefficients is positive, the links are direct, and if it is negative, the links are inverse. ## 3.1. Descriptive Analysis The patient cases analyzed between 2015 and 2021 comprised 2,540,792 patient cases: 1,607,957 ($63.29\%$) were women and 932,835 ($36.71\%$) were men. Women were predominantly affected by depression in a ratio of 1.7 compared to men (Figure 1). The mean prevalence of depression in the general population was $2.13\%$ ± 0.33 (min 1.63, max 2.56), with a mean prevalence of $1.35\%$ ± 0.21 in women (min 1.02, max 1.65) relative to the mean prevalence of depression in men $0.78\%$ ± 0.11 (min 0.60 and max 0.93). The main components of the direct cost are the cost of hospitalization and the cost of medication (Table 1 and Figure 2). The direct annual costs for the treatment of depression per patient ranged from a max of $42.45\%$ and min of $27.93\%$, the medication weighing second in the direct cost. Hospitalization costs are predominant within the DCs, covering more than half of the spending between 2017 and 2019. However, the constant decrease from the last years placed it at $32.97\%$ in 2021. The total cost of specialized services increased by over $10\%$ from 2015, reaching $18.87\%$ in 2021 (Supplementary Material). The cost of psychotherapy services also increased in 2021, reaching $6.24\%$ and being relatively double the percentage of other years; yet, the cost of primary care remained consistent, below $1\%$, throughout the study. The mean direct cost was EUR 51,068,834 with a mean annual direct cost of depression per patient of EUR 143. The mean cost of sick leaves was EUR 523,173,605 with a mean sick leaves cost per patient/year with EUR 273 significantly higher than the mean direct cost ($$p \leq 0.001$$, $t = 4.340$) (Figure 3). The invalidity cost was $23.59\%$ of the total in 2015, $39.19\%$ in 2016, $20.08\%$ in 2017, $34.22\%$ in 2018, $32.83\%$ in 2019, $32.48\%$ in 2020, and $9.15\%$ in 2021 (Figure 4). The number of deaths recorded in the adult population is increasing along with the prevalence of illnesses. The CPL value represented $73.00\%$ in 2015; unfortunately, in 2021, it increased to $89.16\%$ (Figure 4). The mean total cost of depression (direct cost and indirect cost) was EUR 2,015,731,285.86 with the annual mean cost per patient during the study period EUR 5553 for an average of 362,000 patients/year, of which $2.83\%$ corresponded to direct health costs and $97.17\%$ to indirect costs. ## 3.2. Correlations Data analysis from the correlation matrix (additional word file from Supplementary Material) showed that: The number of days of hospitalization is negatively correlated with the number of specialty outpatient consultations (r = −0.783, $$p \leq 0.037$$) and the number of psychotherapy services (r = −0.783, $$p \leq 0.037$$). The cost of hospitalization is negatively correlated only with the number of services in primary care (r = −0.799, $$p \leq 0.03$$), and there are no other correlations with other analyzed variables. A strong positive correlation (r > 0.974, $p \leq 0.001$) occurs between the number of psychotherapy services and number of specialized outpatient clinic services. The cost of psychotherapy is positively correlated (r > 0.98 and $p \leq 0.001$) with the cost of specialized outpatient services (psychiatry) and negatively with the number of days of hospitalization (r = −0.762, $$p \leq 0.03$$). The number of medical prescriptions is positively correlated (r > 0.97, $p \leq 0.001$) with the ones of outpatient consultations and psychotherapy services. The cost of primary care is negatively correlated with the number of days of hospitalization (r = −0.833, $$p \leq 0.02$$). The number of sick leaves is not correlated with any other studied variable. The cost of medication and sick leaves are not correlated with any other variable analyzed. ## Principal Component Analysis Principal component analysis (PCA) is an exploratory method of data analysis applied in the study of the relationship between numerical variables. Due to the different nature of the variables used, the data are standardized for the calculation of the distance between two point-values using Euclidean distance. In this study, PCA was used to highlight correlations between the variables considered (shown in Supplementary Material) and to observe similarities and differences between statistical units. PCA involves the formation of factor axes (principal components) which represent a linear combination of variables that are correlated with each other. These axes make it possible to explain the similarities and differences between the statistical units in terms of all the variables considered. Since the first two factorial axes cumulate $80\%$ of the total variance (Table 2), our analysis will relate only to them, as shown in Figure 5. The first factorial axis highlights a strong correlation between the variables: number of medical prescriptions consultations, number of prescriptions, outpatient cost, cost of psychotherapy, number of psychotherapy consultations, number of outpatient consultations, and cost of general practice. These variables form a cluster. All these variables are far from the number of consultations during hospitalization, with which they are in a reverse connection. In order to identify the periods of the different evolution of the values recorded by statistical variables, we made a graphical representation of the points defined by observed years. The diagram shown in Figure 6 reveals three clusters with specific characteristics:−the years 2015 and 2016, located on the same side relative to the first factorial axis, are characterized by the same pattern;−the years 2017, 2018, and 2019 present values closest to the analyzed period’s mean levels;−2020 and 2021, marked by the COVID-19 pandemic, have similar values for all the analyzed indicators. ## 4. Discussion Mental illness is an individual experience, the responsibility in care belongs to the medical sector, but it also needs attention from society and government policies. Depression is a disorder, but it also manifests as a comorbidity of other chronic diseases, with implications on medical costs, patient’s quality of life, and economic and social costs in general [23]. The analyses of the cost of the disease, along with the cost-effectiveness analysis or other types of evaluations, are necessary for the government policies and have implications on strategic, preventive, and clinical decisions centered on the person [24]. Mental health policies are insufficiently planned, monitored, and reported; thus, policies must be designed with stakeholders and practitioners. They should present strategies for implementation, measuring feasibility, cost-effectiveness, and impact on health outcomes [25]. The current barriers are mainly gravitating around limits of investing in mental health, low political priority, absence of needs-based policy, diagnosis of health disorders, lack of resources, limitation of accessibility due to financial difficulties, and dysfunction in optimizing available resources [26]. Eurostat indicates a depression prevalence in Romania of $1\%$ for 2019, with a European mean prevalence of $7\%$ [27]. In the current study, the mean depression prevalence is $2.13\%$. We mention that the prevalence obtained in this study reflects only the patient cases diagnosed and reimbursed in the public health insurance system. People who received a wrong diagnosis, who turned to the private health service system, etc., were not included. This indicator is increasing in Romania, with mainly women being affected—a trend that has been constantly maintained during the studied period. This result is similar to Shoukai’s findings in a global study on inequalities in mental health. He showed that disparities (i.e., women are twice as likely as men to suffer from mental health illness) have not yet been reflected in health policies [28]. Direct cost. The results of the present study show that the mean annual direct cost of depression per patient was EUR 142.3, representing half the European mean. The total annual cost of depression in Europe was estimated at EUR 118 billion in 2004, corresponding to EUR 253 per inhabitant [13]. The main components of direct cost are the cost of hospitalization with $36\%$ mean/year and medication with $42\%$ mean/year. The hospitalization cost is predominant in the direct cost, with an oscillation finally leading to a decrease in the final years of the study. Proportionally opposite, the outpatient services increased as well as the psychotherapy services. There is a tendency for patients with depression to be diagnosed and treated mainly in outpatients. The increased number of outpatient consultations and psychotherapy services caused a substantial decrease in the days of hospitalization. These correlations confirm the results of previous studies conducted around the globe, which demonstrate that psychotherapy and individualized cognitive behavioral therapy (CBT) are more cost-effective than regular healthcare, both alone and in combination with medication [24,29,30]. The mean annual total costs per patient ranged worldwide between USD 1300 to USD 2700, with hospitalization expenses being the significant component in direct costs, as shown in a systematic review conducted by Luppa [17]. Nevertheless, in the present study, the mean annual total costs per patient are EUR 5553, thus much higher. Primary medicine holds less than $1\%$ of the direct cost; it ensures the monitoring and prescription of drugs under the recommendation of psychiatrists (medical letters with a validity of 3 to 6 months). Primary medicine does not offer diagnostic or preventive services; thus, several patients remain unidentified or are treated for other comorbidities, therefore increasing the number of days of hospitalization. The patients directly access the specialty outpatient and are sent to the hospital from these medical services. Therefore, this previously mentioned situation could be another hypothesis. Direct costs are underestimated due to insufficient knowledge and stigma. Addressability in the healthcare system is conducted either for somatic symptoms or frequent comorbidities of depression. The most known are diabetes mellitus, high blood pressure, inflammatory bowel diseases, and other chronic illness [31,32,33,34]. A meaningful way to stop this cost explosion is by increasing the research in field progress. Moreover, better detection, prevention, treatment, and patient management are imperative to reduce the burden of depression and its costs [13]. Indirect costs. The report on the global burden of the disease states that mental disorders account for $13\%$ of all disability-adjusted life years, with years living with disabilities and depression being the leading cause [35]. The indirect cost of sick leave is a burden at present with a mean EUR 273 per patient/year and EUR 523,173,000 annual cost and they are in a ratio of 6:1 compared to CD. The morbidity costs related to lost working days paid through sick leave are EUR 273 per patient annually. The indirect cost of sick leave is a burden at present. One hypothesis for the high rate of sick leave prescriptions could be that several patients receive them successively without improving their health status. Another hypothesis is that sick leaves are prescribed for long periods of hospitalization. A concept that can be useful in the discussion is inequity in the use of resources. Morris defines horizontal inequity as the use of different amounts of care for the same needs. He found that people with low incomes and ethnic minorities are more likely to access medical services and that economic status has an effect on the demand for health services [36]. Mortality associated with depression. The number of deaths recorded in the adult population is increasing, given the increased prevalence of illnesses. The mean cost of premature death was EUR 1,441,488,847, and they are in a ratio of 13:1 compared to CD. It is an alarming report; the losses we register as a society are much higher than the costs of care or prevention. The mean annual total costs per patient ranged worldwide between USD 1300 to USD 2700, with hospitalization expenses being the significant component in direct costs, as shown in a systematic review conducted by Luppa [Eroare!Fărăsursădereferință.]. Nevertheless, in the present study, the mean annual total costs per patient are EUR 5553, thus much higher. Clusters of the years One direction of analysis can be outlined considering the introduction of a health insurance card for persons and a card reader for the health staff which allows the validation for the consultation in 2015. In the first years, there were difficulties in distributing these to the insured person and in the function of the electronic system that would allow electronic validation of consultation [37]. Another dynamic that we were able to analyze was the regulations on psychotherapy services which are considered auxiliary services, which means the therapist does not have a direct contract with the public health system but through another provider who can prescribe, under certain conditions, psychotherapy services and receive their reimbursement from the public insurance system. In 2012, in the Framework contract, psychotherapy consultations were regulations only for psychiatrists and speech therapists. In 2014, the specialties that could prescribe were psychiatry, neurology, and otorhinolaryngology. In 2017, the list of prescribers of psychotherapy services was extended to nine specialties. In 2023, the list of prescribers is increased to 23 specialties [38,39,40,41]. COVID-19 implications. We face a particular situation during the pandemic years. Although the number of patients with depression and the associated costs increased before 2020, both indicators sharply diminished in 2020 and 2021. The situation was most likely caused by the pandemic restrictions, with limited hospital access and outpatient diagnosis and treatment for any other patient except COVID-19. Limitations The present study is the first study in Romania that analyzes the cost of depression, according to the data known to us, which, however, has limitations that must be mentioned to support the analysis and correct use of these data. Uninsured patients and those who benefited from paid services in the private system were not included. Only the public costs insured by the public health insurance system were included. Incorrect coding could be a limitation. Presenteeism, i.e., the decrease in productivity at work, part of the cost of morbidity, included in the indirect cost, was not considered. Additionally, comorbidities state and medication were not included. The socio-demographic variables were not included; yet, they would have been useful in understanding the background of people. This study did not include additional costs related to suffering, loss of opportunities in education, and reduced participation in family life that could not be quantified [24]. Solutions This analysis is just the tip of the iceberg in solving the burden that depression represents. We only analyzed public health costs. We must not lose sight of the wider context in which these things happen. To find solutions, an integrated approach must be used in which health is alongside economic, social, political, and environmental factors. The perspective of social epidemiology can provide an in-depth understanding of the context and some solutions. Addressing discrimination and inequalities in the social environment can be possible through programs adjusted to the communities and the environment in which they live [42]. Additionally, mental health can benefit from efforts in other areas, such as fighting HIV, improving maternal health, and reducing child mortality. It could access political spaces and become global health priorities, channeling resources, some of which integrate into primary health services [43]. Guidelines for the management of depression should be co-designed by stakeholders and measure feasibility, cost–effectiveness, and impact on health outcomes [25]. Routine monitoring at the patient level has an essential role in individual care and leads to increasing the performance of the mental health care system [44]. A proposal for the primary care system includes depression screening in the annual review and periodic depression screening, especially for patients with chronic diseases. Moreover, an integrated approach involving a clinical guide to psychological services, including digital health interventions, is considered a prerequisite for increasing the accessibility and effectiveness of mental health services [45]. Another proposal would be to prescribe psychotherapy from the initial diagnosis of the depressive episode, at the first hospitalization, and in treating any chronic disease. Funds for psychotherapy programs, primary care, or outpatient can be reallocated from the economy made to the fund currently allocated to sick leaves or hospitalization. Thus, the cost could turn from a burden into a resource. Experience in this regard already exists. For example, a program for Improving Access to Psychological Therapies was put into place in the UK [46]. ## 5. Conclusions Depressive disorders are a burden for Romanian society. Annual costs represent approximately EUR 2,015,731,285.86 with an annual cost per patient of EUR 5553. Indirect costs (cost of disability and lost productive years) represent $97.17\%$ of the total cost. It is helpful to include depression screening in the annual review and monitor patients with depression in the primary care system. Psychotherapy can play a considerable role. An integrated approach to early diagnosis, effective treatment, monitoring, and prevention as well as included economic and social programs are needed to optimize indirect costs. ## References 1. Mental-Health-Strengthening-Our-Response 2. Depression and Other Common Mental Disorders Global Health Estimates. (2017.0) 3. 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--- title: Comparative Metabolomic Analysis of the Nutritional Aspects from Ten Cultivars of the Strawberry Fruit authors: - Xinlu Wang - Linxia Wu - Jing Qiu - Yongzhong Qian - Meng Wang journal: Foods year: 2023 pmcid: PMC10048718 doi: 10.3390/foods12061153 license: CC BY 4.0 --- # Comparative Metabolomic Analysis of the Nutritional Aspects from Ten Cultivars of the Strawberry Fruit ## Abstract Strawberry (Fragaria × ananassa) is among the most widely cultivated fruits with good taste and rich nutrients. Many strawberry species, including white strawberries, are planted all over the world. The metabolic profiles of strawberry and distinctions among different cultivars are not fully understood. In this study, non-targeted metabolomics based on UHPLC-Q-Exactive Orbitrap MS was used to analysis the metabolites in 10 strawberry species. A total of 142 compounds were identified and were divided into six categories. Tochiotome may differ most from the white strawberry (Baiyu) by screening 72 differential metabolites. Histidine, apigenin, cyanidin 3-glucoside and peonidin 3-glucoside had potential as biomarkers for distinguishing Baiyu and another 11 strawberry groups. Amino acid metabolisms, anthocyanin biosynthesis and flavonoid biosynthesis pathways were mainly involved in the determination of the nutrition distinctions. This research contributes to the determination of the nutrition and health benefits of different strawberry species. ## 1. Introduction Strawberry (Fragaria × ananassa) is among the most widely cultivated fruits, with a total of approximately 8,882,500 tons production all over the world in 2019 [1]. Strawberries have good taste and appearance and are rich in nutrients. Organic acids, amino acids, vitamins, other aromatic substances, and nutrients have been reported as the main determinants of strawberry quality and flavor [2]. For example, strawberry sweetness is determined by sugar, whereas acidity and umami are regulated by organic acids [3]. The nutrients and bioactive compounds in strawberries, such as vitamins and phenolic constituents, carry benefits and biological functions, such as antioxidation, anti-inflammatory, and anticancer [4]. The content of vitamin C was up to 58.8 mg per 100 g fresh strawberries [5]. Anthocyanins are well known phenolic constituents in strawberries, and the reported content was from 150 mg/kg to 600 mg/kg in fresh samples [6]. An increasing number of in vitro and in vivo studies have demonstrated that strawberry consumption was related to the reduction of the risks of cardiovascular disease (CVD), type 2 diabetes, neurodegeneration, and even cancers [7,8,9,10]. However, composition and contents in strawberries are different according to species, geographical origin, genetic makeup, and agronomic practices [11]. For example, recent research found that sucrose and fructo-oligo-saccharides (FOS) can be used to distinguish Mexico-grown and Canada-grown strawberries [12]. Similarly, the content of melatonin was found from 1.38 ng/g to 11.26 ng/g in four species of strawberry, namely, Camarosa, Candonga, Festival, and Primoris [5]. To better elucidate the differences in volatile compositions between Albion and Juliette varieties, researchers used comprehensive two-dimensional gas chromatography (GC × GC) combined with time-of-flight mass spectrometry (ToFMS) [13]. Ninety-four differential compounds were identified, including acidic ingredients. These results provide possible explanations for the sweeter flavor of Juliette than Albion. White strawberry is becoming popular in recent years due to its special appearance, and the white strawberry variety of “Baiyu” (BY) was semidomesticated in China with limited research. It is widely known that anthocyanins contribute significantly to the color of fruits, vegetables and flowers [14]. Therefore, the composition differences, including the disparity of anthocyanin contents between BY and other strawberry cultivars, need to be studied. Findings will provide important information for future breeding programs. Nowadays, metabolomic analysis has been increasingly used to identify the overall components that accumulate during fruit ripening to help confirm the differences among strawberries from different genetic backgrounds, agronomic management styles, maturity stages, and growing conditions [15]. For example, a recent study explored metabolic differences in six strawberry cultivars by using metabolomics analysis [16]. The polyphenol profile in Praratchatan No.80 and fatty acid synthesis/oxidation in Akihime were critical distinctions. Interestingly, a recent study revealed the relationships between volatile compounds in strawberries and consumer acceptability through untargeted metabolomics. The quantitative results identified nine aroma biomarkers that impact consumer’s preference for strawberry preserves [17]. The present study focused on the metabolites in 12 groups of strawberry cultivars and distinguished their differences by using untargeted metabolomics analysis. The obtained results provided a metabolite profiling of 142 compounds in positive and negative modes. All of the identified compounds were divided into six categories, and the total contents of each category were analyzed. Furthermore, pairwise comparisons were conducted between the white strawberry group with other strawberry groups by using multivariate statistical analysis. This study is the first to identify and compare the metabolites in 10 strawberry cultivars. The results obtained contribute to an investigation of nutrition in different strawberry cultivars and provide a scientific basis for postharvest aspects of strawberry. ## 2.1. Sample Collection This study included 12 groups of strawberries, with a total of 10 strawberry cultivars. The cultivar names were Baiyu (BY), Fenyu (FY), Guangdian (GD), Benihoppe (HY1, HY2, and HY3), Ssanta (SDH), Kaorino (SZ), Tongzhougongzhu (TZGZ), Yuexiu (YX), Tochiotome (YY), and Akihiime (ZJ). In particular, since the Benihoppe cultivar was widely cultivated in China, a total of three groups (HY1, HY2, and HY3) of this cultivar from the same season and region were chosen in this study. The strawberries were collected from Beijing (China) during February and March 2022. The samples were immediately frozen in liquid nitrogen after harvesting and stored at −80 °C until the extraction of metabolites. ## 2.2. Reagents Methanol and acetonitrile (liquid-chromatography-grade) were purchased from Merck (Darmstadt, Germany). Deionized water (18.2 MΩ) was obtained from a Milli-Q water purification system (Millipore, Boston, MA, USA). Formic acid and ammonium formate were supplied by Sigma-Aldrich (St. Louis, MO, USA). ## 2.3. Sample Preparation for Metabolite Extraction Frozen strawberry samples (1 g) were placed into 15 mL Eppendorf tubes and pre-chilled in liquid nitrogen. Then, 10 mL methanol-water (8:2, v/v) was added to the samples. After that, the mixtures were vortexed for 1 min and subjected to ultrasonic vibration for 30 min. They were then centrifuged for 5 min at 10,000 r min−1 at 4 °C. The supernatants were collected carefully and filtered through 0.2 µm polytetrafluoroethylene filters for analysis. Four replicates were prepared in each strawberry group. Equal quantities of the sample supernatants were mixed to prepare the quality control (QC) sample, which was embedded in the batch of every six samples during the analysis to assess the system’s stability. ## 2.4. Non-Targeted Metabolomics by Ultra-HPLC (UHPLC) Q-Exactive Orbitrap MS Chromatographic separation was achieved with an Ultimate 3000 UHPLC system (Dionex, Boston, MA, USA) equipped with an Xbridge Amide column (2.1 mm × 150 mm × 3.5 µm, Waters) at 40 °C. The flow rate was 0.3 mL min−1. Mobile phase A consisted of water with $0.15\%$ formic acid and 10 mM ammonium formate. Mobile phase B consisted of acetonitrile with $0.15\%$ formic acid and 10 mM ammonium formate. The gradient conditions were set as follows: 90–$80\%$ B for 0–8 min; 80–$70\%$ B for 8–13 min; 70–$60\%$ B for 13–16 min; 60–$90\%$ B for 16–16.1 min; and $90\%$ B for 16.1–20 min. The injection volume was 2 µL. The sample tray was set at 4 °C. Mass spectrometric detection was performed on a Q-Exactive Orbitrap mass spectrometer equipped with an ESI ion source operated in the positive and negative ion modes, respectively (Thermo, Boston, MA, USA). The experimental parameters were as follows: spray voltage, 3.00 kV; sheath gas pressure, 30 psi; auxiliary gas pressure, 10 arbitrary units; capillary temperature, 320 °C; auxiliary gas heater temperature, 350 °C; scan mode, full MS (70,000 resolution)-dd/MS2 (17,500 resolution; NCE, 30 eV); and scan range, 70–1050 m/z. ## 2.5. Data Processing and Statistical Analysis Non-targeted metabolomics data were imported to the Compound Discoverer 3.3 (Thermo, Boston, MA, USA) software for analysis, including peak filtering, alignment, identification, and normalization. Relevant parameters were set as follows. The retention time tolerance was 0.2 min, and the mass tolerance was 5 ppm. The signal-to-noise ratio was 3, and the minimum peak intensity was 1,000,000. The intensity tolerance was set at $30\%$. The results of compound identifications were further confirmed by PubChem database (https://pubchem.ncbi.nlm.nih.gov/ (accessed on 1 May 2022) according to the MS2 information. Differential metabolites were screened out based on log2 Fold Change > 1 and p-value < 0.05. The information is further expressed as volcano plots in the Supplementary Materials. Multivariate statistical analysis, including principal component analysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLS-DA), was performed using SIMCA-P 14.1 software (Umetrics, Malmo, Sweden). All the data were log10 processed before importation to reduce the effect of the absolute values. The type of Pareto scaling, which considers the importance of the peaks, was selected to normalize the data. Moreover, 7-fold cross validation was used for validation. Heat maps were constructed by Multiple Experiment Viewer software (accessed on 10 June 2022). Radar diagrams were generated with Microsoft 2019. Metabolomics pathway analysis was conducted by MetaboAnalyst (https://www.metaboanalyst.ca/ (accessed on 30 June 2022). ## 3.1. Morphological Differences among Strawberry Cultivars Twelve groups of samples from 10 strawberry cultivars were analyzed. The Benihoppe variety is widely cultivated in China. Thus, three groups of samples (stands for Figure 1D–F) from the same season and region were chosen for further nutrition study. Figure 1 shows that different strawberry species were morphologically discrepant, especially in color, green pedicle, shape, and size. For example, Baiyu (Figure 1A) and Fenyu (Figure 1B) presented white and pink colors, respectively. Among the red-colored strawberry cultivars (Figure 1C–L), some were typically red (Figure 1D–H,K,L), whereas some were red and white (Figure 1C,I,J). Nevertheless, the morphologies of three groups of Benihoppe (Figure 1D–F) samples did not exhibit differences. The length and shape of green pedicles of different cultivars showed particular differences. For example, the green pedicles of Guangdian (Figure 1C), Ssanta (Figure 1G), and Tongzhougongzhu (Figure 1I) cultivars were big and long. However, Benihoppe (Figure 1D–F) and Tochiotome (Figure 1K) have short green pedicles. The mucro shape of different species showed that most of the cultivars were cuspidal, whereas the mucro shape of Benihoppe (Figure 1D–F) and Yuexiu (Figure 1J) were relatively flat. Different genotypes of strawberry cultivars presented different morphological differences from multiple aspects. ## 3.2. Identification of Metabolites in Strawberries The metabolites in different strawberry cultivars were detected by UHPLC-Q-Exactive Orbitrap MS in both the positive and negative ion modes. The data were then imported to the Compound Discoverer 3.3 software for further identification. A total of 5011 peaks (3932 peaks in the positive ion mode and 1079 peaks in the negative mode) were detected. All of the peaks were finally confirmed to 113 compounds (positive) and 37 compounds (negative) by the fragment information in the Pubchem library. Furthermore, eight of the identified compounds were detected in both the positive and negative ion modes. These were glutamic acid, histidine, apigenin, cytidine, phenylalanine, aspartic acid, asparagine, and O-acetylserine. Therefore, a total of 142 compounds were finally identified. Detailed information of these compounds are shown in Table S1. The representative total ion chromatograms (TIC) of QC sample in the positive and negative ion mode are shown in Figure S1. All the identified compounds in the positive and negative ion modes were classified into six categories: organic acids, amino acids and related derivatives, vitamins, polyphenols, other endogenous metabolites, and exogenous substances. The median values of the total contents of these compounds in each group are expressed as the radar maps in Figure 2. This showed different rules among the different strawberry species when summing up the compounds that belonged to the same category. For example, the median value of the organic acids total content was highest in the group of SZ. The median value of the amino acids and related derivatives and polyphenols total contents were highest in the group of YY. GD cultivar presented the highest content of vitamins. More detailed information on the total content of these six categories detected in the positive and negative ion modes is shown in Figures S2 and S3. To better understand the identified compounds in all strawberry samples, the heat map generated by the median contents was conducted (Figure 3). Red indicates a higher content, whereas green indicates a lower content. The contents of the detected compounds based on the average and median values in the cultivar of HY3 presented an overall higher level compared with other cultivars. Our results confirmed that small molecule metabolites such as organic acids, amino acids, vitamins and polyphenols were widely detected in strawberries. Additionally, the metabolite contents varied in different strawberry cultivars. In this study, a total of eighteen organic acids were confirmed to exist in different strawberry cultivars, such as citric acid, malic acid, quinic acid and mevalonic acid. The results were consistent with previous studies of fresh strawberry samples [18]. Kinds of sugars were identified in the present study, including maltose, melezitose, and raffinose. The highest concentration of the three sugars were in the species BY, HY1, and TZGZ, respectively. This indicated the distinctions in the levels and types of sugar in different strawberry cultivars. Consumer acceptability of strawberry preserves was correlated primarily to perceived sweetness intensity and sugar content [17]. Moreover, sugar concentration is closely related to energy metabolism, which includes glycolysis, tricarboxylic acid (TCA) cycle, and others. In our study, a total of 18 kinds of polyphenols were identified, including cyanidin 3-glucoside, peonidin 3-glucoside, cyanidin, tiliroside, et al. As can be seen from Figure 2D, the total content of polyphenols was higher in red strawberries than that in the white (BY) and pink (FY) strawberries. This may be because some of the polyphenols, such as anthocyanins, were positively correlated with strawberry color. Interestingly, a previous study revealed that compared with cultivated strawberries, wild strawberry species were found to have a higher proportion of cyanidin [19]. Chlorogenic acid, which was identified in the present study in the positive ion mode, was reportedly among the major polyphenols in strawberries [20]. The content of chlorogenic acid was found to be highest in the TZGZ cultivar of our study. Chlorogenic acid is a kind of strong antioxidant and antimicrobial agent [21]. Emerging evidence from in vitro and in vivo studies suggested that the intake of antioxidative compounds in fruits may enhance body defense against oxidative damage [4]. This finding suggests that eating more strawberries, especially the cultivars of high antioxidative compounds contents, may enhance the body’s antioxidant capacity. Equally important, vitamins, especially vitamin C (ascorbic acid), play major roles in the multiple reactions of organism metabolism. Of all the cultivars in this study, GD cultivar presented the highest content of vitamins, followed by BY and SZ. Previous study pointed out that the ascorbic acid in the cultivars of Darselect, Clery, Elianny, Diammante, and Sonata was two to three times higher than in other cultivars [22]. Amino acids were widely detected in various strawberry cultivars in the present study. This was in line with the profiled amino acids in strawberry juice in a previous study [23]. The richness of amino acids in strawberries provided health benefits, such as antimutagenicity, reduction of blood sugar, and decrease in coronary heart diseases. Moreover, amino acids played vital roles in protein synthesis and strawberry taste composition. Among all detected amino acids in the present study, glutamine was the most abundant in terms of total content in all strawberry samples, followed by pyroglutamic acid and asparagine. Aspartic acid, proline, and valine had co-pigmentation effects on individual anthocyanins in strawberry juice. The combined use of aspartic acid and proline at 105 °C may present better protective effects in anthocyanins [24]. The discrepancy of amino acids in various strawberry species may be related to their genotype and agronomic conditions, and this will lead to the different enzymatic and non-enzymatic browning phenomena during postharvest. Besides the metabolites of the strawberry, various kinds of exogenous compounds were detected at the same time, such as chlormequat and difenoconazole. These compounds are presumed to be used during the strawberry growing process. For example, chlormequat was one of the pesticides in strawberry with the highest detection percentages, according to a previous study [25]. Similarly, although the risk of difenoconazole acute dietary exposure in strawberries among different consumer groups was less than $100\%$, it is necessary to conduct a comprehensive monitoring of pesticide residues in strawberry. The detection of these exogenous compounds prompted a focus on the use of the pesticides in strawberry cultivation. This will help strengthen and standardize farmers’ understanding of strawberry pest-control measures and will provide a scientific basis for the risk control of strawberry products. ## 3.3. Multivariate Statistical Analysis of the Identified Metabolites in Different Strawberry Cultivars For better understanding of the distinctions between the endogenous metabolites among different strawberry cultivars, multivariate statistical analysis was conducted. Figure 4A presents that all the strawberry groups were separated from each other by the PCA analysis, indicating distinctions of the metabolites in different strawberry species. Moreover, pair-wise comparisons based on the OPLS-DA models are established between the BY group and other groups (R2 > 0.995 and Q2 > 0.988) in Figure 4B–L. The total separations further confirmed the diversities between the Baiyu cultivar and the other strawberry groups. Additionally, the repetitions in each group clustered well, indicating that the data exhibited good stability and reproducibility. Figure S4 showed the evaluations of the established OPLS-DA models. The 200 times permutation tests (Figure S4) showed that the original R2 and Q2 (the two right-most points) were always greater than the transposed values on the left (the left scatter points). In addition, the intercepts of Q2 were less than zero in all of the OPLS-DA models. These results demonstrated that all of the established OPLS-DA models were reliable and not overfit. A table of the Std. dev. values in all of the groups has been provided in Table S2. OPLS-DA models were widely used to distinguish cultivar differences, and the process of modelling evaluation and validation was strict. Firstly, 7-fold cross validation was used. Then, R2Y (the explanatory ability of the model to classification variable Y) and Q2 (the predictability of the model) obtained after cross-validation were used to evaluate the effectiveness of the model. Finally, the replacement test was passed (permutation test), the order of Y was changed several times to obtain different random Q2 values, and the validity of the model was further tested (Figure S4). Similar approaches were adopted to differentiate three strawberry cultivars (Camarosa, Festival and Palomar) after GC-MS based metabolomics [26]. ## 3.4. Potential Biomarkers for Distinguishing Different Strawberry Cultivars Based on the principles of p value < 0.05 and log2 Fold Change > 1, the differential metabolites in the both positive and negative ion modes were screened out between the BY group and other strawberry groups. Detailed metabolite information is shown in Table S3, which lists the compounds of significant up-regulation and down-regulation of each pair-wise comparison. Of all the pair-wise comparisons with the BY group, the tochinotome cultivar (YY) may be the most different by screening out 72 differential metabolites. The representative volcano plots between the YY and BY in the positive and negative ion modes are shown in Figures S5 and S6. In addition, histidine, apigenin, cyanidin 3-glucoside and peonidin 3-glucoside were identified as potential biomarkers because they satisfied with the principles (p value < 0.05 and log2 Fold Change > 1) in all of the pair-wise comparisons. Taking a further look at the contents of these four potential biomarkers, they all presented higher contents in other strawberry cultivars than that in the BY group. These results suggest the differences in metabolite contents of various strawberry species, and the distinctions further determined the differentiation of taste, nutrition and even color between the BY species and other strawberry species. Histidine is a semi-essential amino acid for humans and other mammals. It has important functions in mood disturbance and response time [27]. During the storage of fruits and vegetables, histidine is decarboxylated by the effects of amino acid decarboxylase to produce histamine. This may be the reason for the negative effects of histidine on consumer acceptance in another study of peach fruit [28]. The content of histidine varied significantly with the differences among cultivars. For example, in a study of different fig fruit cultivars, “Masui Dauphine” had the lowest content of histidine, whereas “Banane” had the highest content [29]. The differences of histidine content in different strawberry cultivars may further determine the different sourness and storage ability of BY and other strawberries. This finding will provide a scientific basis for exploring the best postharvest treatment and storage conditions for different strawberry cultivars. Apigenin is a kind of flavonoid with potent anti-oxidant effect. Apigenin and its various derivatives are abundant in strawberry, especially in wild strawberry, such as the native Chilean red strawberry [30,31]. In the present study, apigenin was screened out as a significant up-regulation metabolite to distinguish the BY group and other strawberry groups. The nutritional values of apigenin in different strawberry cultivars needs to be a focus of research, especially in species with high content, such as Benihoppe, Yuexiu and Tochiotome. Cyanidin 3-glucoside and peonidin 3-glucoside are two kinds of anthocyanins with significant content differences between Baiyu and other strawberry cultivars. It is well known that cyanidin 3-glucoside was one of the major anthocyanins in strawberry. However, peonidin 3-glucoside was first identified in strawberry in 2010 [32]. They play important roles in providing attractive red color in fresh strawberry fruit [33]. The contents of these two anthocyanins were significantly lower in the Baiyu cultivar than other strawberry cultivars. This may be one of the main reasons leading to the white color of Baiyu. Recent study found that anthocyanin and co-pigment reacted with a noncovalent bond [34]. Amino acids, such as aspartic acid, can be used as co-pigment to increase the stability of anthocyanins in strawberry. Aspartic acid was also identified in different strawberry cultivars in our study. Anthocyanins a good source of nutrients in strawberry. Previous investigations have revealed antioxidant, anti-inflammatory, antihypertensive and anti-hyperlipidemic or anti-proliferative effects of strawberry anthocyanin compounds [35]. Moreover, anthocyanins are also used as dietary polyphenols in food owing to functional properties in regulating blood sugar level and lipid metabolism [36]. Therefore, eating red strawberries with high anthocyanin contents is also an effective way to increase anthocyanin dietary content. According to the 16 differential endogenous metabolites that were screened out multiple times (10 and 11 times) in comparison with BY, the main metabolic pathways involved are summarized in Figure 5A. Significantly different metabolic pathways were related to amino acid metabolisms, anthocyanin biosynthesis and flavonoid biosynthesis. These findings suggested that the distinctions between Baiyu and other strawberry cultivars were mainly reflected in the differences of amino acids and polyphenols. Moreover, complex interactions occurred among these amino acids, related to the tricarboxylic acid (TCA) cycle. The TCA cycle acts as an important junction station for sugars, lipids, and amino acids. A series of processes in the TCA cycle provided energy in the cell metabolism. Many kinds of organic acids are involved in the TCA cycle, including citric and malic acids, which were also identified in the present study. Related differential metabolites are summarized in Figure 5B. Phenylalanine will further participate in the flavonoid biosynthesis pathway by reacting with 4-coumaroyl-CoA. Flavonoid biosynthesis is one of the sources for synthesis of anthocyanins. Taken together, the different metabolite contents in these relevant pathways may further determine the speed and intensity of multiple reactions. This may ultimately lead to the difference in morphology, taste, and nutrition of different strawberry cultivars. ## 4. Conclusions In the present study, we conducted a comprehensive metabolomics analysis of the metabolites in strawberry and compared the differences in their contents in 10 different cultivars. A total of 5011 peaks were detected in the positive and negative modes. They were finally identified as 142 compounds, and were further divided into six categories. The results showed that small molecule metabolites such as organic acids, amino acids, vitamins and polyphenols were widely detected in strawberries, and their contents varied with different strawberry cultivars. Multivariate analysis revealed the distinctions between the white strawberry cultivar (Baiyu) and other strawberries. Histidine, apigenin, cyanidin 3-glucoside and peonidin 3-glucoside had potential as biomarkers for distinguishing Baiyu and the other 11 strawberry groups. Pathway analysis results showed that the main metabolic pathways based on the occurrence of multiple differential metabolites were related to amino acid metabolisms, flavonoid biosynthesis and anthocyanin biosynthesis. These findings suggested that the distinctions between BY and other strawberry cultivars were mainly reflected in the differences of amino acids and polyphenols. 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--- title: Biomanufacturing Recombinantly Expressed Cripto-1 Protein in Anchorage-Dependent Mammalian Cells Growing in Suspension Bioreactors within a Three-Dimensional Hydrogel Microcarrier authors: - Rachel Lev - Orit Bar-Am - Yoni Lati - Ombretta Guardiola - Gabriella Minchiotti - Dror Seliktar journal: Gels year: 2023 pmcid: PMC10048735 doi: 10.3390/gels9030243 license: CC BY 4.0 --- # Biomanufacturing Recombinantly Expressed Cripto-1 Protein in Anchorage-Dependent Mammalian Cells Growing in Suspension Bioreactors within a Three-Dimensional Hydrogel Microcarrier ## Abstract Biotherapeutic soluble proteins that are recombinantly expressed in mammalian cells can pose a challenge when biomanufacturing in three-dimensional (3D) suspension culture systems. Herein, we tested a 3D hydrogel microcarrier for a suspension culture of HEK293 cells overexpressing recombinant Cripto-1 protein. Cripto-1 is an extracellular protein that is involved in developmental processes and has recently been reported to have therapeutic effects in alleviating muscle injury and diseases by regulating muscle regeneration through satellite cell progression toward the myogenic lineage. Cripto-overexpressing HEK293 cell lines were cultured in microcarriers made from poly (ethylene glycol)-fibrinogen (PF) hydrogels, which provided the 3D substrate for cell growth and protein production in stirred bioreactors. The PF microcarriers were designed with sufficient strength to resist hydrodynamic deterioration and biodegradation associated with suspension culture in stirred bioreactors for up to 21 days. The yield of purified Cripto-1 obtained using the 3D PF microcarriers was significantly higher than that obtained with a two-dimensional (2D) culture system. The bioactivity of the 3D-produced Cripto-1 was equivalent to commercially available Cripto-1 in terms of an ELISA binding assay, a muscle cell proliferation assay, and a myogenic differentiation assay. Taken together, these data indicate that 3D microcarriers made from PF can be combined with mammalian cell expression systems to improve the biomanufacturing of protein-based therapeutics for muscle injuries. ## 1. Introduction The production of recombinant therapeutic proteins has become increasingly popular for the large-scale manufacturing of medical-grade biotherapeutics [1,2,3]. Although Escherichia coli (E. coli) and yeast are the most established organisms for biomanufacturing of recombinant proteins, production in mammalian cells has considerable advantages, particularly for complex protein products [3,4]. When produced in mammalian cells, recombinant human proteins undergo more accurate post-translational processing, which is crucial for proteins that require complicated tertiary structures to exhibit biological activity [3,5,6,7]. Compared to the simpler expression systems of E. coli and yeast, one of the biggest drawbacks of using mammalian cells can be the more stringent culture conditions, which can also involve anchorage-dependent growth with less efficient two-dimensional (2D) cultures. The 2D culture systems are reliable and well defined, but the limited growth surface area places them at a disadvantage with respect to large-scale production. Over the past two decades, much effort has been directed at adapting mammalian cell expression systems for more efficient three-dimensional (3D) culture in suspension. This has been achieved by either adapting the mammalian cells to continuous cell lines (CCLs), such as with Chinese hamster ovary (CHO) cells that can grow directly in suspension, or growing the mammalian cells with microcarriers [8,9,10]. Whereas certain mammalian cell types that are anchorage-dependent may not be able to conform to suspension culture, most anchorage-dependent cells can grow in or on microcarriers. The manufacturing capacity of systems using anchorage-dependent mammalian cells has become a critical issue in the development of protein-based therapeutics. As new therapeutic proteins produced in these cells are introduced, 2D systems are initially used for making high-quality products with sufficient production yields for achieving the research and development goals. As the biotherapeutic reaches the clinical phases of development, efficient bioprocessing using large-scale, state-of-the-art manufacturing paradigms becomes essential. The bioprocessing of these biotherapeutics must also meet stringent commercial and clinical requirements, and the 2D manufacturing paradigms initially applied substantially limit the potential to reach the high production yields required by industry for more advanced stages of clinical development. Therefore, as new therapeutic proteins progress toward the clinic, production efficiency must be addressed together with clinical efficacy—usually with the help of the microcarrier systems. In this context, microcarriers combined with suspension bioreactors address many of the key manufacturing drawbacks of the 2D production methodologies. Although conventional microcarriers provide a higher surface area for anchorage-dependent cell cultivation, cells that grow on these polymeric systems exhibit 2D growth characteristics. Therefore, the potential of 3D suspension cultivation using microcarriers may not be fully realized until microcarriers can accommodate cell growth within the volume of the carrier [11,12]. Indeed, many new cell carrier systems for protein production are premised on porous polymeric scaffolds or hydrogel scaffolds that can accommodate 3D cell growth in the pores or bulk of the material. The polymeric materials can be designed as spherical or disk-shaped microcarriers with increased cell growth capacity and the advantages of high surface area to volume ratios. Several research groups worldwide have developed and optimized the production of therapeutic proteins in stirred suspension bioreactors utilizing 2D and 3D microcarrier-based cultivation systems [1,13,14]. Various types of microcarriers have been examined for their ability to serve as growth substrates for cell cultivation in a bioreactor, including solid and porous materials. Solid microcarriers that support 2D cell attachment on their surface are typically made from synthetic materials such as glass or crosslinked polystyrene, and these can be further modified with an electrical charge potential or immobilized proteins for better cell adhesion [15]. Macroporous materials that support 3D cell culture based on a sponge-like architecture are typically made from dextran, collagen, or alginate. Microporous hydrogels can also be used as microcarriers for 3D cell culture in suspension by providing cells with an encapsulating milieu [16,17,18]. In this approach, the microporous hydrogels entrap the cells in the bulk of the materials during the hydrogel gelation process [19,20,21]. This requires a bioprocessing step that can accommodate the need to procure large quantities of cell-laden hydrogel microcarriers prior to the protein production run. A number of techniques are available for this bioprocessing, including microfluidics, emulsion polymerization strategies, drop-wise polymerization, or polymerization on superhydrophobic surfaces [22,23,24,25,26,27]. A 3D microcarrier-based cultivation in stirred suspension bioreactors offers pronounced advantages over the use of 2D microcarriers, which are limited by surface area and hampered by hydrodynamic or collisional forces associated with stirred suspension bioreactors [28,29,30]. Because the 3D microcarriers made from encapsulating microporous hydrogels take advantage of the volume of the microcarrier for cell growth, the cells growing in the bulk of the hydrogel matrix are also protected from the mechanical stress environment of the bioreactor. This combined advantage allows long-term, semi-continuous culturing which contributes to greater biomass production capacity and scalability [31,32,33]. This advantage is also particularly important for secreted proteins that can diffuse through the hydrogel matrix. Specifically, cells expressing proteins that are secreted into the 3D microporous hydrogel system can be periodically harvested from the culture medium without ever disturbing the cells inside the hydrogel matrix. These advantages can bring 3D hydrogel microcarriers closer to automatization, which is a requirement of the biotechnology industry [28,34,35]. Spherical microgels made from hydrogel biomaterials represent a promising class of microcarriers with the potential to be used in 3D cell cultivation systems [9,36,37]. These microcarriers are receiving more widespread attention owing to their viscoelastic behavior that mimics that of the natural environment of the extracellular matrix (ECM) of anchorage-dependent cells [38]. Hydrogels are classified as either natural, synthetic, or semisynthetic. Naturally derived hydrogels provide adhesion sites, which support cell function; however, they lack the mechanical properties necessary for long-term cultivation in suspension cultures. Synthetic hydrogels are reproducible and exhibit stable mechanical properties that can be easily manipulated but are deficient in bioactive functionality, such as possessing adhesive sites for cell attachment. Semisynthetic hydrogels, which contain both biological and synthetic elements, constitute the optimal combination, making them amenable for suspension culture applications. Specifically, these hydrogels possess stable mechanical properties owing to their synthetic composition and can exhibit cell adhesivity and proteolytic degradability because of their biological domains [39,40,41]. The biological and synthetic composition of a semisynthetic hydrogel used for making microcarriers will depend on the type of cells used for protein production, as well as the requirements of the suspension bioreactor system. Polyethylene glycol (PEG)-based hydrogels have been used extensively in 3D cultures of various cell types for tissue engineering; they exhibit high biocompatibility as well as versatile physical characteristics depending on their weight percent, molecular chain length, and crosslinking density [42]. The PEG component can maintain the structural integrity of the composite structure, thereby ensuring microcarrier stability under the hydrodynamic loads of suspension bioreactors. Conjugating PEG with fibrinogen, the natural substrate for tissue remodeling, provides bioactivity to the PEG hydrogels, which would otherwise be lacking [42]. The gelation process of PEG-fibrinogen (PF) is mild and nontoxic for cells, even when cells are suspended in the precursor solution [43]. The natural cell adhesion motifs and protease cleavage sites on fibrinogen facilitate the stable maintenance of cells while simultaneously allowing dynamic changes such as cell motility and invasion, which are necessary for cell growth and proliferation [44]. These features of PF hydrogels, including biological motifs, chemical crosslinking, and ECM-like physical properties, make them an effective candidate microcarrier material for working with anchorage-dependent cells under the hydrodynamics of bioreactors [42]. PF hydrogels have been used to encapsulate various kinds of mammalian cells, such as sheep aortic smooth muscle cells (SASMCs), human foreskin fibroblasts (HFFs), and human embryonic kidney 293 (HEK293) cells [21,44,45]. The latter, when encapsulated in PF hydrogels, have managed to maintain high cell viability for long durations. This strategy was effective in facilitating the production of a model protein, acetylcholine esterase (AChE), to enhance production capacity several-fold over 2D cultures. Using PF hydrogels as a microcarrier for HEK293 cells and cultivating them in stirred suspension bioreactors led to a higher yield of AChE. It was further found that the PF microcarriers provided a biocompatible environment for cell culture, as indicated by a high percentage of living cells and the formation of cell clusters in the 3D PF microcarriers over the culture duration [21]. In the present study, we aimed to scale up the production of a therapeutic protein called Cripto-1 using recombinantly modified HEK293 cells grown in PF microcarriers. Cripto-1 is a 27 kDa membrane glycosylphosphatidylinositol (GPI)-anchored protein, which belongs to the EGF–CFC protein family and plays an important regulatory role in embryonic development [46]. It can act both as a ligand via the Nodal/Alk4-independent signaling pathway and as a co-receptor for Nodal through activation of the ALK4/ActRIIB receptor complex [5,47,48]. Previous findings have indicated that Cripto-1, which is expressed in myoblasts of regenerative muscles but not in normal muscle fibers, influences myostatin signaling in myoblasts [49,50]. Guardiola et al. showed that Cripto-1 modulates myogenic cell determination and promotes proliferation by antagonizing myostatin. In addition, myostatin and Cripto-1 are expressed in regenerating muscles, and the latter attenuates the myostatin signaling pathway. They also demonstrated that Cripto-1 antagonizes the antiproliferative effect of myostatin on isolated myofibers, promoting myogenic commitment, and simultaneously blocks myostatin activity, promoting the entry of satellite cells into S phase and their commitment to differentiation. The promising results of their study suggest that Cripto-1 is a novel regulator of muscle regeneration and satellite cell progression toward the myogenic lineage [51]. The availability of large quantities of biologically activated Cripto-1 is crucial to the advancement of clinical studies using this promising therapeutic protein. Recombinant Cripto-1 protein cannot be expressed in E. coli, yeast, or CHO cells because of a mutation of the Asn63 residue that prevents post-translational modification (i.e., glycosylation), which affects protein activity in vivo [5]. Hence, our goal was to provide Cripto-1-overexpressing HEK293 cells with optimal conditions in a 3D culture suspension bioreactor, including a supportive microenvironment for improved cell viability leading to enhanced protein yields. For this purpose, PF microcarriers were designed to enable the HEK293 cell survival, proliferation, and secretion of large quantities of the recombinantly expressed therapeutic protein. Cripto-1-overexpressing HEK293 cell lines transfected with His-tagged protein were encapsulated in PF microcarriers and cultivated in stirred suspension bioreactors. The PF microcarriers were designed to possess high mechanical strength to resist the shear forces and biodegradation associated with long-term suspension culture in the bioreactors. The yield of Cripto-1 protein obtained using this system was significantly higher than that obtained with the traditional 2D system. Cripto-1 bioactivity was maintained throughout the production and purification process. Moreover, long-term evaluation showed that the stability and integrity of Cripto-1 were maintained for up to 6 years after production in the PF microcarrier system. ## 2.1. Production of Recombinant Cripto in a 3D PF Microcarrier-Based System Using Stirred Suspension Bioreactors Cripto-overexpressing HEK293 cell lines transfected with a soluble form of His-tagged Cripto-1 protein were encapsulated in the PF microcarriers made from PF and different concentrations of PEG-DA. Three PF formulations were initially tested using 8 mg/mL PF and the addition of $0\%$, $1\%$, and $2\%$ PEG-DA to reach a final shear storage modulus of approximately 250 Pa, 1000 Pa, and 2000 Pa, respectively. The 8 mg/mL PF concentration that was used in this study was based on our previous experiments using this hydrogel system for 3D HEK293 cell cultures [21]. The microcarriers were prepared using a droplet-based formation of PF precursor solution mixed with ~8.5 × 106 cells/mL, deposited on a superhydrophobic surface, and exposed to UV light to initiate a photo-polymerization reaction with the PF. The cells were cultivated within the microcarriers in stirred suspension bioreactors (Figure 1). The live/dead staining of the microcarriers showed individual cells encapsulated within the PF matrix after one day forming colonies of viable cells within the matrix after 7 and 21 days (Figure 2A–C). High-magnification live/dead images for day 1 and day 7 are shown in the supplementary data (Supplementary Figure S5). FITC-rhodamine F-actin staining of the microcarriers showed the progression from single cells dispersed within the PF matrix at day 1 to multicellular colonies at day 7 and day 21 (Figure 2D–F). The initially rounded cells remained rounded in the PF matrix throughout the culture duration, and the proliferating cells formed colonies within the matrix during this time. High-magnification confocal images of an individual cell colony after 21 days with TRITC-rhodamine staining for f-actin and a SYTOX-green nuclear stain underscore the consequence of the PF matrix’s confining effects on cell proliferation and colony formation (Supplementary Figure S6). The number of living cells in the microcarriers was quantified and shown to increase relative to the culture duration and the mechanical properties of the PF matrix (Figure 2G). Based on these initial assessments of viability, we decided to use PF microcarriers made from 8 mg/mL PF and $1\%$ PEG-DA (G’ = 1000 Pa) in all further experiments. Accordingly, cells cultured in these microcarriers in suspension for up to three weeks were quantitatively evaluated for viability during the duration of the Cripto-1 production cycle (Figure 2H,I). The quantitative viability data of the HEK293 cells within microcarriers show consistent levels of around $80\%$ throughout the 21 days in culture, as measured by both trypan blue and PI incorporation assays (Figure 2H,I). The cell-laden microcarriers were used for the Cripto production cycle as illustrated in Figure 1. After an initial 3 days of incubation in growth medium, starvation medium was used to collect the secreted Cripto protein on a daily basis for 4 days, and this cycle was repeated three times (Figure 1A). The collected Cripto was defrosted and pooled for ultrafiltration followed by His-tag affinity chromatography and dialysis (Figure 1B). SDS-PAGE results of the Cripto protein in the fractions of the chromatography obtained at different stages of the process confirmed the presence of the Cripto, as well as the effects of the purification steps (Figure 3A). Seven fractions were collected in total, including the concentrated sample after ultrafiltration, samples from the first and second flowthrough of the affinity chromatography run, two samples after each wash, and two samples eluted from the Ni-NTA resin. A band corresponding to Cripto-1 protein was clearly visible at 27 kDa after elution from the Ni-NTA resin [5] (Figure 3A arrow). Cripto-1 purification on the Ni-NTA column was also verified by the gradual disappearance of the dominant protein bands at around 70 kDa during the chromatography process (Figure 3A). These bands are likely attributed to residues of fibrinogen chains originating from the PF microcarrier-based cultivation system. The production yields of purified Cripto-1 protein obtained from the 3D PF microcarrier system (Cripto(3D)) were quantitively compared to the yields obtained from traditional 2D culture production (Cripto(2D)). The comparative experiments were performed using the same initial number of cells in each culture system. A total of six independent experiments were performed using a seeding of 3.2 × 106 HEK293 cells for each treatment (i.e., 3D versus 2D). The experiments using the 3D system were conducted as per the protocol illustrated in Figure 1, whereas the experiments using the 2D cultivation method were conducted on TCP dishes until the cells approached a density threshold and started to detach from the plate. The concentration of total purified Cripto-1 from all experiments was determined by means of an ELISA. The average production yield of the 3D method (4.5 mg Cripto) was more than one order of magnitude higher than that of the 2D method (0.25 mg Cripto) (Figure 3B). These data represent the amount of protein that was produced with each technique using the fixed initial cell population. The Cripto-1 produced in 3D microcarriers and 2D flasks was also tested for its biological activity using an ELISA kit that assesses its ability to bind to the AlK4 receptor. The binding level of Cripto-1 protein produced by the 3D microcarriers was $93\%$, while the binding level of Cripto-1 produced by the 2D system was $84\%$ (Figure 3C); however, there was no statistically significant difference between the two systems in terms of binding levels as measured by the AlK4 receptor ELISA. ## 2.2. In Vitro Functional Activity of Purified Recombinant Cripto To demonstrate the proliferative effect of the recombinantly produced Cripto-1 on myoblasts, a BrdU incorporation assay was performed alongside cell counting experiments. C2C12 myoblasts were cultured for 48 h in starvation medium ($0.5\%$ FBS) containing either the Cripto-1 produced in 3D PF microcarriers (Cripto(3D)) or commercially available Cripto-1 purchased from R&D Systems (Cripto(R&D)). Two control groups were also examined, including myoblasts grown in starvation medium containing basic fibroblast growth factor (bFGF) as a proliferation inducer (positive control) and myoblasts grown in starvation medium with no supplements (negative control). The results confirmed that C2C12 proliferation was significantly increased in the presence of Cripto(3D) compared with the negative control group. Myoblasts treated with Cripto(R&D) or bFGF displayed a significantly higher level of BrdU incorporation when compared to the negative control group but lower levels of BrdU incorporation when compared to the Cripto(3D) treatment (Figure 4A). BrdU incorporation results comparing Cripto(3D) and Cripto(R&D) to negative controls showed a dose-dependent increase in the proliferative response of C2C12 myoblasts to Cripto-1 (Figure 4B). Cell counting experiments quantifying the number of live cells after each treatment showed similar trends among the different treatment groups (Figure 4C). The biological activity of purified Cripto-1 was investigated by staining the treated myoblasts with the proliferation marker Ki67. Bright field and fluorescence images of the cells were acquired and used to assess the effects on the cells. The bright field images showed that cells treated with Cripto(3D) were more confluent than the untreated cells (Figure 5A). Fluorescence images were quantified for the percentage of Ki67-positive cells; the number of positive cells treated with Cripto(3D) was the highest of all the treatments (Figure 5B). To further assess the bioactivity of the Cripto-1 produced in the PF microcarriers, we measured the effects of the protein on satellite cell differentiation. Satellite cells were grown in low-activation medium ($10\%$ HS) supplemented either with Cripto(3D) or Cripto(R&D) or without supplement as a negative control. Double immunostaining for the myogenic differentiation markers myosin heavy chain (MyHC) and myogenin (MyoG) was performed after 24, 72 h, and 7 days (Figure 6A). MyoG is a myogenic marker that is expressed during differentiation. High levels of this marker indicate that the differentiation capacity of the cells is elevated due to the presence of the Cripto proteins. These results are consistent with the previous findings of Guardiola et al.; they observed that treatment with Cripto increases the tendency of satellite cells toward differentiation and expression of MyoG [51]. The rates of myogenic differentiation were evaluated by measuring the fusion index, which is the percentage of nuclei within MyHC-positive myotubes (i.e., those with ≥2 nuclei) out of the total number of nuclei. Quantitative analysis of the images showed significantly increased fusion in cells treated with Cripto(3D) compared to the negative controls at all time points (Figure 6B). The quantification of MyoG-positive cells at each time point also showed a significantly higher level of MyoG in cells treated with Cripto(3D) compared to the negative control cells ($p \leq 0.05$, n ≥ 3) (Figure 6C). MyoG levels in cells treated with Cripto(R&D) were also significantly higher compared to the negative control cells at the early time points ($p \leq 0.05$, n ≥ 3) (Figure 6C). ## 2.3. Stability and Binding Capacity of Recombinant Cripto during Long-Term Storage The shelf life and stability of the recombinant Cripto(3D) protein was evaluated for up to six years after production. After every manufacturing process, the batch of the purified Cripto-1 protein was snap-frozen in liquid nitrogen and stored at −80 °C. Protein samples from batches produced at different time points were thawed and quantified for their stability and integrity. The SDS-PAGE analysis results for Cripto-1 samples from different batches are shown in Supplementary Figure S7A. In all samples, the bands corresponding to Cripto-1 proteins that were stored for different periods showed little evidence of protein degradation that may have occurred during storage. These results are consistent with data obtained in a conducted stability study that used an ELISA to show that Cripto-1 protein retains its ability to bind AlK4 receptors when stored at −80 °C for up to six years (Supplementary Figure S7B). ## 2.4. Discussion Recent clinical progress in novel protein therapeutics has drawn renewed interest in establishing more robust methods for producing complex recombinant proteins in suspension bioreactors, particularly with mammalian cell lines. These efforts involve a range of mammalian cell types being engineered to overexpress the therapeutic protein, which is either secreted directly into the culture medium or retained within cells and extracted afterwards [21,52,53,54]. In this study, our aim was to optimize and validate a PF hydrogel 3D microcarrier for the production of large quantities of Cripto-1 protein by employing Cripto-overexpressing HEK293 cells grown in suspension bioreactors. For this purpose, 3D microcarriers made from semisynthetic PF hydrogels were designed to provide both physical stability and bioactivity to enable HEK293 cell survival and proliferation within the microcarriers for up to 21 days in suspension culture. The ability of the cells to secrete large amounts of Cripto protein into the culture medium was an equally important feature of the PF microcarrier system that we sought to investigate. Moving from 2D culture to 3D microporous hydrogel microcarriers is important when large amounts of therapeutic protein are required, such as in the case of Cripto [51,55,56]. However, there are some challenges when working with 3D hydrogel microcarriers. In particular, the composition of the microcarriers can affect cell survival and protein production. Studies have shown that mammalian cell viability, growth potential, and phenotype can be altered by adjusting the physical and biological features of the encapsulating hydrogel [43,57]. Premised on the concepts of controlling cell phenotype using these features, we first had to identify PF material properties that provided a good growth matrix for cultivating Cripto-overexpressing HEK293 cells in stirred suspension bioreactors. Specifically, the PEG polymer composition of the PF was adjusted to endow the hydrogel with sufficient mechanical strength to resist the shear forces associated with the stirred suspension bioreactors for a duration of up to 21 days without impeding the proliferation or survival of the cells. Three compositions of PF hydrogels were tested with HEK293 cells in 3D culture in a preliminary screening experiment, including a low modulus, intermediate modulus, and a high modulus formulation. These formulations all contained 8 mg/mL PF. Additional PEG-DA crosslinker (0–$2\%$ w/v) was added to the PF to increase the hydrogel modulus from G’ = 250 Pa to G’ = 1000 Pa and G’ = 2000 Pa (see Supplementary Figure S1). The preliminary screening experiment demonstrated that the modulus of the PF affected the proliferation of the HEK293 cells after 7 days in culture (Figure 2H). The higher modulus formulations appeared to reduce the proliferation of the HEK293 cells in the 3D culture. Additionally, of the three compositions that were tested, only the low modulus formulation was not stable in the bioreactor for the full 21 days. The intermediate and high modulus formulations were stable for 21 days; however, because the high modulus formulation impeded cell proliferation more than the intermediate modulus formulation (Figure 2G), we chose to continue all experiments using the intermediate modulus formulation. The composition of the biological component in the PF hydrogel provided adequate bioactivity to allow for cell survival within the microcarrier matrix for the duration of the 21 days in suspension culture. The quantitative viability of the cells was measured using a trypan blue exclusion assay and a PI assay. Both techniques confirmed viability of greater than $80\%$ throughout the culture period. These data are consistent with previous experiments using PF hydrogels to culture other cell types within the gels [42,43,58,59]. The UV photopolymerization in this study may have contributed to the loss of cell viability immediately after gel formation. We and others have verified the cytocompatibility of UV photopolymerization [60], but we cannot exclude the possibility that either the Irgacure2959 and/or the 365 nm UV light adversely affected the HEK293 cells. In terms of cell morphology, the HEK293 cells inside the hydrogel were initially rounded when the hydrogel was formed and remained rounded throughout the duration of the culture. Although anchorage-dependent, the HEK293 cells do not appear to spread within the PF matrix upon their encapsulation. Typically, stromal cells cultured in PF hydrogels with similar properties exhibit morphogenesis, leading to cell spreading within the matrix. We have previously cultured fibroblasts in PF hydrogels with evidence of cell adhesion, including the formation of focal adhesions [61]. Therefore, we assume that cell adhesion is possible between the HEK293 cells and the PF matrix, although we did not evaluate the formation of focal adhesions in this study. Cell morphogenesis may also be influenced by the ability of the cells to proteolytically break down the PF matrix. In hydrogels that are more highly crosslinked with additional PEG-DA, this proteolysis can be hampered [62]. In this study, we cultured the HEK293 cells in PF hydrogels, with slow proteolytic degradation owing to the additional PEG-DA that was added [61]. Hence, the HEK293 cells growing in the PF material that supports cell adhesion may more slowly degrade PF, which can explain why they appear to have a rounded morphology. This PF formulation was chosen to prevent the hydrogels from degrading prematurely during the culture period. Additionally, it is important to note that the HEK293 cells do not necessarily exhibit mesenchymal cell properties and may therefore have a limited ability to express a non-rounded cell morphology in 3D culture. This could be attributed to a limited production of proteases to break down the PF matrix, thus reducing their ability to form cellular extensions within the PF hydrogel milieu. As a preliminary proof of concept with the 3D PF microcarriers, Cripto-1 production yields from 3.2 × 106 HEK293 cells were measured at nearly 4.5 mg of Cripto after 21 days in culture. Although these values are not optimized, they represent an initial assessment of the potential yields achieved by this system. When compared to the cell-normalized yields obtained with conventional 2D tissue culture dishes (i.e., TCP), the 3D methods proved far superior. The cells in the 2D system approached a density threshold, which led to cell detachment from the plate, whereas cell proliferation in the 3D system was controlled by the mechanical properties of the PF microcarriers [21]. Although the 2D and 3D cultures can be further optimized, these results stand in agreement with previously reported data suggesting that cell proliferation is high and uncontrolled in a 2D system compared to the number of cells being maintained at a steady state in the 3D system [21]. More optimizations can also be performed on the 3D system, including optimization of the properties of PF for longer culture durations, optimization of growth/harvest cycle durations, changes in the type of bioreactor used, and control of bioreactor growth conditions using continuous monitoring for optimal production [9,63]. Consequently, the matrix modulus of G’ = 1000 Pa was chosen for the Cripto production experiments based, in part, on limiting cell growth within the matrix. In addition to the high yields of 3D microcarrier Cripto-1 production, high-throughput purification is an important part of bioprocessing. Cripto-1 purification can become a bottleneck in cost-effective production of biotherapeutics. We addressed this issue using a purification strategy that is based on the creation of a fusion Cripto-1 protein with His-tag sequences that are biologically active [5,64] and which can be purified via affinity chromatography. This is a widely accepted strategy for the purification of recombinantly expressed clinically useful proteins [5,65,66,67]. Other techniques have been applied to recombinantly express and purify His-tag-fused Cripto-1 protein, particularly in non-mammalian expression systems such as E. coli. A recent study optimizing the production of recombinant soluble human Cripto-1 protein using the T7 expression system was performed by Senso et al. [ 67]. They found that a functional form of soluble Cripto-1 was difficult to obtain because of the 12 cysteine residues in the protein, which impairs the folding process. They developed a special purification process to obtain His-tagged Cripto-1 protein from inclusion bodies under denatured conditions [65]. The purification process included not only a Ni-NTA column step, but also a CDR-modified cellulose column to remove cellular debris. This step was required, in part, because the protein in E. coli is mostly confined to the inclusion bodies. However, this was not required in the HEK293 cells cultured within the 3D microcarriers as the protein is secreted, modified, and then collected directly from the medium. In general, the purification of Critpo-1 secreted outside the membrane is easier than the purification of proteins from cell lysates [5,68,69], underscoring the advantage of producing Cripto-1 with a HEK293 cell expression system in 3D microcarriers. Beyond the use of affinity chromatography, we applied a couple of other well-established purification steps to achieve the high-quality, high-throughput recombinant protein yields. The first step involved concentrating the Cripto-1 protein in the collected culture medium via ultrafiltration to prepare the solution for chromatography. The second step involved the elution of the His-tagged Cripto-1 protein in a Ni-NTA column. The final step was to dialyze the purified Cripto-1 against PBS. The Ni-NTA affinity column demonstrated high capacity for loading the target protein, and no destructive effects were observed during the elution. Overall, the broad band at 27 kDa, which appeared in the first elution fraction, coupled with the absence of bands at 27 kDa in other fractions, confirms the high efficiency of the Cripto purification steps. Our study has also demonstrated that the amount of active protein increased when compared to 2D production of the protein. This suggests that both of the 3D production and purification processes were delicate enough to preserve the natural biological structure, which is strongly related to bioactive function. In terms of measuring the bioactivity of Cripto-1, previous studies have shown that Cripto-1 plays a dual role by both increasing the proliferation rate of myoblasts and promoting satellite cells toward myogenic differentiation [51]. Cripto-1 binds Nodal and activates ALK4 signaling pathways under normal physiological conditions [70]. Hence, we characterized the activity of the expressed protein using a proliferation assay with the C2C12 myoblast line and a myogenic differentiation assay on primary myoblasts. The highest proliferation rate was found in myoblasts treated with Cripto(3D) and Cripto(R&D). In addition, a dose-dependent pattern of proliferative activity was shown in C2C12 cells exposed to Cripto(3D) and Cripto(R&D). These results were consistent with a previous in-depth study that demonstrated how Cripto-1 regulates muscle regeneration by attenuating the TGF-β ligand myostatin signaling pathway. According to this study, by antagonizing myostatin, Cripto-1 promotes myogenic cell proliferation, and by blocking myostatin activity, Cripto-1 increases the tendency of satellite cells to differentiate [51]. Based on these findings, we further investigated the effect of Cripto-1 on muscle satellite cell differentiation. The results here showed an enhanced differentiation rate for cells treated with Cripto(3D) and Cripto(R&D), which confirms the dual role of Cripto-1 as a regulator that increases the proliferation rate of myoblasts and promotes satellite cells toward myogenic differentiation. Not only was differentiation enhanced in the presence of Cripto-1 (both from 3D and R&D sources), but cell confluency in the differentiation assay was greater (the initial concentration of cells that were seeded was the same for all treatments). This result implies that the presence of Cripto affected proliferation as well as differentiation, although the enhanced differentiation could be also explained as a consequence of the increased proliferation (rather than a direct effect on differentiation). In the results of the bioactivity assays, a slight difference in bioactivity in favor of Cripto(3D) was observed; however, this elevated bioactivity of Cripto(3D) may be attributed to the fibrinogen residues from PF microcarriers that are not removed during purification steps (see Figure 3A). Future improvements to the purification process would thus be required to remove all fibrinogen fragments from the product so that Cripto(3D) could be used in a clinical setting. Storage stability is another important consideration when evaluating the commercial potential of a specific protein in the pharmaceutical market. A critical concern associated with therapeutic protein production is their ability to survive after the manufacturing process for long-term storage before clinical administration. As a result of extended storage durations, protein quality can be jeopardized by degradation and conformational changes [71,72]. The results presented in this work confirm that Cripto-1 protein produced using the 3D cultivation system is capable of maintaining its stability and integrity for at least 6 years. Data on protein characteristics obtained by SDS-PAGE and ELISA assays applied on samples of Cripto-1 batches from different time points show that protein stability and bioactivity were not affected by the long-term storage durations. Specifically, structural integrity was identified by the uniform bands in the SDS-PAGE results for samples from all time points, indicating that no protein degradation was detected in samples stored for up to 6 years. The ability of Cripto-1 to bind to the AlK4 receptor indicates that it retained its active conformation during long-term storage at −80 °C. ## 3. Conclusions A 3D PF microcarrier-based cultivation system was designed for the production of Cripto-1 using HEK293 cell lines in stirred suspension bioreactors. The PF microcarrier maintained its mechanical integrity for up to 21 days in the stirred bioreactor. The mechanical properties of the PF did not prevent the growth of the HEK293 cells, nor did they obstruct the release of the constitutively expressed Cripto-1 protein from the microcarrier into the culture medium. The bioactivity of the purified Cripto-1 protein obtained from 3D PF microcarrier cultures was equivalent to commercially available Cripto-1, despite a near 10-fold increase in production yields obtained from the 3D system compared to conventional 2D production. The 3D PF microcarriers can thus help streamline the biomanufacturing of Cripto-1 in HEK293 cell lines and possibly improve production yields in other therapeutic proteins that require anchorage-dependent mammalian cell expression systems. ## 4.1. Synthesis of PEG-Diacrylate (PEG-DA) and PEG-Fibrinogen (PF) PEG-diacrylate was synthesized as described elsewhere [44]. Briefly, linear PEG-OH with an average molecular weight of 10 kDa (Fluka, Buchs, Switzerland) was reacted with acryloyl chloride (Merck, Darmstadt, Germany) at a molar ratio of 1.5:1 relative to OH groups in dichloromethane (Aldrich, Sleaze, Germany) and triethylamine (Fluka, Buchs, Switzerland). The final product was precipitated in ice-cold diethyl ether (Frutarom, Haifa, Israel), followed by vacuum drying for 48 h. The degree of acrylation was quantified by NMR (nuclear magnetic resonance spectroscopy). PEGylated fibrinogen was prepared by conjugating PEG-DA, via Michael-type addition, with denatured, reduced fibrinogen chains according to previously described protocols [44,73]. Briefly, a 7 mg/mL solution of bovine fibrinogen (ID bio, Baixas, France) in 150 mM PBS containing 8 M urea was reacted with Tris(2-carboxyethyl) phosphine hydrochloride (TCEP–HCl) (Sigma–Aldrich). The molar ratio of TCEP–HCl to fibrinogen cysteines was 1.5:1. Once the protein was dissolved, PEG-DA in a solution of PBS and 8 M urea (280 mg/mL) was added at a molar ratio of 4:1, and the reaction was carried out for 3 h at room temperature in the dark. The PEGylated fibrinogen protein was then precipitated by adding 4 volumes of acetone (Bio-lab) and was re-dissolved in PBS-urea to the desired concentration, followed by dialysis (Spectrum 12–14 kDa MW Cutoff, USA) against 150 mM PBS for 24 h at 4 °C. Finally, the fibrinogen concentration in the product was measured by a NanoDrop spectrometer (A-280 nm, PF coefficiency-15.1) and the degree of PEG substitution was calculated according to published protocols [73]. Rheological parameters were calculated using a strain-rate-controlled shear rheometer (AR-G2, TA Instruments, New Castle, DE, USA) with a 20 mm parallel-plate geometry. Each measurement was carried out using 200 µL of hydrogel precursor solution containing $0.1\%$ w/v Irgacure2959 photoinitiator. All rheological experiments were performed in triplicate. Time sweep oscillatory tests were conducted under a constant strain amplitude of $1\%$ and a constant frequency of 2 Hz, which was determined to be in the linear viscoelastic region (LVR) of the PF hydrogels (data not shown). The shear storage and loss moduli (G’, G″) of the hydrogels was measured for each batch of PF materials (Supplementary Figure S1A) [43]. Increasing the concentration of additional PEG-DA was used to increase the G’, as described elsewhere (Supplementary Figure S1B,C) [33]. Three formulations of PF hydrogels were identified for screen testing, including a low modulus (G’ = 250 Pa), an intermediate modulus (G’ = 1000 Pa), and a high modulus (G’ = 2000 Pa) gel. ## 4.2. Cell Line Maintenance and Expansion in 2D Culture Recombinant Cripto protein was produced in HEK293 cells as a Histidine-tagged (C-terminus 6xHis) fusion protein lacking the COOH-terminal amino acid residues +156 to +172 of Cripto (Minchiotti et al., 2011). Briefly, the Cripto-His (sequence from nucleotide −5 to +156 of the Cripto cDNA) expression vector was obtained via PCR using the complete Cripto cDNA as a template and the appropriate oligonucleotides [68]. The amplified fragment was cloned into a pcDNA3-His expression vector containing the 6XHis tag and the neomycin (geneticin) resistance gene for the selection of stable cell lines. HEK293 cells were transfected at $50\%$ confluence by the calcium phosphate method using 10 μg of plasmid DNA; twenty-four hours after transfection, the cells were incubated with geneticin (G418)-containing medium for 3 weeks. Following the selection process, resistant cells were tested for recombinant Cripto-1 protein production via Western blotting [74]. Cripto-His is released to the medium as it lacks the COOH-terminal hydrophobic lipid anchor (GPI-anchor) [5]. Cell culture was performed in humidified incubators at 37 °C and in a HEPA-filtered atmosphere of air and $5\%$ CO2. Cells were stored frozen in liquid nitrogen until use. At the onset of each experiment, the frozen cells were thawed and 4 × 106 cells were seeded on round tissue culture plates (150 mm diameter × 20 mm height) in growth medium (ScienceCell) supplemented with $10\%$ fetal bovine serum (FBS) (Biological Industries, Haemek, Israel) and $1\%$ penicillin–streptomycin–ampicillin (Biological Industries, Israel). After 24 h of incubation, growth medium was changed to selection medium containing 200 µg/mL G418 (Gibco, Grand Island, NY, USA). Cells were split and expanded for 10–12 days in selection medium for the harvesting of large amounts of cells prior to cultivation in suspension bioreactors. ## 4.3. Cell Seeding in 3D PF Microcarriers Cripto-overexpressing HEK293 cell lines were harvested from 40 tissue culture polystyrene (TCP) dishes at ~$80\%$ confluence by repeated pipetting to detach them from the surface. The suspended cells were then centrifuged for 2–5 min at 300× g to obtain a cell pellet. The pellet of cells was suspended with the PF hydrogel solution until there was no aggregation in the solution. Droplets of PF precursor solution with cells (7.5 × 106–10 × 106 cells/mL) were introduced on a superhydrophobic surface (fumed silica-coated glass plates prepared as described elsewhere [20,33]) using a 23-gauge syringe needle. The droplets of cells in hydrogel precursor were crosslinked under long-wave UV light (356 nm, 4–5 mW/cm2) for 1.5 min in the presence of $0.1\%$ w/v of a photoinitiator (Irgacure®2959, Ciba, Basel, Switzerland) and then washed with growth medium into a 500 mL DURAN® GL 45 bottle. The estimated volume of each bead (radius = 1.25 mm) was approximately 0.008 mL; therefore, there were approximately 6 × 104 to 8 × 104 cells per microcarrier. ## 4.4. Cell Viability and Imaging The viability of the cells in the PF microcarriers was assessed qualitatively by fluorescence imaging using a calcein/ethidium assay. The cells in the microcarriers were stained with 4 mM calcein acetoxymethyl and 2 mM ethidium homodimer-1 (EthD). Calcein penetrates the cell membrane and emits a green fluorescence signal under the enzymatic activity of esterase (emission maximum at 515 nm). This green signal indicates the cells are alive. In contrast, EthD can only penetrate through the disrupted membranes of dead or dying cells. EthD attaches to the nucleic DNA of dying/dead cells and emits a red fluorescence signal at 620 nm. The cells seeded in 3D (PF microcarrier) were stained for 50 min on an orbital shaker at 37 °C with $5\%$ CO2 and were washed with PBS at a ratio of 1:1. Stained constructs were visualized using a Nikon (TS100) fluorescence microscope with a ×2 or ×10 objective and imaged with a Nikon (DS-Fi1) camera. The quantitative viability of the encapsulated HEK293 cells was evaluated using the trypan blue exclusion assay and propidium iodide (PI) assay. PF microcarriers were washed with PBS and incubated at 37 °C with collagenase (0.5–1 mg/mL, Sigma) for 15–60 min to dissolve the 3D gel phase. After dissolution, the solution passed through a filter (70–100 μm) to dispose of large aggregates of cells and clumps of PF gel phase residues. The remaining pellet of cells was suspended with PBS to obtain the desired cell concentration of 5 × 105 to 2 × 106 cells per 1 mL PBS. For the trypan blue assay, we utilized the fact that the cell membrane is selective to the entry of trypan blue; thus, penetrating and staining of the cells measures only the dead cell population. A volume of 10 μL containing a ratio of 1:1 PBS/cells and trypan blue ($0.4\%$ w/v) was loaded in an automated cell counter (Countess®-Invitrogen). The obtained results show a calculation of the number of cells, the percentage of living and dead cells, and the distribution of cell size. An example of this analysis data is provided in Supplementary Figure S2. For the PI assay, PI penetrates only dead cells, and once the dye is bound to nucleic acids, its fluorescence is enhanced up to 20- to 30-fold. The cells were removed from the microcarriers as described above and suspended in 1 mL PBS for PI staining. The staining solution was prepared by mixing 40 μg/mL PI reagent (Sigma) for DNA staining and 100 μg/mL RNases to exclude the staining of nucleic acids derived from RNA. After 10 min of incubation at 37 °C, cells were kept on ice to reduce the ongoing process of apoptosis. All samples were measured by flow cytometry (LSR- II, BD Biosciences). A positive control for dead cells was prepared using PI staining with $70\%$ ethanol (Supplementary Figure S3). Blank samples of unstained cells were used as negative controls (Supplementary Figure S4). The gating strategy involved the use of positive and negative controls against the live cell population. The percentage of viable cells was calculated from the number of PI-positive cells in the entire population. The data were analyzed using a program called FCS Express 4 Plus Research Edition [75]. An example of these analysis data is provided in Supplementary Figures S3 and S4. For microscopic evaluation of the HEK293 cells within the PF microcarriers, the cells were fixed in $10\%$ formalin in PBS (Sigma) and stained for filamentous actin (f-actin) using a TRITC-phalloidin or FITC-phalloidin stain (sigma) and a nuclear stain, DAPI (sigma) or SYTOX-green (Thermo-Fisher), with both procedures performed according to the manufacturer’s recommendations. Briefly, the cells were permeabilized with $0.1\%$ Triton X-100 in PBS (Sigma) for 10 min. The cells were then stained with FITC- or TRITC-labeled phalloidin (FTIC-phalloidin or TRITC-phalloidin, 1 μg/mL, Sigma) for 1 h at room temperature and counterstained through the addition of DAPI or SYTOX-green for 30 min. The cells were then washed three times with PBS at room temperature and then left overnight at 4 °C. The stained cells were imaged via confocal microscopy (Zeiss LSM700, Oberkochen, Germany) at a resolution of 1024 by 1024 pixels using a ×20 objective (numerical aperture = 0.45) and a z-step size of 2.3 μm per layer up to a depth of 200 μm. ## 4.5. Cultivation of Cells in Spinner Flasks Cells in crosslinked PF microcarriers were cultured in suspension within GL 45 stirred reactors (DWK Life Sciences, Millville, NJ, USA) for up to 21 days. Typically, 12–15 mL of PF microcarrier was placed in 500 mL DURAN® GL 45 bottles containing 200–300 mL of medium. The proportion of PF volume to culture medium volume in the bioreactor was normally 12 mL/280 mL. The estimated number of microcarriers in 280 mL of each bioreactor medium was approximately 1500 microcarriers, or 5 microcarriers in each ml of culture medium. The magnetic stirrer was set to 30–40 revolutions per minute (RPM). Two vents were used for air flow into the bioreactor. Increasing gas exchange in the medium was achieved with air pumps while keeping the conditions sterile using a 0.2 μm in-line pre-filter (Whatman PolyVENT™). The PF microcarriers containing HEK293 cells were subjected to a prescribed growth/starvation cycle for periodic harvesting of the secreted Cripto protein. Specifically, 3 days of growth medium was followed by 4 days of starvation/harvesting. Starvation medium included serum-free DMEM that was phenol-free and which contained $1\%$ penicillin–streptomycin–ampicillin. After each 4-day starvation cycle, the medium was replaced with a fresh growth medium and incubated for an additional growth/harvesting cycle resulting in a total of three iterations (see Figure 1). This iterative cycling was chosen based on an initial optimization study evaluating cell viability in the microcarriers during long-term suspension culture in the bioreactors (data not shown). During the harvesting phase, the medium was collected every 24 h and further processed prior to protein purification. Briefly, cell debris was removed via centrifugation (10 min at 3000 RPM) and the harvested media was frozen at −80 °C until the beginning of the purification process. ## 4.6. Cripto Production in 2D Cultivation System For the production of Cripto in a 2D culture system, the selection medium was removed from 40 TCP dishes and replaced with a starvation medium. After incubation at 37 °C and $5\%$ CO2 overnight, the medium was collected and centrifuged. The harvested medium was stored at −80 °C prior to protein purification. The medium was then replaced with a fresh starvation medium, and these steps were repeated for three more days until the cells started to detach from the TCP surface. ## 4.7. Cripto Purification, Quantification, and Detection Cripto protein was purified using a three-step procedure consisting of ultrafiltration, chromatography, and dialysis. A Centramate™ tangential flow filtration cassette membrane with a molecular weight cutoff of 10,000 Dalton was used for concentrating the Cripto from the collected culture medium (Pall Corporation, New York, NY, USA). This was followed by His-tag affinity chromatography with a Ni-NTA column (Qiagen, Hilden, Germany) to purify the Cripto. Finally, dialysis against PBS (130 mM) was conducted for 24 h at 4 °C, with four changes of the dialysis buffer performed to remove any impurities from the elution buffer. The purified Cripto solution was then frozen at −80 °C and freshly thawed for use at the beginning of each experiment. To qualitatively monitor purification efficiency, the Cripto protein fractions were withdrawn during the purification process and visualized by SDS-PAGE and Coomassie blue staining. Fractions were collected from the following steps: after concentration with ultrafiltration, flowthrough after the first and the second binding to the resin, each wash step, and from samples eluted from the Ni-NTA resin. To measure the concentrations of both the total purified protein and the active protein, an assay was carried out using a commercial ELISA kit (DuoSet, R&D systems, Minneapolis, Minnesota, United States). Serial dilutions of the concentrated protein were put into 96-well plates coated with either mouse Cripto antibody (for total concentration) or recombinant mouse activin receptor IB/Fc (for active protein concentration) (R&D systems AF1538 and 1477-AR, respectively). Briefly, 96–well plates were coated with 0.5 ng/mL of either activin RIB/ALK-4 (R&D systems 1477-AR) or Cripto antibody (R&D systems AF1538) in PBS (pH 7.5) overnight at 4 °C. After being washed three times, unbinding sites were blocked with $1\%$ PBS-BSA for 2 h at room temperature (RT). The plates were then washed three times and Cripto samples were added (300 μg) and incubated for 2 h. The plates were incubated with 0.5 μg/mL His-tag biotinylated antibodies (R&D systems BAM 050) in PBS-Tween for 1 h at 37 °C and then for 1 h at RT. Finally, the plates were incubated for 1 h at RT with Streptavidin-HRP complex conjugated with HRP (R&D systems DY998). The plates were then developed with tetramethylbenzidine hydrogen peroxide substrate (R&D systems DY999), and absorbance was read at 450 nm on a Benchmark microplate reader (Bio-Rad Laboratories). Relative binding levels were determined by dividing the amount of Cripto that was bound to the activin RIB/ALK-4 antibody by the amount of Cripto that was bound to the Cripto antibody. ## 4.8. BrdU Incorporation Cell Proliferation Assay A cell proliferation assay was used to measure the bioactivity of the recombinantly expressed Cripto, as has been more fully described elsewhere [51]. This assay was specially used to compare quantitative cell proliferation in the presence of Cripto produced using the PF microcarriers (Cripto(3D)) and that of commercially available Cripto purchased from R&D systems (Cripto(R&D)). Briefly, C2C12 skeletal muscle myoblasts were seeded on 96-well plates (104 cells/well) in growth medium for 4 h. The growth medium was then replaced with starvation medium (DMEM, $0.5\%$ FBS, $1\%$ pen/strep) and incubated overnight in order to synchronize the mitotic cycle of the C2C12 cells. At the onset of the proliferation assay, the starvation medium was supplemented with either 500 ng/mL Cripto(3D) or Cripto(R&D). Two additional control groups were evaluated, including cells grown in starvation medium supplemented with basic FGF, as a positive control, and cells grown only in starvation medium, which was used as a negative control. For the dose–response assay, cells were treated with starvation medium containing either Cripto(3D) or Cripto(R&D) at increasing concentrations from 5 to 500 ng/mL. Cell proliferation was quantified using a BrdU Cell Proliferation Assay kit (Cell Signaling, 6813) according to the manufacturer’s instructions. ## 4.9. Ki67 Immunostaining Proliferation Assay A Ki67 immunoassay was used to quantify cell proliferation in the presence of recombinantly expressed Cripto. Briefly, C2C12 cell viability was first quantified by staining cells with trypan blue (Biological Industries, Haemek, Israel) and measuring viability with an automated cell counter (Countess® Invitrogen). The C2C12 myoblasts were then seeded on 24-well plates (3 × 104 cells/well) and treated with starvation medium supplemented with either 500 ng/mL Cripto(3D) or Cripto(R&D). The positive control group included C2C12 cells grown in starvation medium supplemented with basic FGF, and the negative control group included C2C12 cells grown in starvation medium only. After 42 h in culture, the cells were fixed with $4\%$ paraformaldehyde and permeabilized with $1\%$ Triton X-100 in PBS. Cells were blocked in $5\%$ BSA in PBS and incubated with rabbit polyclonal Ki-67 antibody (1:40, Abcam 15580) at 4 °C overnight. Subsequently, cells were incubated with donkey anti-rabbit AlexaFluor 555 (1:400; Invitrogen) and DAPI (1:1000; Invitrogen) at room temperature for 1 h. Finally, cells were mounted in mounting medium and visualized under a Zeiss LSM 700 confocal microscope (Carl Zeiss, Oberkochen, Germany). Bright field images were acquired using an inverted fluorescence microscope (Nikon Eclipse TS100, Nikon, Tokyo, Japan), a digital camera (Digital Sight, Nikon, Japan), and Nikon Nis-Elements F3.00 software (Nikon, Japan). ## 4.10. Muscle Satellite Cell Differentiation Assay Muscle satellite cells were isolated from 6 muscles of adult mice according to published protocols [76,77]. The cells were plated at 1.9 × 104 cells/well in 24-well gelatin-coated cell culture plates in BIO-AMF-2 medium (Biological Industries Ltd.). After 72 h, the medium was replaced with low-activation medium (DMEM/F12, $10\%$ horse serum (HS), $1\%$ pen/strep) and the cells were subjected to one of three different treatments: 500 ng/mL Cripto(3D), 500 ng/mL Cripto(R&D), or the control (low-activation medium only). The cells were incubated for 24 h, 72 h, and 7 days, fixed in $4\%$ paraformaldehyde, and stained with DAPI, MyoG, and MyHC. All cells were imaged using a Zeiss LSM 700 confocal microscope (Carl Zeiss, Oberkochen, Germany). The percentage of MyoG-positive cells and MyHC-positive cells, as well as the fusion index, was measured and analyzed using Image-J software version 1.530. ## 4.11. Stability and Functionality of Cripto over Time The stability and bioactivity of the Cripto(3D) protein, taken from batches that were produced at different time points, was determined using SDS-PAGE and Coomassie blue staining, as well as ELISA assays. Briefly, samples of Cripto(3D) that were stored for up to 6 years at −80 °C were loaded into NuPAGE Tris-base $10\%$ gels (Life Technologies, Australia) at a loading concentration of 5µg protein per band, and SDS-PAGE was performed following the manufacturer’s protocol. The effect of shelf life on Cripto functionality was determined using an ELISA bioactivity kit (R&D systems, DuoSet) according to the manufacturer’s protocol, as described in detail above. ## 4.12. Statistical Analysis All data were obtained from at least three independent experiments (n ≥ 3) and expressed as mean ± standard deviation (S.D.). Statistical analysis was performed using the un-paired Student’s t-test. 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--- title: Development and Testing of the Smart Healthcare Prototype System through COVID-19 Patient Innovation authors: - Po-Chih Chiu - Kuo-Wei Su - Chao-Hung Wang - Cong-Wen Ruan - Zong-Peng Shiao - Chien-Han Tsao - Hsin-Hsin Huang journal: Healthcare year: 2023 pmcid: PMC10048738 doi: 10.3390/healthcare11060847 license: CC BY 4.0 --- # Development and Testing of the Smart Healthcare Prototype System through COVID-19 Patient Innovation ## Abstract Since the outbreak of the novel coronavirus disease 2019 (COVID-19), the epidemic has gradually slowed down in various countries and people’s lives have gradually returned to normal. To monitor the spread of the epidemic, studies discussing the design of related healthcare information systems have been increasing recently. However, these studies might not consider the aspect of user-centric design when developing healthcare information systems. This study examined these innovative technology applications and rapidly built prototype systems for smart healthcare through a systematic literature review and a study of patient innovation. The design guidelines for the Smart Healthcare System (SHS) were then compiled through an expert review process. This will provide a reference for future research and similar healthcare information system development. ## 1. Introduction The outbreak of the novel coronavirus disease 2019 (COVID-19) changed the entire world. As of January 2023, more than 750 million people have been diagnosed, nearly 7 million have died from the outbreak, and the world economy has been devastated [1]. Even though many countries have lifted restrictions, it is expected that the global economy, and the lives of individuals, will continue to be affected. Given the speed and impact of epidemic diseases such as COVID-19, smart healthcare systems can effectively assist healthcare workers, reduce healthcare resource consumption, and assist healthcare teams and government agencies in building healthcare ecosystems to assist in policy decision-making. It is important to discover and understand the needs of system users through participatory research, and then evaluate and adjust the design of the system to optimize its usability [2,3]. During this epidemic, people have seen their lives change dramatically overnight, but more and more people are willing to devote themselves to their professions as amateur data scientists, using relevant technologies and data graphical analysis to assist in the management of medical decision-making. In the past, information system design and development were mainly applied to various aspects of product and service process design. Ulrich focused on system design and development in the design of system functions [4] and aesthetic style [5]. Concurrently, companies were thinking about extending the design and development of information systems by introducing a human-centered approach to these R&D design processes. This approach needs to focus on the needs of system users and their use process rather than the attributes of the system itself, which is called ‘design thinking’ [6]. In recent years, there has been an extensive introduction into the healthcare field of an emphasis on patient-centeredness and patient engagement, as in the studies by Roberts et al., Oliveira et al. and Cennamo et al. [ 7,8,9]. Cennamo et al. suggested that innovation in the healthcare system can be facilitated by commercializing desired products via the empowering of care recipients through a multilateral platform innovation organization [9]. The following case studies are provided as examples to illustrate the process of how healthcare services were improved. In the case of the Patient Innovation Platform, patient Catherine Patton was diagnosed with diabetes during her pregnancy in 2001, and her doctor required her to take insulin injections multiple times a day, like a diabetic, but this was a painful routine and she was left searching for possible solutions. To improve the experience of this treatment process, she proposed the development of a device that would solve the problem of repeating her painful experience every day. Such a product is designed to allow patients to inject insulin and other medications without having to pierce the skin to complete the injection. The product is designed to be worn for three days and can be used to meet the patient’s normal lifestyle needs, including sleeping, bathing, and physical training, while wearing the product [10]. According to a study by the Cliver Research Team at Carnegie Mellon University’s School of Design, the team was commissioned by the Department of Neurosurgery at the University of Pittsburgh Medical Center in 2007 to improve patient experience. The design team began by identifying all the steps in the patient’s journey before and after surgery to create a detailed customer service blueprint that illustrated the steps the patient would go through. The design team then shared their thoughts on the medical process through on-site observations and invited patients and the medical team to share their thoughts in order to help the design team to understand the patient’s true feelings. Based on the provided information, the design team then analyzed the services needed by the patient and improved the patient experience [11]. COVID-19 has severely impacted and changed human habits worldwide since 2019. Soon after, different resources were invested in addressing the problems associated with COVID-19. While the disease may be under control, a review of recent human history shows that the frequency of such major diseases has been shortened by the ease of transportation. We should indeed prepare for a rainy day and use the lessons of COVID-19 to consider how to improve today’s smart healthcare system. Based on the spirit of human-centeredness, this study examines how innovative information technologies such as artificial intelligence can be incorporated into smart healthcare systems, and considers how to incorporate the concept of patient innovation, good communication between doctors and patients, and overall program evaluation. Therefore, the following research questions and definitions were developed. We will examine the target users of the smart healthcare system, including the general public and healthcare workers. The general public can be subdivided into three user groups according to their needs: the first group consists mainly of those who use the system for their healthcare, the second group consists of those who use the system for their family members, and the third group of those who want to obtain relevant information through the system for follow-up analysis. As mentioned above, different user groups with different needs may have different evaluations of a smart healthcare system according to these needs. Accordingly, the following four research objectives are summarized. COVID-19 virus is highly infectious and has high variability [12,13]. This study presents the outcomes of an industry-academia cooperation project. The university (National Kaohsiung University of Science and Technology) came up with the idea of developing an innovative SHS based on previous studies and on the current situation. Experts from the Chung Shan Medical University Hospital provided their suggestions from their professional point of view. This study aims to combine smartphones and webcams with artificial intelligence to enable healthcare practitioners to provide remote care to people in home quarantine. The researchers hope that the study can reduce the risk of contact for healthcare practitioners and improve their work quality and efficiency. ## 2. Materials and Methods This study uses the grounded theory as the developmental framework of the research model and applies it to COVID-19 isolated patients in the SHS, as the contours of the research context. The grounded theory is a theory-generating method that follows the collection and analysis of data in the research process [14]. It is mainly applied to reveal social relationships and group behavior [15]. In Beech et al. ’s study, the recovery experience of patients was observed through a surgical intervention experiment [16]. The three phases of this research process were constructed by using past research to assist in patient innovation, prototype system establishment, and usability testing and evaluation (Figure 1). In the first phase, we presented the background of the research on the use of artificial intelligence and other innovative technologies in an SHS, and examined possibilities for application in the usage context through the compilation of relevant literature. In the second phase, we invited COVID-19 patients or those with previous experience in caring for COVID-19 patients to conduct the interviews. In addition to listening to their experiences during treatments or quarantine, we also exchanged ideas on the use of innovative technologies in healthcare systems as inputs to the design of the prototype system and then completed the prototype design. In the third phase, we invited some of the respondents who had participated in the previous phase and nine experts from different fields of expertise to conduct a usability test of the system from the context of use requirements, and we compiled questions and corresponding suggestions from the experts about their thoughts on the system’s use flow and interface design. These suggestions and recommendations not only help the research team to improve the design of the prototype system but also serve as guidelines for the design of future SHSs. The final results and the knowledge and experience gained during the development process will be finalized. The results will be used as a reference for future research. ## 2.1. Phase I: Systematic Review of Recent Related Studies We conducted a systematic review to identify the main trends in the development of user interfaces (UI) and user experiences (UX) in the SHSs. The literature was collected through ScienceDirect Online (SDOL), IEEE Xplore and MDPI journal databases. To have a clearer understanding of how the designers of the SHSs in the literature designed the UI/UX, the literature used in the study needed to be available in full text. In considering the rigor of the relevant research, the used literature must have been peer-reviewed, excluding conference papers with questionable research frameworks. Research articles related to SHSs were accepted during the systematic review process. We excluded papers on healthcare policy, vaccines, and treatment planning. The systematic literature review process follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [17]. Finally, the researcher has extended the focus from these primary references to related projects and studies for prototype system design. We compiled this information as input for the next phase of the study. ## 2.2. Phase II: Human-Centered Smart Healthcare System Development Human-centered design is an approach to system design and development that focuses on the operational needs of the system to make it more usable and useful. Such an approach enhances efficiency and effectiveness, improves user satisfaction and sustainability, and reduces the potential adverse effects of system operation [18,19]. The concept of patient innovation even invites patients or their caregivers to participate directly in the development of the system. Patient innovation is a definition of the need for use by patients and/or the family and friends who take care of patients [3,9]. Therefore, this study recruited patients with related disease problems, through medical institutions or universities, to participate in research development. The prototype concept of the SHS is proposed by combining the literature collected in the first phase with patients’ personal experiences. An operational prototype system based on these concepts was constructed by the researchers for usability testing. ## 2.3. Phase III: Usability Test of the Smart Healthcare Prototype System The prototype smart healthcare system developed is based on the Best of Both Worlds (BoB) framework as the design criteria for iterative development [20]. Thirty participants took part in the previous phase of interviews. Nine experts with more than 10 years of experience in their respective fields were invited to conduct the usability test for this study. These experts not only have rich working experience but also their independent views and in-depth insights into this study. The expert panel consists of three areas: three medical healthcare professionals, three information systems professionals, and three human factors professionals. The selection of the study participants was conducted following the guidelines of the Helsinki Declaration and was approved by the National Cheng Kung University Governance Framework for Human Research Ethics (NCUK HREC-E-109-571-2). ## 3. Results In this section, we describe the information obtained through the selected papers. Through the systematic review, the researchers invited COVID-19 patients or those with experience caring for COVID-19 patients to conduct in-depth interviews. In addition to listening to these treatment and isolation care experiences, the researchers shared the information obtained in the first phase and exchanged ideas with the participants as inputs for the design of the prototype system. The researcher then compiled these design inputs and selected items of interest and suitability to complete the prototype system design. Finally, usability tests were conducted to understand the views of system users and experts and to suggest system improvements. The researchers iteratively repeated the above three phases, with each complete cycle running for one year and the complete study running for two years. The researchers then compiled the results of these usability tests and studies as a reference for future research. ## 3.1. Results of the Systematic Review The systematic review uses a reproducible research methodology to compile existing published literature and to identify research findings relevant to the topic. In the process of compiling the relevant literature, the eligibility criteria we used to select the literature were as follows:Only available in English. Published between January 2019 and October 2022.Papers that discuss SHSs. We excluded articles or papers that met the following criteria:Short conference papers. Full-text not available. Not related to healthcare information systems. The following databases were used: ScienceDirect Online (SDOL), IEEE Xplore, and MDPI, to identify and collect articles related to SHSs. The selection was performed based on relevance to the domains of interest and scope. The fields considered in the search query were limited to the titles and abstracts of the papers. Several keywords (COVID-19, healthcare, systems, design) were used, and combined using Boolean operators (AND, OR, and NOT) to cross-examine the scientific databases. After retrieving the articles from the search databases, we use Endnote software to create references and remove duplicates. As highlighted in the inclusion criteria, articles were selected based on a three-step process: assessment of the title, abstract, and full text. The full process (as shown in Figure 2) is used for selection, including screening and determining eligibility and inclusion. The selected 22 documents in this phase can be divided into the following three categories, as shown in Table 1’s summary of the applications and the sub-topics covered. ## 3.1.1. Internet of Medical Things (IoMT) The Internet of Medical Things (IoMT) is the set of medical products and services that can be connected to medical and healthcare systems through other systems and networks [43]. Specific IoMT applications include telemedicine through wearable devices for patients with chronic diseases [32], and the ability of healthcare professionals to track patient prescriptions and providers through IoMT technology [44]. With a new IoMT integrated healthcare system, better personalized healthcare can be achieved. The benefits of the Internet of Medical Things also come with risks, such as serious information security issues and issues related to patient privacy [45]. The IoMT framework addresses the shortage of resources faced by medical teams during the COVID-19 epidemic; not only can the geo-location of COVID-19 patients be tracked by Bluetooth to assess personnel risk [26,27,31,40], but the technology allows for tracking of patient recovery [22,33]. Chen’s research focuses on visualizing and discussing this important information [35]. Through the presentation of these visual digital dashboards, public health units and government departments can quickly plan and allocate healthcare resources [36,39]. Gerli’s research focuses on analyzing the 50 e-health applications proposed by the European Union during the COVID-19 epidemic and outlines three issues (i.e., user orientation, participation, legality and equity) [23]. Patients and healthcare professionals can also learn online via virtual reality through IoMT technology [29,38]. During the COVID-19 epidemic, information security concerns about data breaches plagued the healthcare industry. Blockchain technology not only accelerates the adoption of electronic health records but also ensures that systems are resistant to malicious cyber-attacks [21,24,28]. ## 3.1.2. Clinical Decision Support System (CDSS) CDSS can be defined as follows: “Health observation and health knowledge system are linked together to impact health choice by medical experts to enhanced healthcare system” [46]. The functions and benefits of CDSS include patient safety, clinical management, cost containment, administrative functions, and diagnostics support. Support for CDSS continues to grow in the age of the electronic medical record, and there are still more advances to be made including interoperability, speed and ease of deployment, and affordability [47]. CDSSs can help healthcare professionals make diagnoses more efficiently and accurately, and reduce medical errors and costs [48]. The development of artificial intelligence technologies has also facilitated the development of clinical decision support systems and the integration of knowledge systems in the field to solve existing problems [49]. However, while these technologies have improved the accuracy of clinical decision systems, research has also found that it is impossible to ignore the uniqueness that people value, thus driving the development of personalized medicine [50]. During the COVID-19 epidemic, artificial intelligence-based clinical decision support systems can assist medical staff in making decisions based on the patient’s actual condition, whether to hospitalize, isolate, or the patient just has a common cold [22,31,34], and even the risk of death is predicted [25]. Many countries have developed smartphone-integrated healthcare applications to collect extensive user information in order to confirm COVID-19 spread trends [23,26,32,36]. Many artificial intelligence-based clinical decision support systems, combined with graphical medicine and patients‘ electronic health records, can assist medical teams in determining screening records more accurately [30,33,39,41,42]. The electronic health records constructed by blockchain technology can be combined with smart contracts to allow medical staff to incorporate personalized patient records (such as the display of chronic diseases) in their decision-making dashboard, and to move toward personalized medicine [28]. ## 3.1.3. Telemedicine Telemedicine refers to the provision of healthcare services and the exchange of health information at a distance [51]. During this COVID-19 epidemic, telemedicine allows patients to obtain the medical advice they need remotely, which can reduce the risk of cross-infection of mildly ill patients with seriously ill patients [52]. It can also reduce the chance of the influx of people into medical institutions, resulting in a shortage of medical resources [53]. However, the privacy of patients is a concern when using Internet-based telemedicine applications [54,55]. Telemedicine uses a smartphone or virtual reality to provide a suitable and safe environment for patients and medical staff to obtain and provide the consultation services they need [22,26,36]. The use of telemedicine can not only reduce the risk of disease transmission but effectively reduce the waste of resources, such as staff movement time [23,37,39,42]. With the contribution to the technology of wearable devices, patients can transmit their physiological data to remote medical personnel for personalized medical services, but the issue of information security is an upcoming challenge [31,32,34]. Scholars recommend encrypting technologies for transmission through blockchain technology to strengthen information security [28]. ## 3.2. Human-Centered Smart Healthcare System Development According to the literature review (Table 2), three functions (i.e., IoMT, CDSS, and Telemedicine) are the most important functions during the COVID-19 epidemic. Moreover, patient innovation should also be considered in the lifecycle of smart healthcare system development. That is, we introduced a process to develop a human-centered SHS from the sketch, with the basic functions of IoMT, CDSS, and Telemedicine. The concept of IoMT used has contributions from smart wearable devices, snoring detection, and a smart robot. As for the CDSS function, our proposed SHS can provide the patient with remote diagnoses. Through these, the cooperated healthcare unit can henceforward provide prescriptions. This is the function of telemedicine. The specific functions above will be introduced in this subsection. This study recruited former COVID-19 patients or those with experience in caring for COVID-19 patients within a university for an academic research workshop (Figure 3). First, 46 recruited participants were divided into groups of 2–4 to discuss their previous experience of, or caring for, patients during COVID-19. These participants reflected on many of the difficulties they found in their daily lives during the treatment and isolation process after developing COVID-19. Examples include poor sleep quality at night, worries about unsubstantiated rumors on the Internet, and physical or psychological problems such as reduced exercise or social interaction due to isolation. Second, the researchers shared examples of recent research on innovative technology relating to the disease. The researchers shared the relevant applications published in the literature mentioned in the above Section 3.1. Finally, the groups were asked to report on possible future applications of these innovations in the context of their personal experience. With this shared experience and information, the SHS prototype was proposed. When a user registers for the first time, the system will guide the user to take a simple health survey and conduct an electronic health record for the user. The user can upload personal physiological signal information obtained from the wearable device to a cloud-based database to assist medical professionals in making personalized medical suggestions. The user can also record their sleep process through smartphones. The use of artificial intelligence technology allows the user and medical personnel to determine whether there is a sleep-breathing disorder through snoring detection (Figure 4) [56]. Besides nighttime sleep healthcare, the SHS can also incorporate key-point technology to record the user’s living trajectory [57]. As shown in Figure 5a, images/videos of the care recipient in the living space can be obtained through webcams. To avoid blind spots, multiple webcams are set up to extend the image information. As shown in Figure 5b, the acquired images are input into the healthcare system (Key-point coordinates data at the bottom as shown on the right side of Figure 5b). The system detects the person and obtains the location of the key points of the human body. The system determines the location of the human body by the information on the foot position and then connects different cameras in all indoor spaces to obtain information on the movement of the human body in the lower right corner of the living space. In particular, it is necessary to reserve private spaces, such as restrooms, bedrooms, etc., to prevent users from feeling like they are being monitored when being in those spaces. During the COVID-19 epidemic, this method can be used to assist social care agencies in the trajectory tracking of confirmed case patients and prevent close contacts. This study also includes a conversational robot to assist users in making electronic health records (Figure 6) [58]. When initially registering, users can complete their daily health records through interesting interactions with the robots. In addition, applications are built into the robots with key point detection technology [59]. Through these applications, the elderly can be assisted in physical training during home isolation. The robots can also be connected remotely, so that family members or medical teams can provide care to patients during isolation. This study developed an SHS prototype system. People in home quarantine and healthcare practitioners can access healthcare and caring services through the system. The healthcare practitioners can obtain physiological information about their care recipients through the system, in combination with wearable devices. Voice recognition helps healthcare practitioners understand the sleep quality of their care recipients. Image recognition helps people record their activities at home. Healthcare robots allow people in home quarantine to access the social interaction and telemedicine services they need. ## 3.3. Usability Test of the SHS Prototype The purpose of usability testing is to evaluate the efficiency and effectiveness of the system and user satisfaction through quantitative and qualitative methods [60]. The test method allows users to complete a pre-prepared operation task, and the researcher obtains users’ subjective cognitive assessments of the system through observation during the operation process and questionnaires and interviews after completing the operation task [61,62]. System developers can also use this information as a design guide for design or as a reference for improvement [63]. Thirty participants were invited to take part in the usability tests. The expert consultation process passed the National Cheng Kung University academic ethics review. The researcher explained the purpose and rights of the experiment to the participants and invited them to sign the informed consent form. The process of the usability test was ethically reviewed by the Zhongshan Medical University Hospital (CSH-2021-C-055). The Tobii eye-tracking device was used to assist in the recording of the participants during the operation of the SHS (Figure 7) [64]. At the end of the experimental session, they were interviewed by the research team. They were asked to describe the problems they had encountered during the operations by incorporating a recall-based playback method. Finally, they were invited to provide suggestions for the prototype system design [65]. Nine experts from different fields were invited to a final expert consultation. The expert consultation process passed the National Cheng Kung University Governance Framework for Human Research Ethics review (109–571). The research team conducted expert interviews using the Delphi method, and presented the system and its operation to the experts in a one-hour presentation. This allowed the experts to fully understand the design concept of the system. Then, an interview was concluded by allowing the experts to operate the system on their own, and after completing the operation they filled in the System Usability Scale (SUS) questionnaire in the form of an online questionnaire, and provided feedback on the system design. After all experts had completed the online form, the researcher then conducted a separate interview with the experts. The results of the SUS questionnaire survey received an average score of 74. The experts provided details of their recommendations. The suggestions provided by the experts were recoded according to the system framework categories and provided to future researchers and designers as a reference for design guidelines. The design guidelines for each structure of the SHS system are shown in Table 2. The researchers invited 30 people to conduct a usability test using an eye-tracker combined with a retrospective think-aloud interview. Through usability testing with the participants, researchers can understand how to improve human–computer interactions, ease of use, and fluency. The six structures in Table 2 are the main frameworks of the prototype system. The experts provide their concerns and suggestions for these frameworks. The researcher summarized these experts’ suggestions and further discussed with them the expectations of the future system and used the KJ method to finalize the design guidelines described in Table 2 (Figure 8). ## 4. Discussion First, we reviewed 22 SHS-related papers out of the 275 papers searched initially. The three main categories of the SHSs were defined, i.e., IoMT, CDSS, and Telemedicine. According to the literature review, we treated the three categories as the three main functions in our proposed SHS prototype. Moreover, the concept of patient innovation was also adopted to develop and evaluate the system. The design of traditional information systems often starts with the closure of the design team. This study combines patient involvement and innovation by inviting patients to participate in the system design and development process. This not only allows the system designers to be closer to the actual needs of the system users, but also shortens the time required to integrate the supply and demand sides of the system during the development process. It can also integrate the knowledge and skills that the patients may have and increase the possibilities of improving system development. Second, as noted in the introduction and the literature review, the COVID-19 epidemic has facilitated the development of many innovative technological applications in response to the epidemic. The study provides a method to rapidly build prototype systems in order to allow system designers to review these innovative applications and confirm whether the prototype system can meet the needs of system users within a minimal timeframe. The time required for development through expert consultation and the potential risks associated with the development process are also reduced. Although the prototype system developed in this study is still rather far from actual commercial application, through an iterative development approach similar to that used in this study, these gaps can be minimized after multiple iterations, and the design team can understand whether the design deviates from the original needs of the users. As the COVID-19 epidemic gradually slowed down, the need for such information systems as visual dashboards for COVID-19 epidemic transmission and vaccine appointment systems also gradually decreased. Researchers can use the technology and experience from the development of these systems to transfer the development of smart healthcare systems to other chronic diseases. The management of chronic diseases is another very serious topic for healthcare systems. Finally, each development process is a valuable experience for researchers and system designers. By presenting and sharing the research results, we hope to consolidate these valuable experiences and pass them on. ## 5. Conclusions This study aims at filling the research gaps in current SHS-related studies. [ 1] Patient innovation: by involving the COVID-19 experienced participants, the patients’ needs can be identified; [2] Innovative technologies: studies discussing the usability of innovative technologies are few, and we focused on evaluating usability for building a more user-friendly innovative SHS; [3] Precise target users: all the participants have real experience of contracting COVID-19, interacted with the experts to provide more precise suggestions for the proposed SHS; [4] Variety of experts: experts from different professions were involved to identify issues regarding the proposed SHS. These can provide professional suggestions and recommendations to improve the system. This paper presents a methodology for developing a prototype healthcare system that incorporates patient participation. Such an approach allows patients to be involved in the development of the prototype from the initial stages of system development and allows system developers to understand the needs of patients. Each development review process can also integrate patient and expert review to ensure that the system development process meets the needs of the end user. In addition, through actual participation in the development process, this study compiles the opinions provided by experts and their experiences to serve as design guidelines for future research and practitioners to follow. In the future, even if the COVID-19 outbreak gradually subsides, the demand from healthcare systems will not decrease. It is suggested that healthcare systems for patients with chronic diseases or non-specific diseases can be developed so that the use of smart healthcare systems can become more widespread. 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--- title: Changes in Cardiorespiratory Fitness and Probability of Developing Abdominal Obesity at One and Two Years authors: - Ricardo Ortega - Gonzalo Grandes - María Teresa Agulló-Ortuño - Sagrario Gómez-Cantarino journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048740 doi: 10.3390/ijerph20064754 license: CC BY 4.0 --- # Changes in Cardiorespiratory Fitness and Probability of Developing Abdominal Obesity at One and Two Years ## Abstract Low cardiorespiratory fitness (CRF) is associated with an increased risk of developing abdominal obesity (AO), but it is not known if and/or how changes in CRF affect AO. We examined the relationship between changes in CRF and the risk of developing AO. This is a retrospective observational study of a cohort of 1883 sedentary patients, who had participated in a clinical trial of physical activity promotion carried out in Spain (2003–2007). These data were not used in the clinical trial. At baseline, they were free of cardiovascular disease, hypertension, diabetes, dyslipidemia, and/or AO; with an indirect VO2max measurement; 19–80 years old; and $62\%$ were women. All the measures were repeated at 6, 12, and 24 months. The exposure factor was the change in CRF at 6 or 12 months, categorized in these groups: unfit-unfit, unfit-fit, fit-unfit, and fit-fit. We considered fit and unfit participants as those with VO2max values in the high tertile, and in the moderate or low tertiles, respectively. The main outcome measure was the risk of developing AO at one and two years, as defined by waist circumference >102 (men) and >88 (women) cm. At two years, $10.5\%$ of the participants had developed AO: $13.5\%$ in the unfit-unfit group of change at 6 months; $10.3\%$ in the unfit-fit group (adjusted odds ratio (AOR) 0.86; $95\%$ confidence interval (CI) 0.49–1.52); $2.6\%$ in the fit-unfit group (AOR 0.13; $95\%$CI 0.03–0.61); and $6.0\%$ in the fit-fit group (AOR 0.47; $95\%$CI 0.26–0.84). Those who stayed fit at 6 months decreased the probability of developing abdominal obesity at two years. ## 1. Introduction Abdominal obesity (AO) is a greater health problem than general obesity (Body Mass Index ≥ 30 kg/m2) because it has a higher prevalence, a higher annual increase [1,2], and is a better predictor of obesity-related metabolic disorders [3] and the risk of cardiovascular disease (CVD) mortality [4]. Cardiorespiratory fitness (CRF) reflects the ability of the oxygen transport system to deliver oxygen to the muscles when they are performing physical work. The more physical work that is completed, the more oxygen is needed and the more the oxygen transport system has to work to meet those needs. This makes the organs involved in this transport improve their performance, and therefore translates into a higher CRF [5]. This higher CRF is also influenced by genetics in the early stages of life. After these stages, if physical work is not performed, it is not possible to maintain or increase that higher CRF [6]. Age, sex, and the presence of diseases or medication that influence the oxygen transport system also influence the CRF [5]. Only three studies about the relationship between CRF and abdominal obesity in adults have been detected in the scientific literature [7,8,9]. Two of them are cross-sectional observational studies that looked at the association between CRF and abdominal obesity [7,8]. The third is a longitudinal observational study that analyzed the relationship between CRF at a given time and the probability of developing abdominal obesity at two years [9]. This study about the relationship between CRF and obesity measured by different methods showed a $129\%$ higher probability of developing AO in subjects with low CRF in comparison with those with high CRF. Furthermore, it relied on a single baseline assessment of CRF, with subsequent follow-up for AO development, but there are people who experience changes in their lifestyle that lead them to increase their CRF. So, it assumed that subjects were going to remain in the same condition during the observation follow-up period. With this single exposure assessment, it is difficult to discount the hypothesis that genetic factors or other confounder variables are an important cause of the relationship between CRF and AO. Moreover, changes in the variable of interest during follow-up may affect the subsequent probability of developing AO. One way to examine this issue more completely is to evaluate the effect of changes in CRF on the probability of developing AO. The follow-up in the original study, PEPAF [10], provides an opportunity to evaluate the relationship of changes in CRF with AO in a cohort of men and women with four clinical evaluations over a two-year period. Therefore, the hypothesis that changes in CRF at six and twelve months produced changes in the probability of developing AO at one and two years was retrospectively examined. ## 2.1. Design and Data Collection This retrospective longitudinal observational study used data from the cohort of 4927 sedentary men and women (they did not meet the recommended aerobic physical activity levels [11] gathered between 2003 and 2004 for the PEPAF study and belonging to 11 Spanish health centers. The participants were between 19 and 80 years old and had no known cardiovascular disease. This cohort underwent a baseline evaluation and was re-evaluated at 6, 12, and 24 months. More details have been published elsewhere [10,12,13]. The PEPAF study complied with the guidelines of the Declaration of Helsinki. Its protocol was approved by the Institutional Clinical Research Ethics Committees (CRECs) for all of the participating centers (ClinicalTrials.gov Identifier: NCT00131079). Written informed consent was obtained from all participants involved in the study and patients signed a consent form before the baseline measurement. ## 2.2. Population For the present study, we included participants who did not have hypertension, diabetes, or dyslipidemia ($$n = 2974$$). We excluded those who had missing values for oxygen uptake measurement (VO2max) and waist circumference (WC) at baseline ($$n = 218$$), as well as those men and women ($$n = 873$$) who already had a baseline WC > 102 cm and >88 cm, respectively. Thus, the cohort of this study included 1883 participants. ## 2.3. Measurements For the purposes of this study, we selected the following socio-demographic and behavioral variables and measurements from the original study corresponding to baseline and 6- and 12-month visits: WC, VO2max, gender, age, smoking and alcohol habits, and physical activity levels. We also selected the measurement of WC at 24 months. With the participant lying on the examination table, WC was measured level with the umbilicus, using a laminated meter tape around the uncovered abdomen. VO2max was indirectly estimated by using a sub-maximal cycle ergometer (VarioBike 500) exercise test, using the YMCA-ACSM protocol, and was standardized by age, sex, and resting heart rate [14]. To predict VO2max according to the YMCA protocol, the participant pedaled on a cycle ergometer at specified kgm/min work rates for two to four 3 min stages, until his steady state heart rate rose to between 110 and 150 beats/min for two consecutive stages. Heart rate was recorded during the final 15 to 30 s of the second and third minutes. Gender, age, social class, educational levels, employment status, physical activity levels (minutes per week and METs × hours per week spent in moderate or vigorous leisure and occupational activity in the week previous to the interview), tobacco (current smoker and non-smoker), and alcohol habits (drinker at no risk and drinker at risk) were recorded by questionnaires as explained in detail in the PEPAF study publication [13]. Nurses were trained for the performance of all measurements and for the guarantee of data quality. For data quality, a pilot study was conducted followed by a three-day review training, and double data entry into a centralized Oracle™ database. Quality control consisted of daily online supervision of the study process and data, daily feedback to nurses, monthly progress reports, and regular meetings with the collaborating investigators and nurses every four months. ## 2.4. Exposure Variable The 1883 subjects of this sample were categorized as low, moderate, and high CRF according to the tertiles of their estimated VO2max and gender at baseline. These CRF tertiles were automatically generated by the statistical package, corresponding to the following VO2max values: low < 28.88 (men) and <21.94 (women) mL/kg/min, moderate from 28.88 to 35.71 (men) and 21.94 to 26.25 (women) mL/kg/min, and high > 35.71 (men) and > 26.25 (women) mL/kg/min. We obtained the number of METs corresponding to the value of each tertile by dividing those values of VO2max by 3.5 mL/kg/min. They were “low” < 8.25 (men) and <6.268 (women) METs, “moderate” from 8.25 to 10.21 (men) and 6.268 to 7.5 (women) METs, and “high” > 10.21 (men) and >7.5 (women) METs. Because in the previous study [9], low and moderate tertiles had a higher probability of developing AO than the high tertile, for the present study we now considered as unfit those subjects in the low and moderate tertiles (VO2max ≤ 35.71 mL/kg/min or ≤10.21 METs in men and ≤26.25 mL/kg/min or ≤7.5 METs in women), and as fit those subjects in the high tertile (VO2max > 35.71 mL/kg/min or >10.21 METs in men and >26.25 mL/kg/min or >10.21 METs in women). Using those VO2max values, unfit and fit subjects were classified at 6 and 12 months, according to the VO2max values obtained at the two follow-up visits. Then, 4 groups of CRF change at 6 and 12 months were established, and they constituted the exposition groups, as follows: the group of unfit subjects at baseline who remained unfit at 6 or 12 months (unfit-unfit group); group of unfit subjects at baseline who became fit at 6 or 12 months (unfit-fit group); group of fit subjects at baseline who became unfit at 6 or 12 months (fit-unfit group); and group of fit subjects at baseline who remained fit at 6 or 12 months (fit-fit group). Each of the 3 groups (unfit-fit, fit-unfit, and fit-fit groups) was compared to unfit-unfit subjects as the reference group. The four groups obtained at 6 and 12 months were examined in separate models. ## 2.5. Outcome Variables The cumulative incidence of AO, defined as the transition from a WC of ≤102 cm in men or ≤88 cm in women at the study baseline to a WC of >102 or 88 cm in men and women [15,16,17], respectively, was observed one or two years later. Given that gender, age, social class, educational levels, employment status, changes in smoking and alcohol habits, and changes in physical activity levels may influence CRF [5] and/or abdominal fat, they were considered as potential confounders. ## 2.6. Data Analysis All analyses were conducted using STATA. Means (SDs) were calculated for age, difference in WC, and difference in physical activity levels, and the percentage of participants in each category was determined for gender, social class, educational levels, employment status, AO, CRF, and smoking and alcohol habits. Those values were distributed according to the four groups of change in CRF and were compared using a chi-square test for the proportions of categorical variables and analysis of variance for the means of continuous variables. The one- and two-year cumulative incidences of AO were calculated by dividing the number of new cases at those points in time by the number of exposed participants in each of the four groups of change in CRF at 6 or 12 months. The probability of developing AO was computed as the odd ratios (ORs) of the one- or two-year cumulative incidence in the three exposed groups, divided by the cumulative incidence in the non-exposed group, as a point of reference, and adjusted for potential confounding variables and change in WC at 6 or 12 months by using multivariate mixed logistic regression models. ## 3. Results At 6 and 12 months, 406 and 557 participants, respectively, failed to attend the VO2max and/or WC measurement visit, with remaining available data for 1477 and 1326 participants, respectively. Their mean age was, respectively, 41.5 (SD, 13.1) and 42.5 (SD, 13.2) years, of which $61.8\%$ and $61.3\%$ were women. There were no significant differences between those with valid and missing values in gender at 6 months, as well as VO2max and distribution in tertiles at 6 and 12 months. There were significant differences in age ($p \leq 0.001$; those with missing values were 3.2 and 3.1 younger, respectively, at 6 and 12 months), gender ($p \leq 0.05$; more women ($32.5\%$) than men ($28\%$) at 12 months), and waist circumference ($p \leq 0.001$; those with missing values had 2 and 1.6 cm, respectively, less at 6 and 12 months). Participants’ characteristics across the four groups of change in CRF at 6 and 12 months are summarized in Table 1 and Table 2. Table 1 shows that all tests for trends throughout the four groups of change in CRF at 6 months were significant ($p \leq 0.05$), except for gender, social class, and changes in physical activity levels at 6 months. Table 2 shows that all tests for trends throughout the four groups of change in CRF at 12 months were significant ($p \leq 0.05$), except for gender, social class, and changes in WC, and in physical activity levels at 12 months. The numbers of new cases of AO at one and two years were, respectively, 97 (cumulative incidence: 6.98; $95\%$ confidence interval ($95\%$CI): 5.70–8.45) and 135 (cumulative incidence: 10.5; $95\%$CI: 8.88–12.32). Table 3 shows cumulative incidence rates at one and two years across unfit-unfit, unfit-fit, fit-unfit, and fit-fit groups of change at 6 and 12 months. Compared to those participants who remained unfit at 6 or 12 months, it shows that those who remained fit at 6 months had a lower incidence of AO at one and two years, and those participants who remained fit at 12 months had a lower incidence of AO at two years. Moreover, compared to those who remained unfit at 6 months, those participants who changed from fit at baseline to unfit at 6 months had a lower incidence of AO at two years. Table 4 shows the results of the adjusted ORs of changes in CRF at 6 months and the development of AO in the next six and eighteen months, as well as changes in CRF at 12 months and the development of AO in the next twelve months. Compared to the unfit-unfit change group at 6 months, the fit-unfit and fit-fit change groups were associated with a lower probability (OR = 0.26; $95\%$CI, 0.07–0.99 and OR = 0.44; $95\%$CI, 0.22–0.84, respectively) of developing AO in the next six months (at one year). Compared to the unfit-unfit change group at 6 months, the fit-unfit and fit-fit change groups were associated with a lower probability (OR = 0.12; $95\%$CI, 0.02–0.58 and OR = 0.45; $95\%$CI, 0.25–0.83, respectively) of developing AO in the next eighteen months (at two years). Compared to the unfit-unfit change group at 12 months, the fit-unfit and fit-fit change groups were associated with a lower probability (OR = 0.33; $95\%$CI, 0.13–0.83, and OR = 0.37; $95\%$CI, 0.20–0.68, respectively) of developing AO in the next twelve months (at two years). Moreover, the multivariate analysis yielded the following results. Women had a higher probability of developing AO at one (OR = 2.29; $95\%$CI, 1.31–4.00) and two years (OR = 1.90; $95\%$CI, 1.14–3.16) than men. A middle or high school education had a higher probability of developing AO at one year (OR = 2.25; $95\%$CI, 1.03–4.91) than less education. Retirement had a higher probability of developing AO at two years (OR = 2.56; $95\%$CI, 1.03–6.38) compared to working outside of the home. For every cm of increment in WC at 6 months there was a greater probability of developing AO at one year (OR = 1.26; $95\%$CI, 1.18–1.34) and two years (OR = 1.17; $95\%$CI, 1.11–1.24), and for the same change in WC at 12 months there was a greater probability of developing AO at two years (OR = 1.20; $95\%$CI, 1.15–1.16). For every minute per week increment of physical activity at 6 and 12 months, there was a lower probability of developing AO at two years (OR = 0.997; $95\%$CI, 0.995–0.999 and OR = 0.997; $95\%$CI, 0.995–0.998, respectively). Continuing to smoke at 6 or 12 months had a higher probability of developing AO at two years (OR = 1.71; $95\%$CI, 1.12–2.65 and OR = 1.79; $95\%$CI, 1.15–2.78, respectively) than ceasing to smoke, and beginning to smoke at 6 months had a higher probability of developing AO at two years (OR = 2.80; $95\%$CI, 1.26–6.22). Being a risky drinker at 6 months had a higher probability of developing AO at two years (OR = 4.13; $95\%$CI, 1.35–12.6) than remaining a non-risky drinker. ## 4. Discussion Data of this cohort of patients free of CVD, hypertension, diabetes, and dyslipidemia who had consulted their primary care physicians in Spain indicate that those who stayed fit at 6 or 12 months had a lower probability of the subsequent development of AO at one and two years, when compared to those who remained unfit. Particularly, compared with remaining unfit, staying fit at 6 months was associated with a $56\%$ and $55\%$ decrease, respectively, at one and two years, in the probability of developing AO; changing from fit to unfit at 6 months was associated with a $74\%$ and $88\%$ decrease, respectively, at one and two years. On the other hand, compared with remaining unfit, staying fit at 12 months was associated with a $63\%$ decrease in the probability of developing AO at two years; furthermore, changing from fit to unfit at 12 months was associated with a $67\%$ decrease at two years. The lower probability of developing AO in those who stayed fit is consistent with the recommendation made in a previous article [9] of increasing CRF to over 10.2 and 7.5 METs, respectively, in men and women. These CRF levels are fairly consistent with the levels of an existing meta-analysis [18] about CRF and all-cause mortality and cardiovascular events. This meta-analysis was conducted using thirty-three studies: twenty-three of which included only men, four of which included men and women, and six of which did not mention the gender of the participants. It considered low CRF those levels below 7.9 METs, intermediate CRF those levels between 7.9 and 10.8 METs, and high CRF those levels above 10.8 METs, but it did not distinguish between men and women. Its most plausible explanation was that people who stay fit lead a lifestyle that involves physical efforts of sufficient intensity to maintain that fitness [5], and to maintain the physical efforts that require the utilization of energy from the body’s fats. The higher energy expenditure of people who stay fit may either generate a neutral (energy expenditure equals energy intake) or excess (energy expenditure exceeds energy intake) energy balance, thereby avoiding an excessive accumulation of fat in the abdomen. The lower probability of developing AO in those who changed from fit to unfit was probably due to the short time elapsed since they changed from fit to unfit, which was not enough to increase the accumulation of fat in the abdomen until reaching AO. It also probably depends on how long they maintained a high level of CRF. A study about changes in CRF and mortality [19] found that those participants who changed from fit to unfit had a lower risk of mortality than those who stayed unfit. The same can be said for those who changed from unfit to fit. The time elapsed since they changed from unfit to fit was probably insufficient to prevent them from continuing to accumulate fat in the abdomen. Our search has not identified any study on the change in CRF and the development, not only of AO, but of any type of obesity. The existing studies on the change in CRF looked for its influence on general and cardiovascular mortality [19,20,21,22], cancer mortality [23], the risk of dementia incidence and mortality [24], the risk of stroke and death [25], the risk of atrial fibrillation and heart failure [26], or the risk of hypertension [27,28]. ## 4.1. Limitations The limitations of this study may include methods of measuring CRF and WC, the number of cases developed from AO, the short follow-up period, and a potential bias from loss to follow-up. The exposure variable, CRF, was measured by an indirect method and a sub-maximal stress test, which are valid and feasible measuring ways for primary care [29], and were used in numerous studies, although the most accurate form of measurement is a direct method and a maximum exercise test [5]. The outcome variable, AO, was measured by waist circumference, instead of computerized tomography, because it is a valid and more feasible method of measurement for primary care and has been used in numerous studies [30]. Waist circumference was validated against other ways of measuring abdominal fat, such as computed tomography, and correlations of 0.84, 0.71, and 0.73 with total, subcutaneous, and visceral abdominal fat, respectively, have been found [31]. On the other hand, the number of cases of AO developed at one and two years according to the changes in CRF (unfit-unfit, unfit-fit, fit-unfit, and fit-fit) at 6 and 12 months were as follows: sixty-one, eleven, three, and fourteen new cases developed at one year for changes at 6 months; eighty-four, nineteen, two, and eighteen new cases developed at two years for changes at 6 months; and seventy-nine, seventeen, six, and sixteen new cases developed at two years for changes at 12 months. Note that the fit-unfit group of change in CRF at 6 months did not reach five cases at one year and two years. That situation, in addition to the fact that only 75 and 98 participants at 6 and 12 months, respectively, changed from fit to unfit, may have misrepresented the probability of developing AO in this group compared to that of the unfit-unfit. The incidences of the fit-unfit change group were the lowest of the three exposure groups. Compared to what would be expected, the short follow-up period might have influenced the results of the unfit-fit and fit-unfit groups in the sense of having made possible other results with a longer follow-up period, such as a lesser incidence of AO in the unfit-fit group and a similar or higher incidence in the fit-unfit group compared with the unfit-unfit group. Regarding losses, we think that biases were probably of little importance given that differences between those with valid and missing values were small. ## 4.2. Strengths Possible strengths of the study are as follows: the repeated performed measures of exposure and the results over time in this study in which the results remained constant, although with different magnitudes at each moment of CRF changes and development of AO; the consideration of low and moderate CRF tertiles as unfit, which represent $66\%$ of the cohort, and the high CRF tertile as fit, which represents only $33\%$ of the cohort, which caused the fit-unfit group of CRF change to have the least number of participants of all the groups. ## 5. Conclusions This study shows that staying fit over time protects against the development of AO. It also shows that staying fit and becoming unfit may still protect against the development of AO during a certain period of time, which will probably be in relation to the time that one remains fit. Having changed from unfit to fit might also protect against the development of AO but it surely needs a longer period of time. 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--- title: Preliminary Support for the Use of Motivational Interviewing to Improve Parent/Adult Caregiver Behavior for Obesity and Cancer Prevention authors: - Ashlea Braun - James Portner - Menglin Xu - Lindy Weaver - Keeley Pratt - Amy Darragh - Colleen K. Spees journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048747 doi: 10.3390/ijerph20064726 license: CC BY 4.0 --- # Preliminary Support for the Use of Motivational Interviewing to Improve Parent/Adult Caregiver Behavior for Obesity and Cancer Prevention ## Abstract Motivational interviewing (MI) is a promising behavioral intervention for improving parent and adult caregiver (PAC) health behavior for obesity and cancer prevention. This study explored the preliminary effects of MI from a registered dietitian (RDMI) within an obesity prevention intervention to promote PAC behavior change and positive proxy effects on children and the home environment. $$n = 36$$ PAC/child dyads from low-resource communities were enrolled in a randomized trial testing a 10-week obesity prevention intervention. Intervention dyads were offered RDMI sessions. Data were collected at baseline and post-intervention (PAC diet quality (Healthy Eating Index (HEI)), child skin carotenoids, home environment, and PAC ambivalence regarding improving diet). Results show that for every RDMI dose, PAC HEI scores increased (0.571 points, $$p \leq 0.530$$), child skin carotenoid scores improved ($1.315\%$, $$p \leq 0.592$$), and the home food environment improved ($3.559\%$, $$p \leq 0.026$$). There was a significant positive relationship between RDMI dose and change in ambivalence (ρ = 0.533, $$p \leq 0.007$$). Higher baseline ambivalence was associated with greater dose (ρ = −0.287, $$p \leq 0.173$$). Thus, RDMI for PACs may improve diets among PACs who are otherwise ambivalent, with potential effects on the diets of their children and the home food environment. Such intervention strategies have the potential for greater effect, strengthening behavioral interventions targeting obesity and cancer. ## 1. Introduction The presence of obesity has been identified as a key factor in the development of multiple preventable cancer types [1,2,3,4]. Sufficient consumption of fruits and vegetables and overall dietary patterns contribute to decreased risk for certain cancers, as well as obesity, which further mitigates cancer risk [2]. In the United States (US), rates of obesity among adult and youth populations alike have been steadily increasing, while fruit and vegetable consumption remains suboptimal [5,6,7,8]. Given the challenges associated with treating obesity in adulthood as a means of cancer prevention and control, obesity prevention in youth is essential [9]. Critical to effective obesity prevention are multidimensional strategies, including family-based prevention efforts, to simultaneously address parents and adult caregivers’ (PACs) and children’s health behavior (e.g., diet) [10,11,12,13,14]. Motivational interviewing (MI) has an established empirical base for obesity prevention efforts that include PACs [15]. Previous MI-based interventions have targeted PACs to improve adherence in child-based interventions [16], encourage weight loss in children [17], change child behavior or family-related factors (e.g., family meals) [18,19,20,21,22,23], and have been delivered in groups to families [24,25,26,27,28] or directly to children in the presence of PACs [16,29,30,31,32,33,34]. Many of these studies suffer from design limitations, as they do not specifically or adequately define, and subsequently target, PAC behaviors as a catalyst for youth behavior change and obesity prevention [10,35,36,37,38]. Given PACs can influence child behavior (e.g., modeling), there is potential for PAC-delivered interventions to indirectly influence child behavior, versus directive approaches that may be frustrating for families if child compliance is poor [39,40,41,42]. Given the novelty of PAC-focused MI, rigorous methods are warranted (e.g., use of trained interventionists, fidelity monitoring, integration with behavior- or outcome-specific treatment modalities) [18,43,44,45,46,47,48,49,50]. The efficacy of MI is strengthened when combined with behavior- and/or outcome-specific treatments [45,51]. As such, the use of registered dietitians (RDs) is a logical extension of MI targeting dietary patterns [18,44,52]. Integration is challenging, and a thorough understanding of implementation is critical [53]. The purpose of this study was to explore the preliminary efficacy of PAC behavior-focused MI from an RD (RDMI), in order to improve diet- and health-related outcomes and the home/family environment, among dyads enrolled in an obesity prevention intervention. A secondary purpose was to characterize implementation. The authors hypothesized that RDMI would contribute to an improved diet in the PACs and children, and that greater improvements would be seen with greater doses. ## 2.1. Participants and Recruitment Parents/adult caregivers and one 8–9-year-old child from the central Ohio area were invited to participate as dyads. Recruitment occurred via communication with local schools that received federal funding for free, as well as reduced-price, breakfast and lunch to students, in order to target dyads from low-resource/under-resourced communities. Interested PACs were screened for eligibility prior to scheduling a baseline orientation/data collection visit. Additional inclusion criteria included the following: [1] fluent English-speaking dyads and [2] ability to consume fruits and vegetables without concerns of medication–nutrient interactions. Exclusion criteria included the following: [1] diagnosed with mental, physical, or communication disabilities, or difficulties that would impair full participation in all components of the intervention; [2] lack of transportation to weekly classes; [3] non-English speaking; [4] consuming over-the-counter herbals, botanicals, or nutritional supplements (excluding multivitamins); [5] diagnosed with active metabolic or digestive illnesses, which may result in nutrient malabsorption. Participating caregivers were required to be a primary caregiver responsible for food procurement and preparation for the participating child, though no specific relationship was required (e.g., mother/father, aunt/uncle). All procedures were performed in accordance with the ethical standards of The Ohio State University’s institutional review board and the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. ## 2.2. Study Design The Summer Harvest Adventure is a five-year, randomized controlled trial designed to determine the efficacy of a multicomponent obesity prevention intervention to improve the consumption of fruits and vegetables in youth; RDMI was offered as one intervention component. Enrolled participants were randomized to the intervention, The Summer Harvest Adventure, or an education-only control, My Summer Plate (Figure 1). For the study presented herein, in-depth data were collected among only those dyads randomized to the Summer Harvest Adventure in year 2, for an evaluation of methods and processes. The ten-week intervention included weekly [1] group child-focused education; [2] garden harvesting of fruits, vegetables, and herbs; and [3] RDMI for PACs. In total, descriptions of the study are informed by the Template for Intervention Description and Replication (TIDieR) checklist [54]. The Summer Harvest Adventure intervention has been described elsewhere (Clinicaltrials.gov: NCT05367674) [55,56]. ## 2.3. Weekly Intervention Sessions Each week of the intervention included education focused on obesity-preventive behaviors, including foundational healthy habits (e.g., avoidance of sugar-sweetened beverages), with an emphasis on fun physical activity and improving adherence to the US Dietary Guidelines for Americans [57,58]. Sessions included cooking demonstrations and taste tests, interactive lessons, and kinetic activities to integrate “play” into the week’s lesson. After each session, dyads could harvest produce from a professionally maintained 2.5-acre urban garden. ## 2.4. Remote Motivational Interviewing from a Registered Dietitian (RDMI) As part of the intervention, each PAC was introduced to the RD providing RDMI at baseline data collection visits (Figure 1). Here, participants were explained the purpose/structure of RDMI, asked for reasons for study enrollment, and asked to schedule an initial phone call for the first week of the ten-week intervention [59]. The purpose of RDMI was framed as being focused on the PAC’s behavior related to improving their own diet quality, not the child’s, but that such change could have a positive impact on the child’s behavior. Thereafter, the structure of this phone call occurred using methodology integrating MI with MNT, nutrition counseling, or nutrition education, as well as more directive styles consistent with Self-Determination Theory [60]. Initial phone calls lasted approximately 20 min, though could be altered as needed [61]. After completion of this initial call, contact was planned collaboratively based on the RD’s evaluation and PAC-communicated needs/preferences. In the instance of an unsuccessful call, the RD would attempt again the following week. Contact ceased after three unsuccessful interactions (e.g., no response, no return call) [62]. RDMI interactions focused on issues related to the dietary patterns of PACs; PACs were able to discuss other related topics of interest (e.g., weight loss) if desired. If PACs brought up questions regarding their child’s health (e.g., picky eating), the RD would answer these questions by drawing connections to PAC’s own dietary patterns. If requested, additional resources were delivered to PACs via email, however, counseling occurred only via phone. ## 2.5. Data Collection Comprehensive data collection visits occurred before and after (i.e., baseline and post-intervention) the ten-week intervention period for dyads, during which data were recorded using Research Electronic Data Capture (REDCap) tools hosted at The Ohio State University [63]. Additionally, data regarding PAC RDMI interactions were recorded, including the dates of interactions, summaries of PAC’s responses, and lengths of phone calls. ## 2.6. Survey Measurements At baseline, sociodemographic questions adapted from the Behavioral Risk Factor Surveillance System were administered [64]. Parental modeling was assessed via the Diet Structure Scale within the validated Home Self-Administered Tool for Environmental Assessment of Activity and Diet Family Food Practices Survey (HomeSTEAD) [65]. Items included six Likert-scale questions, with responses ranging from 1–5, e.g., “My child learns to eat healthy snacks from me.” An average score across items was computed; higher scores indicate more PAC modeling. Attitudinal ambivalence was assessed using the validated Change Questionnaire [66,67]. This includes 12 questions related to a specified target behavior, which for the present study was “improve the foods that you eat.” Each question incorporates a different aspect of “change talk” (e.g., “I want to make this change,” “I have to make this change,”), and is rated on a scale from 0 (“definitely not”) to 10 (“definitely”); higher scores indicate less ambivalence. ## 2.7. Dietary Patterns and Diet Quality PACs completed the National Cancer Institute’s Diet History Questionnaire III (DHQIII) [68]. Upon completion, DHQIII computes Healthy Eating Index 2015 (HEI) scores in order to assess overall diet quality, based upon adherence to the 2015–2020 US Dietary Guidelines [57,69]. Total HEI scores range from 0 to 100; a score of 100 represents maximal adherence. Children’s skin carotenoids were measured in triplicate on the palm of their right hand via resonance Raman spectroscopy using the Pharmanex NuSkin Biophotonic Scanner S3 (NuSkin Enterprises, Provo, UT, USA). Means were computed for a final score, reported in Raman intensity counts (RSS counts). ## 2.8. Anthropometric and Clinical Measurements For dyads, height was measured with a seca 213 portable stadiometer (seca North America, China, CA). Weight was measured on a Tanita SC-331S Total Body Composition Analyzer (Tanita, Arlington Heights, IL, USA). Height and weight were used to calculate body mass index (BMI) for PACs in kg/m2, as well as the BMI percentile for children, based upon age and sex. ## 2.9. Interaction and Process Data Data on all interactions with the RD were recorded. Each interaction was coded as a reciprocal interaction (RI) if the participant responded to the RD’s communication in any capacity, regardless of the content of the response. Each RI was then further classified as a dose of RDMI if the RD and participant engaged in a two-way interaction that resulted in MI, MNT, nutrition counseling, or nutrition education. Based on this coding, use was defined four ways: [1] RI Dose Received (RI), or a summation of the total number of individual contacts from the RD to which the participant responded at all, regardless of content of that response; [2] RDMI Dose Received, or the number of individual RIs that included the PAC’s thorough response to the delivery of MI, MNT, nutrition counseling, or nutrition education; [3] RDMI Time Received, or the total time (in minutes) of interaction with the RD that resulted in RDMI; 4) RDMI Engagement, calculated by dividing the number of completed RDMI interactions by the number of attempted interactions, computing a total percentage. PACs were classified as RDMI Completers or RDMI Non-Completers based on whether or not communication ceased after three consecutive failed attempts (i.e., reaching this cut-off point led to classification as a RDMI Non-Completer). The Health Care Climate Questionnaire (HCCQ) was administered to PACs at post-intervention to assess their perceived autonomy [70,71]. The HCCQ includes 15 items, each rating a different aspect of provider care (e.g., “I feel that my [provider] has provided me choices and options”). Each item is rated from 1–7, with an average of all items computed; higher scores indicate a greater emphasis on autonomy. Lastly, included in programmatic evaluations were questions related to satisfaction with the RDMI, which were administered at post-intervention, including “How would you rate the health coaching with [the dietitian]?” ( rated on a five-point scale from poor to excellent). ## 2.10. Attendance and Participation Dyad participation in the other intervention components was collected. At each weekly session, dyad attendance was collected. Although dyads were encouraged to attend these activities together, PACs could attend independently if the child was unable to do so, while children could attend alone as long as they were accompanied by an adult caregiver. Ten total sessions were offered, and the total number of sessions attended by either member of the dyad were summated. ## 2.11. Fidelity to MI With PAC approval, phone calls were audio-recorded for the purposes of MI fidelity assessment. Recordings were selected at random and reviewed with the Motivational Interviewing Treatment Integrity Tool (Version 4.2.1) [47]. A total of $10\%$ of all phone calls were randomly selected and reviewed for fidelity, and all met the benchmark for “good” MI, with the exception of the reflections to questions ratio, which reached an average of 1.6:1 [45,47]. ## 2.12. Statistical Analyses Descriptive statistics were computed for PAC socio-demographics, baseline characteristics, and measures of RDMI use. For the purposes of baseline descriptions, the cohort was defined in three ways: [1] all PACs who were classified as RDMI Completers; [2] all PACs randomized to the Summer Harvest Adventure who completed baseline and post-intervention assessments, regardless of RDMI use; [3] all PACs randomized to the Summer Harvest Adventure at enrollment regardless of follow up. Logistic regression was used to explore the relationship between PAC race (African American or white); baseline PAC BMI, PAC HEI, and Change scores; baseline child BMI percentile; and classification as a RDMI Completer. These variables were selected based on influences considered relevant based on the theoretical basis of the intervention (i.e., ambivalence), as well as potential influences, per the existing literature [72,73,74]. To explore the relationship between measures of RDMI use and both baseline measures of and changes in ambivalence, Spearman correlations were conducted given non-normality of data. Pearson correlations were used to explore the relationship between measures of RDMI use, percent change in HEI scores, and percent weight change from baseline to post-intervention; Spearman correlations were used to evaluate the correlation between RDMI Time Received and relevant outcomes due to non-normality of data. Lastly, individual multiple linear regressions were conducted to explore the relationships between measures of RDMI use and changes in PAC diet quality, child skin carotenoid scores, and PAC modeling, while controlling for dyad participation in the other components of the Summer Harvest Adventure. Unless otherwise specified, all assumptions were evaluated and confirmed based on visual and statistical inspection of data. Analyses were completed using SPSS (Version 25 or 26) and statistical significance was set at p ≤ 0.05. ## 3.1. Baseline Characteristics and Measures of RDMI Use Thirty-six PACs were enrolled in the Summer Harvest Adventure; $78\%$ ($$n = 28$$) participated in at least one RDMI session, and $44\%$ ($$n = 16$$) were considered RDMI Completers (Figure 2). Overall, a large portion of PACs were African American, and the average age of RDMI Completers was 38 years (SD = 6), while the majority were female ($81\%$), married ($44\%$), and employed ($94\%$) (Table 1). Measures of RDMI use indicated that among PACs who attended the baseline and post-intervention assessments, an average of three RDMI doses were received, equating to a total of 47.6 minutes of RDMI delivered. Among RDMI Completers, RDMI Engagement was $70\%$, and all measures of RDMI use increased sequentially among those randomized to the Summer Harvest Adventure, those being Summer Harvest Adventure completers, and those classified as RDMI Completers, respectively (with the exception of RDMI Dose Received) (Supplementary Table S1). Logistic regression was utilized to determine the likelihood of classification as a RDMI Completer versus RDMI Non-Completer (Supplementary Table S2). The odds of RDMI completion increased with increasing PAC BMI (OR = 1.080), indicating that for every one unit increase in PAC BMI, the odds of being classified as a RDMI Completer increased by $8\%$. Odds of RDMI completion decreased with increasing baseline child BMI percentile (OR = 0.997), baseline PAC HEI (OR = 0.992), baseline Change scores (OR = 0.652), and among those identified as African American (OR = 0.545). ## 3.2. Ambivalence A strong statistically significant relationship was found between percentage change in Change score from baseline to post-intervention and RDMI Dose Received (ρ = 0.533, $$p \leq 0.007$$). Overall, baseline Change scores were negatively correlated with all measures of RDMI use, including RDMI Time Received (ρ = −0.287, $$p \leq 0.173$$) (indicating higher ambivalence was associated with higher measures of dose). ## 3.3. PAC Clinical Outcomes and Diet Quality A weak positive correlation between measures of RDMI use and percentage weight change among PACs existed (e.g., $r = 0.291$, $$p \leq 0.178$$ for RI Dose Received). Weak to strong positive correlations existed between measures of RDMI use and percent change in the PAC HEI scores from baseline to post-intervention (RI Dose Received, RDMI Dose Received, RDMI Time Received, RDMI Engagement ($r = 0.250$, $$p \leq 0.238$$; $r = 0.342$, $$p \leq 0.102$$; ρ = 0.477, $$p \leq 0.018$$; $r = 0.358$, $$p \leq 0.086$$, respectively)). Differing contributions of various representations of RDMI use were observed in predicting the raw change in PAC HEI scores (Table 2). The lowest predictive capacity was seen in RDMI Time Received ($B = 0.055$), and was the highest for RDMI Dose Received ($B = 0.980$). These data indicate that for every one unit increase in RDMI Time Received, a 0.055 point increase was noted in HEI score, versus RDMI Dose Received, which, for every one unit increase, a 0.980 increase in HEI score was noted. ## 3.4. Child and Home-Related Outcomes The lowest contribution towards child skin carotenoids was noted for RDMI Engagement (β = −0.047, B = −0.027), and the highest was for RI Dose Received (β = 0.128, $B = 1.315$) (Table 3). That is, for every one unit increase in RI Dose Received, a $1.315\%$ increase in child skin carotenoid scores were noted. Regarding changes to the home environment (Table 4), similar trends were noted: a lower contribution was found for RDMI Engagement ($B = 0.088$, β = 0.228) versus RI Dose Received ($B = 3.559$, β = 0.506), the latter of which was significant ($$p \leq 0.026$$). That is, for every one unit increase in RI Dose Received, a $3.559\%$ increase in PAC modeling was observed. Collectively, Summer Harvest Adventure attendance and RI Dose Received accounted for the greatest variability in both child skin carotenoids (adjusted R2 = −0.055, f2 = 0.037) and PAC modeling (adjusted R2 = 0.144, f2 = 0.279). ## 3.5. Programmatic Evaluations and Autonomy Fifty-eight percent of PACs ($$n = 14$$) rated RDMI as “excellent”, $21\%$ ($$n = 5$$) as “very good”, and $4\%$ ($$n = 1$$) as “good”; $17\%$ ($$n = 4$$) stated they “did not use” RDMI. Of those, $$n = 2$$ did not engage in any RDMI interactions. However, $$n = 2$$ did, reporting “Did not really know what to discuss…and find the recipes helpful we learned at the garden study…” and “I am already well-versed in nutrition and healthy living and employ those practices in my everyday life, consistently.” Participants who completed at least one RDMI session reported a mean score of 6.5 out of 7 (SD = 0.87, range 4.3–7) on the Health Care Climate Questionnaire. ## 4. Discussion The objective of this study was to explore the preliminary effects of PAC-focused RDMI on outcomes among dyads in a multicomponent obesity prevention intervention. Addressing PACs in this context is critical [13]. The current study provides initial support for use of MI as a means to do so, as it facilitates supportive environments for children to learn and modify behavior through social learning, corroborating previous work [29,75,76]. There is a large body of work regarding the application of MI, with a direct emphasis on child behavior [32]; this study fills a gap regarding the utility of MI focused on PAC behavior. A primary advantage of using MI is its focus on the psychological and/or cognitive predictors of behavior change (e.g., ambivalence) versus an emphasis on passive education/knowledge, or more paternalistic approaches. Such an appreciation for the varying predictors of, and/or contributors to health behavior and its related outcomes (e.g., psychological factors, mental health), is essential for prevention strategies. This characteristic of MI may contribute to its potentially positive effect on health outcomes among underserved populations, including diverse populations [77]. The present cohort consisted, primarily, of women who self-identified as African American. Some previous MI literature has suggested MI may not be as well-received by African American populations [78]. However, the cohort in this study responded well, which may be the result of the integrated approach. African American cohorts may prefer more directive styles of counseling, thus the flexible approach taken in this study may be advantageous [60,73,78,79]. This is reflected in MI fidelity assessments (e.g., a lower reflections-to-questions ratio (1.16:1)). African American participants were more likely to be classified as RDMI Non-Completers, however, this provides evidence for a need to further understand the appropriate tailoring of the design and understanding the ideal dose of RDMI, and does not indicate poor compliance per se. In reality, the majority of participants completed more than one RDMI interaction, with improvements in outcomes (e.g., diet quality), thus the criteria for “completion” may be too exclusionary and require future revisions. An average of three to four doses were delivered, and as few as one dose of MI may modify behavior. Focusing on number of doses, rather than rate of completion of planned doses, may be a more appropriate approach [80,81]. Further examination of race/ethnicity, as well as other demographic variables (e.g., marital status, education), as potential moderators should be conducted in future MI research, and be used to adapt more culturally or socially tailored approaches. This study documents that RDMI use was associated with improvements in PAC health behavior. Specifically, for every one-unit increase in RDMI Dose Received, there was a 0.98-point increase in HEI scores. This equates to an improvement of nearly ten points if one RDMI session was completed every week of the 10-week intervention. Though these results were not significant, this may be secondary to the small sample size, and nonetheless provide an indication of potential impacts that may be elucidated in fully-powered trials. In various populations, HEI improvements greater than five points are associated with positive health outcomes [82,83]. Interestingly, child skin carotenoids did not similarly respond to RDMI, but instead responded to general dose, regardless of content (i.e., RI Dose Received). Similar trends were observed for PAC Modeling, suggesting that the quality of interactions was a greater predictor of change within PACs, but for child or family-related outcomes, quantity of interactions was paramount. This suggests differing mechanisms. Although research indicates positive effects on children when PACs engage in health behavior, dieting can negatively impact a child’s health if it results in negative messaging in the home [84,85]. Though the present study focused on PAC diet quality (i.e., not dieting) it is possible that those more likely to engage with the RDMI could be delivering different indirect messages in the home. Further, RDMI in this study was one component of a multicomponent intervention, and interest remains in understanding the unique contributions of RDMI as an “active ingredient.” Attendance/participation in other study components did contribute to diet-related outcomes, though to varying degrees, and in some instances, RDMI demonstrated a greater contribution. RDMI Engagement and Summer Harvest Adventure attendance/participation accounted for the greatest variability in PAC diet quality (i.e., $9.5\%$). Regarding changes in child skin carotenoids, RDMI had a substantial impact on the context of the intervention, which may provide support for options enabling remote intervention delivery, to account for families’ busy schedules and to address common issues (e.g., travel) [86,87,88]. Results of logistic regression indicate that PAC BMI was the strongest predictor of RDMI completion. The literature indicates that confronting weight status may negatively impact treatment engagement and outcomes; thus, the lack of focus on these issues in this study highlights a strength [89,90]. Delivery of RDMI in this study instead focused on diet quality, which can encompass a variety of factors (e.g., added sugars, fruits/vegetables). While such dietary factors may subsequently influence weight status, focusing on a flexible change goal enables greater individual autonomy, allowing weight-related discussions per participant preference, but not forcing them. This finding is further augmented by the use of RDs, who may be better equipped to navigate such conversations [91]. Despite MI’s role in addressing ambivalence [92], few studies measure ambivalence as either an outcome or potential mediator/moderator. This includes studies employing “opportunistic MI”, that is, MI for individuals who did not specifically present to receive MI/counseling [92,93]. Such approaches are promising, though often short in duration [94]. This study was opportunistic in the mechanism by which MI was presented to individuals, but maintained a more traditional length/duration. Interestingly, we found lower ambivalence made PACs less likely to be RDMI Completers, while baseline Change scores were negatively correlated with RDMI use. That is, those with higher ambivalence used more RDMI, and sustained a decrease in this ambivalence. Though these results cannot be attributed to RDMI alone, they do suggest that increasing RDMI’s reach to those with high ambivalence may improve engagement in interventions and outcomes. This study had numerous strengths, including a detailed examination of MI from a RD, including in a diverse cohort, as well as for the purposes of modifying PAC behavior in the context of a child-focused pediatric obesity prevention intervention. However, this study is not without limitations. As data for one year of the overarching five-year trial are presented, the sample size is small, limiting interpretation and potentially explaining the absence of statistical significance. In addition, in this study, the short-term follow up may be insufficient to observe sustained changes in health or behavior, and limit the interpretation of long-term or sustained implications. In conclusion, results indicate MI from a RD delivered to PACs can improve ambivalence regarding diet quality, as well as measures of diet, in both PACs and youth, including among PACs with high ambivalence. ## 5. Conclusions This research provides preliminary support for the use of MI from a RD to directly target PAC diet and indirectly target child diet and the home environment as a means of obesity prevention, and to improve cancer prevention and control efforts. 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--- title: Novel Approach for Glycemic Management Incorporating Vibration Stimulation of Skeletal Muscle in Obesity authors: - Mijin Kim - Hanlin Zhang - Taeho Kim - Yutaro Mori - Tomohiro Okura - Kiyoji Tanaka - Tomonori Isobe - Takeji Sakae - Sechang Oh journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048751 doi: 10.3390/ijerph20064708 license: CC BY 4.0 --- # Novel Approach for Glycemic Management Incorporating Vibration Stimulation of Skeletal Muscle in Obesity ## Abstract Because obesity is associated with impaired glucose tolerance and type 2 diabetes (T2D), it is important to manage the blood glucose level at an early stage. Nevertheless, people with obesity have significantly lower resistance to muscle fatigue after exercise and exercise adherence. Therefore, we developed a novel “Relaxing-Vibration Training (RVT)” consisting of 25 postures using vibration stimulation of skeletal muscle and determined the feasibility of RVT for glycemic management. Thirty-one participants with obesity were enrolled in a controlled trial (CT) and experimental trial (ET) based on a 75 g oral glucose tolerance test (OGTT). During the CT, participants were required to rest in a quiet room. During the ET, the RVT program (50 Hz, 4 mm), consisting of 25 postures of relaxation and stretching on the vibratory platform, was performed for 40 min. Subsequently, the participants rested as in the CT. Subjective fatigue and muscle stiffness measurements and blood collection were conducted before and after RVT. In both the CT and ET, interstitial fluid (ISF) glucose concentrations were measured every 15 min for 2 h. The incremental area under the curve value of real-time ISF glucose during an OGTT was significantly lower in the ET than in the CT (ET: 7476.5 ± 2974.9, CT: 8078.5 ± 3077.7, effect size $r = 0.4$). Additionally, the levels of metabolic glucose regulators associated with myokines, muscle stiffness, and subjective fatigue significantly improved after RVT. This novel RVT suggests that it is effective in glycemic management with great potential to improve impaired glucose tolerance and T2D with obesity in the future. ## 1. Introduction The increasing prevalence of obesity directly and indirectly contributes to increased morbidity and mortality, including type 2 diabetes (T2D) [1]. Considering that impaired glucose tolerance (IGT) and T2D are paradigms of obesity-related disorders, it is necessary to manage the blood glucose metabolism in people with obesity at an early stage. Exercise and diet restriction have been recommended as crucial preventive treatment measures. Exercise is recognized to be more effective in the prevention and improvement of insulin resistance and T2D in the long term because it increases mitochondrial biogenesis and improves glucose tolerance and insulin action [2]. However, people with obesity have significantly lower muscle fatigue resistance after exercise than those with non-obesity [3]. Additionally, patients with T2D have greater muscle fatigability in both lower and upper body muscles [4]. Even though exercise is effective in regulating glucose metabolism, most people with obesity maintain a sedentary lifestyle, and exercise adherence is low because of physical limitations, musculoskeletal discomfort, and physical and psychological fatigue [5]. Therefore, there is a need for an alternative to conventional exercise that is relaxing, safe, easy to continue, and effective. Recently, vibration training (VT), which contracts and relaxes skeletal muscles by vibration stimulation without a mass load or dynamic exercise, has received considerable attention. This mechanical stimulation uses proprioceptive spinal reflexes to induce an amyotrophic stretch reflex mediated by the muscle spindle and a type-Ia sensory fiber, thereby facilitating activity of homonymous α-motor neurons [6]. It has been proved that this technique has a similar benefit to resistance exercise based on voluntary muscle contraction [7]. VT is effective in plasma glucose regulation because it increases glucose utilization by causing voluntary muscle contraction through vibration stimulation [8]. In addition, it has been reported to be effective in improving glycemic indicators and lipid-related cardiovascular risk factors [9]. However, most VT was designed as an alternative to resistance exercise movements such as squats and lunges, which are accompanied by muscle pain, and fatigue, as we have previously experienced [10,11]. Therefore, the former approach may also be unsuitable for those who do not favor exercise and those with limitations in mobility and posture among people with obesity. On the basis of these considerations, in this pilot study, we aimed to advance a previously general VT that requires high-intensity resistance posture and developed a novel “Relaxing Vibration Training (RVT)” program that can be performed more comfortably and safely. As a first stage, we conducted an acute trial to verify the feasibility of a novel RVT for glycemic management. Using interstitial fluid (ISF) glucose concentrations during an oral glucose tolerance test (OGTT), blood markers, and muscle stiffness and fatigue were determined in middle-aged and older adults with obesity. We hypothesized that our practice of RVT has a more positive effect on subjective and biochemical indicators and has potential viability as a glycemic management program. The primary outcome was a change in the ISF glucose concentration, and the secondary outcomes were a change in blood markers, muscle stiffness, and fatigue. ## 2.1. Ethical Approval and Study Design This study of a single-arm, acute intervention design was conducted for 7 days (March 2021) at the University of Tsukuba. The study objectives, design, criteria of inclusion and exclusion, assessments, practice of RVT, OGTT, insurance compensation for injury, withdrawal of consent, and privacy protection were explained face-to-face to eligible participants. Written informed consent was obtained from each participant, the study was conducted in accordance with the Declaration of Helsinki and was approved by the ethical committee of the University of Tsukuba (reference no. Tai 020-95). The study protocol was registered at the University Hospital Medical Information Network center (UMIN no. 000042787). We applied the devices, the Free-Style Libre Flash continuous glucose monitoring (FSL-CGM) system (Abbott Diabetes Care, Witney, UK) and the Polar A370 fitness tracker (Polar Electro Oy, Kempele, Finland), to study the participants’ bodies on the first day. Thereafter, the controlled trial (CT) was conducted on the fourth day and the experimental trial (ET) on the seventh day. Participants were required to fast for more than 10 h before each trial and were forbidden to consume alcohol or perform excessive exercise on the day before the trials. In both trials, participants ingested 225 mL soda-flavored solution (TRELAN® G75; Ajinomoto Pharmaceuticals Co., Ltd., Tokyo, Japan) containing 75 g glucose and performed an OGTT lasting for 2 h, the ISF glucose concentration being recorded every 15 min. The details of the CTs and ETs are as follows (Figure 1): ## 2.1.1. CT Participants were asked to fill out a questionnaire consisting of questions about age, sex, smoking, alcohol intake, and medical history, and to measure blood pressure and heart rate (OMRON HEM-7111, Kyoto, Japan). Additionally, during the 2 h OGTT, participants were required to rest while measuring ISF glucose concentrations every 15 min on a chair in a quiet room. They were allowed to perform tasks such as reading a book, watching a movie, or operating a computer. ## 2.1.2. ET Participants performed RVT for 40 min, starting 15 min after the intake of a 75 g glucose solution. Subjective fatigue surveys, muscle stiffness measurements, and blood collection were conducted before and after RVT. Subsequently, the participants moved to a quiet room and rested as in the CT. In this study, we used a vibration machine (Pro5 AIRdaptive; Power Plate, Badhoevendorp, The Netherlands) that can deliver three-dimensional harmonic vibration to the body. An expert with a power plate instruction certificate developed a novel RVT consisting of 25 postures of relaxation and stretching. The RVT was performed for 1 min per posture with a frequency of 50 Hz and an amplitude of 4 mm for 40 min on the vibratory platform, including preparation time for the next posture and rest (Figure S1). ## 2.2. Participants On estimating the sample size using the statistical software G* Power 3.1, 34 subjects were required as the total sample size (a priori effect size = 0.5, α = 0.05, power [1 − β] = 0.8). Forty participants with obesity residing in Tsukuba City, Japan were recruited through snowball sampling and a regional information magazine (Joyo Living Co., Ltd., Tsukuba, Japan). A screening survey via telephone or face-to-face interviews was conducted using a self-reported questionnaire. The inclusion criteria were: [1] age ranging over 40–74 years, [2] body mass index (BMI) ≥ 25 kg/m2, [3] having one or more risk factors for metabolic syndrome, [4] active participation in the study. The participants were excluded if they [1] took neuropsychiatric drugs, [2] were prohibited from exercising by doctors due to serious diseases, including brain dysfunction, renal disease, liver dysfunction, heart disease, and peripheral angiopathy, [3] had an excessive alcohol intake (>60 g/day) [12], [4] had participated in other clinical studies within the past three months, [5] were pregnant or possibility pregnant, [6] were judged inappropriate by the lead principal investigator, for example, conduct that interferes with the progress of the research by not cooperating with the research, making a fuss, or fighting with other participants. In total, 40 people with obesity applied for this study. However, four applicants were excluded according to the criteria and two applicants declined to participate because of conflicting schedules. Additionally, the participants experienced RVT for approximately 5 min to check whether there were any problems on the body and whether it was feasible. We finally analyzed data from 31, excluding 3 participants who did not complete RVT for 40 min due to personal reasons (a posteriori effect size = 0.5, α = 0.05, power [1 − β] = 0.8) (Figure 2). ## 2.3. ISF Glucose Concentrations The glucose concentration was determined using the FSL-CGM system, which has the advantage of being able to measure ISF glucose concentrations in real-time without blood collection through a sensor attached to the subcutaneous tissue. The FSL-CGM system consists of a glucose reader and sensor and the activation time for the sensor is 14 days, with the sensor’s high accuracy and convenience having been shown in previous studies [13,14]. Before the study, a health professional attached a sensor of the FSL-CGM to the rear upper arm of the participants under an aseptic technique and monitored it for 48 h to confirm the adaptability and stability of the ISF glucose concentration measurement. Participants were asked to set the alarm for the 2 h OGTT in both trials and measure the ISF glucose concentrations by touching the reader to the sensor every 15 min and recording the value in the datasheet. ## 2.4. Characteristics of Participants BMI (kg/m2) was calculated as body weight in kilograms divided by height in meters squared. Fat and muscle mass were determined using a bioelectrical impedance analyzer (MC-980A, TANITA, Tokyo, Japan). We measured the waist circumference (WC), blood pressure, fasting blood glucose, high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) to confirm whether the participants displayed the corresponding risk factors for metabolic syndrome. Risk factors for metabolic syndrome were evaluated according to the Japanese standards as follows: abdominal obesity (WC: male ≥ 85 cm, female ≥ 90 cm), high blood pressure (systolic blood pressure ≥ 130 mmHg, diastolic blood pressure ≥ 85 mmHg, or a history of hypertension), high fasting glucose (≥110 mg/dL or a history of diabetes), low HDL-C (<40 mg/dL), and hypertriglyceridemia (≥150 mg/dL or a history of hyperlipidemia) [15]. To measure the amount of daily physical activity (PA) and sleep time, participants were required to wear a Polar A370 fitness tracker based on a wrist-worn three-axis accelerometer model for the entire study period of 7 days [16]. The number of walking steps, total sleep time, and PA time by intensity (light, moderate, vigorous) were divided into baseline and during the experiment, and the mean values of each were calculated. ## 2.5. Blood Markers Blood samples were collected from an antecubital vein and separated fractions were stored at −80 °C until further analysis. The free fatty acids (FFAs) and HDL-C levels were determined by enzyme method, lactate dehydrogenase (LDH), aspartate transaminase (AST), and creatine kinase (CK) levels by the Japan Society of Clinical Chemistry transferable method, fasting serum glucose level by the hexokinase-G-6-phosphate dehydrogenase method, high-sensitivity C-reactive protein (hs-CRP) level by fixed time assay method, and cortisol level by radioimmunoassay. We evaluated serum levels of fibroblast growth factor 21 (FGF21; R&D Systems; Minneapolis, MN, USA), interleukin 6 (IL6; R&D Systems), and myostatin (Cusabio biotech, Wuhan, China) using commercial enzyme-linked immunosorbent assay kits. ## 2.6. Muscle Stiffness and Fatigue Muscle stiffness of the trapezius, deltoid, biceps brachii, rectus femoris, biceps femoris, tibialis anterior, and medial gastrocnemius on the dominant side was determined before and after RVT in the ET using the Myoton® PRO (Myoton AS, Tallin, Estonia), an accurate and reliable device for non-invasive digital palpation of superficial skeletal muscles. The location of the muscle to be measured was marked, and the probe of the Myoton® PRO device was vertically mounted on the surface of the measuring mark point as suggested by the manufacturer [17]. Three consecutive measurements were performed on each muscle area, and the mean value for each was used for statistical analysis. Additionally, subjective fatigue was assessed before and after RVT using the questionnaires “subjective symptoms of fatigue (Jikaku-sho shirabe)” and “body parts of fatigue (Hirou-bui shirabe)”, developed by the Japan Occupational Health and Occupational Fatigue Research Committee. The subjective symptoms of the fatigue questionnaire comprised 25 items divided into five categories (I: drowsiness, II: instability, III: uneasiness, IV: local pain or dullness, V: eyestrain). The body parts of the fatigue questionnaire investigated subjective fatigue levels for each body part, including the neck, shoulder, middle back, upper arm, forearm, lower back, hand, hip and thigh, lower leg and knee, and foot [18]. ## 2.7. Statistical Analyses Linear mixed model analysis was applied to evaluate the differences in the intervention effect of the two trials on the change of ISF glucose concentrations. On the basis of significant interactions (trials × times), this study performed post hoc analysis with Bonferroni correction. Wilcoxon signed-rank test was used only in the ET to compare changes in the incremental area under the curve (IAUC) of the ISF glucose concentration response, blood markers, muscle stiffness, and fatigue before and after RVT practice. The IAUC value is the sum of the areas of triangles and rectangles geometrically calculated using the elapsed time from baseline to 120 min and the response value of ISF glucose concentration. On the basis of the fasting blood sugar (0 min), the sum of increased values at 15 min (A), 30 min (B), 45 min (C), and 60 min (D; divided by 2) were multiplied by 15 and the sum of the increased values at 90 min (E) and 120 min (F; divided by 2) were multiplied by 30 {IAUC = (A + B + C + (D/2)) × 15((D/2) + E + (F/2)) × 30} [19]. To verify the effect sizes (0.1: small, 0.3: medium, 0.7: large) of all variables from the baseline to after the RVT, the effect size r was calculated as the Z statistic divided by the square root of the sample size (N) (Z/√N). All statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA), with significance levels set to $p \leq 0.05.$ The rate of change (Δ (%)) was calculated by subtracting the baseline value from the value after RVT practice and was expressed as a percentage of the baseline value. ## 3.1. Characteristics of Participants As shown in Table 1, the age of the 31 participants ($48.4\%$ female, $51.6\%$ male) was 55.6 ± 8.4 yr. All participants had abdominal obesity, and it could be inferred that the participants were of the type with much muscle mass (49.5 ± 11.2 kg) but also considerable fat mass (male: 25.3 ± 7.6 kg, female: 30.3 ± 9.5 kg). Additionally, there were no significant changes in the sleep time, walking, or PA during the experiment period (Table S1). ## 3.2. Comparison of ISF Glucose Concentrations by the OGTT in Two Trials Figure 3A shows a comparison of the changes in ISF glucose concentrations with time of the two trials, focusing on within 1 h of the OGTT and before and after RVT practice. A significant interaction ($$p \leq 0.014$$) was observed between ISF glucose concentrations and the two trials, and according to the post hoc analysis, the ISF glucose concentrations in the ET was significantly reduced at 45 min ($$p \leq 0.019$$) and 60 min ($$p \leq 0.001$$). Figure 3B shows the comparison of the IAUC of ISF glucose concentrations of the two trials during 2 h of the OGTT, with the ET being significantly lower than the CT ($$p \leq 0.047$$). ## 3.3. Changes in Blood Markers after RVT FGF21 and myostatin as markers of metabolic glucose regulation were significantly decreased (Figure 4). FFAs as relative marker of lipid utility was significantly decreased (Figure 4). Additionally, there were no changes in the muscle-damage markers CK, AST, LDH, or hs-CRP after RVT (Table 2). ## 3.4. Changes in Muscle Stiffness and Fatigue after RVT The stiffness of the medial gastrocnemius was significantly decreased ($p \leq 0.05$, r = −0.43). Regarding the fatigue of body parts, the upper-body parts ($p \leq 0.01$, r = −0.65), lower-body parts ($p \leq 0.01$, r = −0.63), and total score of the whole body ($p \leq 0.01$, r = −0.71) decreased significantly. Additionally, drowsiness ($p \leq 0.01$, r = −0.51), instability ($p \leq 0.05$, r = −0.42), local pain or dullness ($p \leq 0.01$, r = −0.71), eyestrain ($p \leq 0.01$, r = −0.64), and total score of fatigue symptoms ($p \leq 0.01$, r = −0.70) decreased significantly after RVT (Table 2). ## 4. Discussion Vibration stimulation of skeletal muscles and tendons activates muscle spindles, which is termed “tonic vibration reaction”. It activates more motor neurons, which in turn activates motor units and the movement of actin and myosin, increasing muscle contraction [20]. Such muscle contraction increases the content of glucose transporter 4 in muscle cells and its translocation to the sarcolemmal membrane, which considerably improves glucose transport capacity [21,22]. As such, muscle activation aids in glucose and insulin control, thus it has been actively suggested for T2D patients. Therefore, for those people with obesity who have a sedentary lifestyle or the elderly with weak joints and strength, muscle contraction by the tonic vibration reaction is considered as a more effective treatment. A meta-analysis study reported that VT interventions could reduce fasting blood glucose concentrations by 25.7 mL/dL in older adults with T2D [23]. It has been reported that 12 weeks of VT and strength training decreased the IAUC values of the OGTT [24], and that glycosylated hemoglobin values improved after VT for 6 [25] and 12 weeks [24]. By contrast, a previous study that performed acute VT reported reduced blood glucose concentrations in both diabetic patients and healthy elderly women [26]. In our pilot study, designed as an acute intervention, ISF glucose concentrations were significantly lower with respect to the IAUC values of the 2 h OGTT as well as during the practice of RVT (45 min) and immediately after the end of RVT (60 min). Overall, our study supports the positive result of previous studies that the glucose concentration can be controlled regardless of whether the period of skeletal muscle vibratory stimulation is acute or chronic. When muscle tissue is stimulated, it secretes myokines, which affect inflammation, glucose processing, and adipose tissue. FGF21 and myostatin, myokine factors, are known to be associated with obesity and insulin resistance [27]. It was reported that FGF21 is involved in glucose metabolism regulation and promotes blood glucose absorption by adipocytes [28]. However, “paradoxical” plasma FGF21 elevation in obesity and diabetes suggests a potential FGF21-resistant state [29]. Additionally, myostatin increases with obesity and with a lack of exercise, which is involved in the acquisition of insulin resistance [30]. Therefore, a balanced FGF21 level and a reduced myostatin level are also attracting attention as potential therapeutic targets for insulin resistance in T2D. In older men, the intervention effect of resistance exercise decreased FGF21 and myostatin and increased muscle strength in both T2D and non-T2D subjects [31]. After performing RVT in the present study, there were great decreases in the ISF glucose concentration, FGF21, and myostatin. Contrary to the results of this study, it was reported that acute VT in both overweight and normal weight subjects displayed a time-dependent response in IL6, glucose, and insulin, but no change in myostatin [32]. By contrast, myostatin mRNA expression started to decrease 1 h after resistance exercise and was most suppressed after 8 h, while IL6 expression was highest after 4 h [33]. In the previous studies above, it was shown that glucose metabolism and myokines react in response to muscle contraction, whether voluntary or involuntary; however, opinions are still divided on the reactions as time course and the physical interaction. Also, FFAs as a relative marker of lipid utility were significantly decreased. The RVT may stimulate the hydrolysis of TG, which results in the release of FFAs to circulation and their oxidation in skeletal muscles. The elevated blood lipid level, observed in obesity and metabolic syndrome, is an adverse condition that leads to lipotoxicity and ectopic fat deposition in other organs, and consequently insulin resistance and impaired glucose metabolism [34]. Thus, efficient uptake and oxidation of FFAs in working muscles by intense vibration may be effective for retracting their high blood glucose levels. Moreover, because vibration stimulation of skeletal muscle has different physiological effects depending on the parameter settings (e.g., frequency, amplitude, posture), the protocol should be carefully set by a certified professional [35]. In a previous study, VT was set at 12–16 Hz frequency, 4 mm amplitude, and resistance exercise [9], and in another study, it was set at 30 Hz frequency, 2 mm amplitude, and squat posture [36]. Both studies showed a significant decrease in fasting blood glucose in the VT group compared with the control group. Compared with the protocols of the previous studies (12–30 Hz, 2–4 mm), that of the present study (50 Hz, 4 mm) was set a higher, but was consistent with the fact that vibration stimulation of skeletal muscle can modulate glucose concentration. Hazell et al. reported that the greater the amplitude (4 mm) and frequency (35, 40, 45 Hz) of VT, the greater the measured electromyography activity of the muscle in both static and dynamic contractions [37]. It was also suggested that the frequency should not be <20 Hz to avoid the resonance frequency range [35]. Despite the high frequency and amplitude, no adverse events were reported from the participants during RVT, rather, the results that the muscle stiffness and fatigue improved showed satisfaction with this RVT. More importantly, we need to focus on the specific postures that were performed on the vibrating platform. High-intensity postures such as resistance exercise are difficult protocols for the elderly or diabetic patients in the high-frequency and high-amplitude settings. Because people with obesity and T2D have lower exercise adherence than healthy adults, strenuous resistance exercise accompanied by muscle fatigue is more likely to reduce exercise continuity [3,4]. The novel development of RVT in the present study significantly improved medial gastrocnemius stiffness, drowsiness, instability, local pain or dullness, eye strain, and whole-body fatigue. Moreover, after RVT practice in this study, the muscle-damage markers CK, AST, LDH, and hs-CRP did not change. In the participants with fibromyalgia, VT exercise (30 Hz, 2 mm) and conventional exercise significantly reduced pain and fatigue compared with the control group; however, stiffness and depression did not change. Additionally, it was insufficient to prove the effect of VT alone [38]. Another previous study reported that an acute VT performed for 60 sec in a half-squat posture on a vibrating platform set at 35 Hz and 5 mm was effective in reducing delayed-onset muscle soreness, the pressure pain threshold, and CK in young adults [39]. Delayed-onset muscle soreness after strenuous exercise such as resistance exercise and sprint were reduced by 22–$61\%$ after muscle massage with five stretching movements of lower extremities on a vibrating machine (30–50 Hz, 2 mm) [40]. Because vibration stimulation increases blood flow, it is effective in the excretion of fatigue substances. In addition, because the stiffness of the muscle is softer, the muscle is activated, such that muscle pain and fatigue are quickly recovered. Therefore, compared with the conventional exercise method, this novel RVT has advantages in the ability of glucose regulation and improvement of muscle fatigue while its practice is comfortable and safe. This pilot study has several limitations. First, it did not include diabetic patients exclusively. Because it is at an early stage to verify the feasibility of a novel RVT, we were concerned about unexpected side effects in diabetic patients, including hypoglycemia and data instability. As mentioned in the introduction, we focused on people with obesity, because glucose regulation is important at an early stage from a prevention standpoint before the onset of IGT and T2D in obesity. Second, this pilot study was designed as an acute trial. The glucose concentration responds temporarily and sensitively to acute external stimuli such as diet and exercise. Therefore, we decided that it was necessary to first verify the novel RVT through an acute intervention trial. Because positive results were obtained in this situation, it is expected to show positive results in long-term interventions. Third, there was sampling bias and a relatively small sample size. Through the statistical software G* Power 3.1, 34 and 31 participants were estimated as a sample size by setting a power (1-β) of 0.8; however, it did not satisfy the sample size required for 0.95, which is the best power. However, a 1-β of statistical power indicates the possibility of Type II error (β) occurrence, with 0.8 meaning that even with $80\%$ significant power, there is a $20\%$ chance of not discriminating a significant difference. Additionally, it was reported that a power of ≥0.8 is more suitable for having a statistically significant difference [41]. Fourth, this study was designed as a single-arm, acute intervention, with the same person performing the CT first, followed by the ET at timed intervals. Therefore, in the next intervention study, it will be necessary to divide the participants into two groups and complete the CT and ET in a counterbalanced order. Based on results of this study, we would perform mass clinical studies exclusively on diabetic patients. ## 5. 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--- title: Comparative Study of Sensory and Physicochemical Characteristics of Green-Tea-Fortified Cupcakes upon Air Frying and Oven Baking authors: - Hiu-Lok Ngan - Shu-Yu Ip - Mingfu Wang - Qian Zhou journal: Foods year: 2023 pmcid: PMC10048755 doi: 10.3390/foods12061266 license: CC BY 4.0 --- # Comparative Study of Sensory and Physicochemical Characteristics of Green-Tea-Fortified Cupcakes upon Air Frying and Oven Baking ## Abstract The air fryer and the oven are common cooking methods in our daily lives. However, previous investigations of the air fryer were limited to its comparison with deep-fat frying. This study compared the differences between air frying and household oven baking (without a fan or other forced airflow inside) on food quality and physicochemical properties using a cupcake model. Results showed that the oven-baked cupcakes were softer in texture ($87.15\%$), greener in color ($6.07\%$), and lower in weight loss ($7.78\%$) and toxic advanced glycation end products (AGEs, $21.40\%$) when the heating temperature and duration were the same as oven baking. To improve the sensory characteristics and health value, the cupcakes were fortified with green tea. The differences in texture, color, and level of toxicants between the two cooking methods were diminished after the addition of green tea. Moreover, the chemical profiles of green tea catechins in the green-tea-fortified cupcakes remained similar upon thermal cooking, except that the air-fried cupcakes were lower in gallic acid (GA) but higher in (−)-gallocatechin (GC). Collectively, based on the differences in heating mechanisms, our data indicated that oven baking is a better cooking method suitable to prepare cupcakes than air frying from the perspectives of sensory characteristics and food safety, while green tea additives effectively counter the drawbacks of the air fryer. ## 1. Introduction Thermal cooking methods, including baking and frying, are essential for food preparation in both the food industry and home cooking. The term baking is preferred to describe the preparation of dough products, and it is defined as “cooking food in an oven in air to which water vapor may or may not be added”. Frying is to dehydrate food through a heat transfer medium, such as air, fat, or oil, in direct contact with the food [1]. Among a variety of frying techniques, air frying technology was developed and became popular for the household preparation of fried foods to reduce the use of oil and to achieve a similar product quality [2,3]. The air fryer is mainly based on using the circulation of hot air to cook foods. Hence, due to their similarity in thermodynamic properties, many previous studies compared the differences between air frying and deep-fat frying in microstructure and sensory properties by using starch-based food models, including doughnuts and French fries. Particularly, Gouyo et al. pointed out that deep-fat-fried French fries were crispier than air-fried samples, which was possibly owing to the difference in the pore diameter and pore size distribution in the crust. Ghaitaranpour et al. found that air-fried doughnuts were smoother and softer on the surface than deep-fat-fried samples [4,5,6]. However, air frying is different from deep-fat frying from the perspective of food engineering. The air fryer intrinsically is an oven-designed cooker to improve the circulating airflow problem of the conventional oven [3]. By modifying the shape of the heating chamber, air and food materials are forced to contact with the enhanced air velocity in the air fryer than in the conventional oven [7]. In the conventional oven, foods are cooked only by the heat source (the heat pipes), which has no fan or other forced airflow apparatus inside. To date, only a few works have been reported comparing air frying with oven baking [8]. To be specific, the comparisons were merely limited to unraveling the impact of airflow on the product volume in a conventional oven by using starch-based models such as bread and moist cakes [9,10]. Hence, a comprehensive evaluation of the product quality of air-fried food and a comparison with oven-baked food is needed. Food additives are commonly used to improve the nutritional value, sensory characteristics, and shelf-life of foods. Agricultural products or their extracts, which contain a high level of polyphenols, have been used as natural additives in bakery products [11]. Thereinto, green tea is a potential candidate due to its beneficial effects such as anti-carcinogenicity, cardioprotective ability, neuroprotective ability, and its attenuating effect on Parkinson’s disease [12,13]. In addition, green tea and its extracts are widely added to a variety of cooking foods to offer a characteristic flavor and additional health benefits. The active components in green tea were identified as caffeine (CAF) and catechins (GTCs). These GTCs included (−)-epigallocatechin-3-gallate (EGCG), (+)-catechin (CAT), (−)-epicatechin-3-gallate (ECG), (−)-epigallocatechin (EGC), (−)-gallocatechin-3-gallate (GCG), (−)-epicatechin (EC), (−)-catechin-3-gallate (CG), and (−)-gallocatechin (GC), with EGCG being the most abundant component [14,15]. GTCs are known as strong radical scavengers and inhibitors against the formation of some harmful Maillard reaction products (MRPs) [16,17]. For example, EGCG can trap 3-oxo propanamide to prevent the formation of carcinogen acrylamide in several food systems [18,19,20] and scavenge reactive di-carbonyl species (RCS) to inhibit the formation of pro-inflammatory advanced glycation end products (AGEs) in bakery foods [21,22]. AGEs are a group of heterogenous compounds that are formed at the late stage of the Maillard reaction during thermal processing. Abundant in sugar, proteins, and oils, cupcakes tend to form toxic AGEs because of reactions that start from the carbonyl group of reducing sugar and the amino group of proteins and oils [17]. GTCs were reported to show potential for the development of safe food products [16]. Therefore, it is still unknown whether under the two cooking methods, air frying and oven baking, GTCs can have different effects on the formation of harmful MRPs or not. The present research first compared the sensory and physicochemical characteristics of cupcakes prepared by using air frying and oven baking. The impacts of green-tea-fortified cupcakes on these characteristics were then examined for both cooking techniques. Considering that AGEs are the predominant MRPs in starch-based foods, AGEs were chosen as representative hazardous substances, and their levels in air-fried and oven-baked cupcakes were detected. Finally, to understand the effects of GTCs, we evaluated their thermal stability upon air frying and oven baking separately. ## 2.1. Chemicals and Reagents Premium Green Tea (Imperial Choice, Shenzhen, China), white cake mix (Betty Crocker TM, Minneapolis, MN, USA), sunflower oil (Yu Pin King®, Hong Kong, China), and brown eggs (Yu Pin King®, Hong Kong, China) were purchased from a local supermarket in Hong Kong. CAT, EGCG, ECG, EGC, and GCG were obtained from Cool Chemical Technology Co., Ltd. (Beijing, China). EC, CG, GC, CAF, GA, Tris-HCl, and formic acid were purchased from Sigma-Aldrich (Saint Louis, MO, USA). Tween-20, sodium dodecyl sulfate (SDS), and 2-mercaptoethanol were obtained from Bio-Rad Laboratories (Hercules, CA, USA). Methanol and hydrochloric acid in AR grade were purchased from RCI Labscan Limited (Bangkok, Thailand). Acetonitrile in HPLC grade was purchased from Merck KGaA (Darmstadt, Germany). ## 2.2. Preparation of Green Tea Powder and Extract Dried green tea leaves (Premium Green Tea) were blended into powder form for their fortification use in cupcakes and were safely edible in our experimental section of “sensory evaluation”. After that, green tea extract (in methanol) was prepared to evaluate the effect of green tea on the physicochemical characteristics of cupcakes. Briefly, the extract was prepared by extracting 90 g of the powder twice with 900 mL of methanol for 30 min through sonication at room temperature. Another 200 mL of methanol was used for rinsing during filtration. To determine the amount of each green tea component, a few volumes of green tea extract in methanol were sampled, diluted in methanol, and filtered through a 0.45 µm nylon syringe filter prior to HPLC analysis by using the external calibration method [23]. Following that, the green tea extract was concentrated under vacuum at 35 ± 1 °C to reach 2 different concentration levels, 0.058 ± 0.001 g (Level 1) and 0.094 ± 0.001 g (Level 2) of EGCG per gram of green tea extract. The concentration of each level of green tea extract was confirmed by triplicate sampling prior to HPLC analysis. All extracted solutions were stored in an air-tight screw bottle at −20 °C and rewarmed by sonication to room temperature for 5 min before use. ## 2.3. Preparation of Cupcakes The cupcake mix was prepared according to the instructions printed on the external packaging material of the commercial cake premix (Betty CrockerTM Super MoistTM Favorites White Cake Mix) with minor modifications. In brief, a cup of water (237.00 g), a half-cup of sunflower oil (118.50 g), and 3 eggs were mixed with the premix powder (461.00 g). In each batch, 6 cupcakes (20.00 g each) were prepared. After mixing, each 20.00 g of cupcake mix was transferred into individual aluminum cups and weighed before thermal treatment. Both upper and lower heating elements of the oven (Hauswirt HO-30C, with the nominated power of 1600 watts) and the air fryer (Philips HD$\frac{9743}{11}$, with the nominated power of 1400 watts) were preheated concurrently at 180 °C for 15 min. The thermal condition was set to 180 °C for 10 min for both machines. After heating, the air-fired and oven-baked cupcakes were cooled at room temperature for 30 min. Physical measurements for color, weight, and firmness were conducted. Following that, the cupcake samples were ground and stored in a −20 °C freezer until use. For the green-tea-fortified cupcakes, two levels were prepared based on the concentration of EGCG added to the cupcakes. In each batch of 6 cupcakes, 6.00 g of Level 1 green tea extract or Level 2 green tea extract (prepared in Section 2.2) was added with 120.00 g of the cupcake mix for 2 min, respectively. The final concentration of EGCG was 0.058 g in each cupcake mix for the low level of green-tea-fortified cupcakes (LGCs) and was 0.094 g in each cupcake mix for the high level of green-tea-fortified cupcakes (HGCs). Moreover, for the analysis of EGCG-fortified cupcakes, pure EGCG was added, and its concentration was 0.094 g EGCG in each cupcake. Other procedures were the same as mentioned above in the non-green-tea-added cupcakes. ## 2.4. Sensory Evaluation of Cupcakes Ground green tea powder with the equivalent amount of EGCG as in the green tea extract was alternatively used to replace the extract in the case of any safety issues. Quantitative descriptive analysis (QDA) was used to evaluate the air-fried and oven-baked cupcakes with and without green tea powder fortification. Six attributes (i.e., sweetness, bitterness, oiliness, astringency, aroma, and doneness) were selected. For each attribute, 10 intensity anchors were set as the integrals between and included 0 and 10 (0 = dislike very much, 5 = intermediate, 10 = like very much). Ten panelists (6 females and 4 males, aged 20–30) were recruited and each panelist needed to evaluate 6 samples prepared by different food preparation methods, including control air-fried cupcake, control oven-baked cupcake, air-fried LGC, oven-baked LGC, air-fried HGC, and oven-baked HGC. Cupcakes were assessed sequentially from the cupcake with the least to the most astringency. Drinking water was supplied to rinse the mouth between samples. Each cupcake was assessed for the attributes of its appearance first, followed by the assessment of its attributes of taste. ## 2.5. Color Measurement of Cupcakes Three chromatic cylindrical coordinates (L, a, and b) of the ground cupcake samples were measured by a colorimeter (CR-400, Konica Minolta, Japan). For each sample, measurements were performed for 4 times and recorded as L, a, and b values, where L* reflects the brightness of the color (0 = black, 100 = white), a* indicates redness/greenness (−a* = greenness, a* = redness), and b* implies blueness/yellowness (−b* = blueness, b* = yellowness). The E-index of each cupcake sample was calculated by the following equation, and the difference of 2 E-indices (∆E) was used to compare the color distance between 2 samples [24]. E=L2+a2+b2 ## 2.6. Firmness Measurement of Cupcakes The firmness of the freshly prepared cupcakes was measured by using a TA-XT plus Texture Analyzer with the Texture Exponent software version 2.0.7.0 (Stable Micro-systems, Godalming, UK). The test speed was 1 mm/s, and the press distance was 5 mm. A trigger force of 5× g was enforced on the entire standing cupcake by a round probe with a diameter of 36 mm (P/36). ## 2.7. Weight Loss Determination of Cupcakes Weight loss of cupcakes upon thermal processing was determined by the percentage decrease in weight that a whole batch of cupcake before heating to after heating and cooling. ## 2.8. Determination of Fluorescent AGEs in Cupcakes A 100 mL volume of extraction buffer was prepared by dissolving 0.05 g of Tween-20, 1.0 g of SDS, 5.0 g of 2-mercaptoethanol, and 50 mM Tris-HCl (pH 7.4) into 100 mL of water. The pH value of the buffer was adjusted to pH 7.40 by adding hydrochloric acid ($37\%$). Following that, each 1.00 g of ground cupcake sample was mixed with 4.75 mL of the extraction buffer, vortexed for 30 s, and mechanically shaken at 150 rpm/min at room temperature overnight to extract the fluorescent AGEs. After shaking, the cupcake samples were centrifuged at 2020× g for 15 min to precipitate large particles. Then, 1 mL of the aqueous supernatant was further centrifugated at 14,000× g for 5 min, and each 200 µL of supernatant was pipetted into a black 96-well plate. Finally, the total fluorescent AGEs were measured by using a Victor X4 Multilabel Plate Reader (PerkinElmer, Waltham, MA, USA). The excitation and emission wavelengths adopted in this study were $\frac{360}{40}$ nm and $\frac{460}{40}$ nm, respectively [25]. ## 2.9. Chromatographic Analysis of Green Tea Components The active components in the green-tea-fortified cupcakes were extracted by the method developed by the Weibiao Zhou Group with some modifications and then subjected to HPLC analysis [26]. Briefly, 4.00 g of ground cupcake sample was extracted by 40 mL of methanol at room temperature for 60 min by a mechanical shaker operating at 150 rpm/min. After shaking, 5 mL of methanol extract was then centrifuged at 2020× g for 15 min. The supernatant was filtered at 0.45 µm of nylon syringe filter and diluted by methanol according to the ratio of weight loss upon heating. A reverse-phase Shimadzu HPLC system (LC-20AT) was applied, with a photodiode array detector (SPD-M20A) and a Phenomenex Prodigy C18 column (4.6 × 250 mm, 5 μm; Torrance, CA, USA). The mobile phases were $0.2\%$ formic acid in water (A) and acetonitrile (B), and the gradient elution program was as follows: 0 min, $7\%$ B; 16 min, $12\%$ B; 25 min, $22\%$ B; 33 min, $27\%$ B; hold 3 min; 39 min, $40\%$ B; back to $7\%$ B in 1 min and held for 5 min. The flow rate was 0.8 mL/min, and the injection volume was 10 µL. To analyze green tea components, an 11-mixed standard stock solution (2 mM) was prepared by dissolving 0.03763 g of GA, 0.06125 g of GC and EGC, 0.03884 g of CAF, 0.05805 g of C and EC, 0.09167 g of EGCG and GCG, and 0.08847 g of ECG and CG into 100 mL of methanol and storing in a −20 °C freezer until use. All working solutions were prepared daily by serial dilution in methanol. The concentrations of the standard working solutions were 0.5, 1, 2, 5, 10, 50, and 100 µM for each component except EGCG. The calibration curve of EGCG involved the high-point calibration up to 200 µM, and its low-point calibration was down to 1 µM. The calibration curves of each component were established corresponding to their wavelength with maximum absorption (271 nm for GA, 270 nm for GC and EGC, 272 nm for CAF, 278 nm for C and EC, 273 nm for EGCG, 274 nm for GCG, 276 nm for ECG and CG). The calibration curves were linear with correlation coefficients (R2) of at least 0.9993. To determine the most principal component in the green tea-fortified cupcakes responsible for the variations during thermal treatment, a data table of the aligned peaks was imported into partial least squares–discriminant analysis (PLS-DA) using SIMCA-P version 13.0 software package (Umetrics, Umeå, Sweden). All variables were mean-centered and scaled to Pareto variance, and the variable importance for the projection (VIP) was used to spot the chemical marker in the green tea-fortified cupcakes. ## 2.10. Statistical Analysis All descriptive statistics were computed with the GraphPad Prism 5 software package (GraphPad Software Inc., La Jolla, CA, USA) and Microsoft Excel 2016. Student’s t-test was used to assess statistically significant differences, by which a value of $p \leq 0.05$ was considered statistically significant. Data are expressed as mean ± standard deviation (SD) of at least triplicate determinations in this study. ## 3.1. The Impact of Air Frying and Oven Baking on the Characteristics of Cupcakes As shown in Figure 1A, the air fryer caused irregular bumps on the surface of the cupcakes. The volume expansion of the cupcakes during air frying creates a more porous structure with larger voids [9]; therefore, the highly porous microstructure might differentiate in appearance between the ground air-fried cupcake samples and the oven-baked samples. The lower uniformity of the air-fried cupcakes might be due to the presence of the forced hot airflow during heating because a higher volume expansion could be observed when the heat penetrated the cupcakes [10]. A sensory evaluation study of the air-fried and oven-baked cupcakes was conducted to understand consumers’ acceptability of the taste of the cupcakes prepared by the two methods. The average intensity value of each attribute was used to draw a radar chart (Figure 1B). The results showed that the oven-baked cupcakes were higher in doneness, lower in oiliness, and almost the same as air-fried cupcakes in sweetness, bitterness, and astringency (Figure 1B). Sweetness, bitterness, and astringency seemed to be the intrinsic properties of cakes, which may only rely on the ingredients. However, no significance could be observed between air frying and oven baking for all six attributes. After the sensory evaluation, a variety of physical measurements (including color, firmness, and weight loss) were performed to further characterize the food qualities of the oven-baked and air-fried cupcakes. To begin with, L, a, and b values were used to measure and describe the color change of the cupcakes in this study. As illustrated in Figure 1C–E, the oven-baked cupcakes were slightly higher in brightness ($$p \leq 0.1800$$ in L value), significantly lower in redness ($$p \leq 0.0020$$ in a value), and slightly higher in yellowness ($$p \leq 0.7463$$ in b value). The calculated E values were 51.00 ± 0.38 and 52.61 ± 0.52 for air frying and oven baking, respectively, which were significantly different (Figure 1F). Color distances were described by the ∆E value, which was 1.61 between the two cooking methods. Furthermore, the firmness of the cupcakes was determined by a texture analyzer. We found that air frying produced cupcakes that were 6.78-fold higher in firmness (Figure 1G). This indicated that the air fryer made harder cupcakes than the oven, and the panelists seemed to prefer the softer texture. In addition, the weight loss of cupcakes upon thermal treatments was observed and was determined by percentage loss of weight (Figure 1H). Our results indicated that air frying and oven baking resulted in 22.2 ± $0.2\%$ and 20.5 ± $0.3\%$ weight loss, respectively. Hence, air frying led to a significantly greater weight loss than oven baking ($$p \leq 0.0024$$). This finding implies that the heating mechanism of the air fryer should differ from that of the conventional oven, although both cookers implement hot air to transfer thermal energy. The higher weight loss was owing to the higher loss in water. This finding also reinforced that the heating with airflow in the air fryer exhibits higher efficiency in heat supply [9,10], suggesting that a shorter heating duration should be adopted in its household application compared to the heating time setting of conventional ovens. Several lines of evidence have exemplified that starch-based foods are prone to produce harmful substances, especially AGEs and acrylamide generated by the Maillard reaction during thermal treatment. For instance, the formation of fluorescent AGEs has been found in cookies upon oven baking. Continuous consumption of AGEs has been proven to induce chronic diseases, including cardiovascular diseases, senile dementia, and malignant tumors [25,27]. Therefore, declining the level of these hazardous substances in baking foods is critical to human health. Intriguingly, a lower acrylamide content in French fries was reported by using oven baking compared to air frying [28]. Dry heat such as in baking and frying is known to promote 10–100-fold dietary AGE formation [29]. Thus, in this study, we investigated the extent of AGE formation upon thermal treatment and found that air frying was significantly higher ($13.3\%$) in fluorescent AGE formation than oven baking when the heating temperature and duration settings were the same ($$p \leq 0.0021$$, Figure 1I). AGE accumulation in the human body was reported to be involved in the onset/progression/propagation of diabetic complications [27]. However, the study on the association between the daily tolerance of dietary AGE intake and the occurrence of these pathological conditions was still lacking. Future work can contribute to this field. Therefore, our results implied that oven baking might be generally healthier than air frying to prepare starch-based food products. ## 3.2. Green Tea Fortification Reduced the Characteristic Differences between Oven-Baked and Air-Fried Cupcakes Generally speaking, our sensory evaluation data tended to imply that oven baking can offer a better taste to green-tea-fortified cupcakes (Figure 2A). To be specific, oven-baked cupcakes were higher in doneness, bitterness, and astringency, while sweetness and oiliness were almost the same as air-fried cupcakes in both LGCs and HGCs. Again, no significant differences were found. On the other hand, our results also showed that the average intensities in both air-fried and oven-baked cupcakes decreased along with the increment in the concentration of green tea additives. This might be because bitterness and astringency were positively associated with the content of polyphenolics presented in the cupcakes. Meanwhile, it seemed that in LGCs, oven baking and air frying caused more diversity in bitterness (Figure 2A), which had little difference in the non-fortified cupcakes (Figure 1B). Therefore, current data were not able to give any explanation of this phenomenon, and further efforts are needed. Moreover, the addition of green tea generally reduced the oily mouthfeel. This might have contributed to the lipophilicity of polyphenols. The physical parameters of cupcakes that characterized their resultant food product quality (L, a, and b* values and firmness) were compared upon air frying and oven baking. To be specific, green tea fortification lowered the L, a, and b* values of the cupcakes along with the increment in green tea concentration, presenting a more tea-like color (Figure 2B–D). Therefore, no significant color change could be observed between oven baking and air frying, either in the LGC- or the HGC-added group. However, on the basis of the fundamental data of cupcakes (non-green-tea-added samples) in Figure 1C–E, green-tea-fortified cupcakes were lower in darkness and redness, while higher in yellowness. Our results agreed with the work by the Andrzej Półtorak Group, in which the same observations were reported in green-tea-fortified sponge-fat cakes [30]. This is probably due to the color of green tea itself. For the two thermal processing methods, less color difference was observed when a higher concentration of green tea fortification was applied (Figure 2E). Interestingly, the firmness of air-fried cupcakes was reduced by $84.7\%$ and $85.4\%$ in LGCs and HGCs (compared to the non-green-tea-added samples), respectively, whereas it remained almost unchanged in the oven-baked samples (Figure 1G and Figure 2F). This shows that the heated air circulation in the air fryer causes the larger cell diameter of porous size in cakes, while the fortification of green tea can shorten the cell diameter. Our data were supported by a previous study, which also presented a decrease in the cell diameter of porous size in the central slices of bread after the addition of green tea [31]. Remarkably, the firmness of the air-fried cupcakes was comparable to that of the oven-baked cupcakes upon natural green tea addition, suggesting that the food quality of cupcakes can be effectively enhanced by green tea fortification. In terms of hazardous substances, the content of fluorescent AGEs in the air-fried cupcakes was reduced by $43.0\%$ and $51.7\%$ in the LGC and HGC groups, respectively, while it decreased by $27.7\%$ and $34.1\%$, respectively, in the oven-baked samples (Figure 1J and Figure 2G). By referring to literature data, GTCs (especially EGCG) are known to prevent AGE formation by trapping intermediate precursors and via antioxidation [27]. Although air frying exerted a higher risk of harmful substance formation, green tea fortification exhibited its antiglycative ability and removed the differences between the two cooking methods. Collectively, we presented that green tea supplementation in cakes can offer a healthy profile and effectively reduce the inappropriate impact of air frying. ## 3.3. The Change of Green Tea Components in Cupcakes upon Air Frying and Oven Baking To further understand the action mechanism, comparative studies of the change of green tea components in LGCs and HGCs were performed upon air frying and oven baking. To begin with, the chemical profile of the green tea extract was elucidated by reverse-phase HPLC analysis, and the chromatogram of green tea extract (in methanol) was illustrated in Figure 3A. Our results showed that EGCG was the most abundant component in green tea, which agreed with previous literature data [32,33,34]. Furthermore, the detected GTCs included GC (retention time = 12.97 min), EGC (retention time = 7.98 min), CAT (retention time = 20.08 min), EC (retention time = 26.82 min), EGCG (retention time = 27.71 min), GCG (retention time = 28.96 min), and ECG (retention time = 34.45 min). CG was undetected in the green tea extract before its addition to the cupcake mix. As illustrated in the chromatograms in Figure 3A–C, EC, EGCG, and ECG extracted from the heated cupcakes were lower in amount than those in green tea extract, while the amounts of CAT, GCG, and CG were increased. A similar tendency was described in literature data that EC, EGCG, and ECG were found to be lowered, and GCG and CG were boosted when they were heated in solution form. It was estimated that epimerization of GTCs from their epi-forms to non-epi-forms occurred spontaneously under thermal conditions in the water- or moist-containing cupcake model [35]. Therefore, the air-fried and oven-baked cupcakes showed no difference regarding the chemical profiles. Our PLS-DA results indicated that inter-group variations existed among the cupcakes fortified with different concentrations of green tea extract (Figure 4A). VIP analysis found that EGCG was the predominant chemical marker in the four groups of green-tea-fortified cupcakes (Figure 4B). Therefore, EGCG was selected to perform a single-component cupcake model, and the results demonstrated that besides the conversion to GCG, EGCG was degraded into GA (Figure 3D–E). The degradation of epi-forms of GTCs into GA was also suggested by the increment of GA upon thermal treatment in Yuerong Liang Group’s study [35]. Hence, it can be concluded that simultaneous epimerization and degradation were the major reactions causing the reduction of EGCG. The levels of green tea components from the two different matrices were evaluated individually. As presented in Figure 5, in the LGC groups, EC (Figure 5F), EGCG (Figure 5G), and ECG (Figure 5I) extracted from the oven-baked cupcakes were lower in amount than those from the air-fried cupcakes, while the amount of CAT (Figure 5E), GCG (Figure 5H), and CG (Figure 5J) were higher. Although these results had no significant difference, they implied that the cupcakes in the conventional oven suffered from a higher degree of epimerization. Moreover, in the HGC groups, the oven-baked samples were significantly higher in GA (Figure 5A) but significantly lower in GC than the air-fried samples (Figure 5B), indicating that the cupcakes in the conventional oven also underwent a higher degree of degradation. We proposed the higher extent of epimerization and degradation was due to the lower weight loss during oven baking, retaining a higher volume of water or moisture to maintain the cake mix in its solution form. Collectively, our current data indicated that air frying and oven baking had little difference in retaining GTCs during thermal treatment. ## 4. Conclusions In the present study, we compared air frying with conventional oven baking from the perspectives of sensory and physicochemical properties by using a cupcake model. Results showed that the air-fried cupcakes were higher in weight loss, lower in conformity, significantly higher in firmness, slightly darker in color, and significantly higher in redness and in AGE formation than oven baking when the heating temperature and duration time were the same as the settings of the conventional oven. Our data further suggested that the addition of green tea modified the physical characteristics of the air-fried and oven-baked cupcakes significantly, and it removed the differences between the two cooking methods. Moreover, no significant change in green tea profile was found upon air frying and oven baking, except that GA was lower and GC was higher in air-fried HGCs. In addition, to the best of our knowledge, this study is the first to compare air frying with oven baking in household machines by using a natural-additive-fortified starch-based food model. We believe our work may not only draw attention to the safety of household foods, but also provide insights into the development of functional foods. 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--- title: 'Pluronic® F127 Hydrogel Containing Silver Nanoparticles in Skin Burn Regeneration: An Experimental Approach from Fundamental to Translational Research' authors: - Pedro Francisco - Mariana Neves Amaral - Afonso Neves - Tânia Ferreira-Gonçalves - Ana S. Viana - José Catarino - Pedro Faísca - Sandra Simões - João Perdigão - Adília J. Charmier - M. Manuela Gaspar - Catarina Pinto Reis journal: Gels year: 2023 pmcid: PMC10048756 doi: 10.3390/gels9030200 license: CC BY 4.0 --- # Pluronic® F127 Hydrogel Containing Silver Nanoparticles in Skin Burn Regeneration: An Experimental Approach from Fundamental to Translational Research ## Abstract Presently, skin burns are considered one of the main public health problems and lack therapeutic options. In recent years, silver nanoparticles (AgNPs) have been widely studied, playing an increasingly important role in wound healing due to their antibacterial activity. This work is focused on the production and characterization of AgNPs loaded in a Pluronic® F127 hydrogel, as well as assessing its antimicrobial and wound-healing potential. Pluronic® F127 has been extensively explored for therapeutic applications mainly due to its appealing properties. The developed AgNPs had an average size of 48.04 ± 14.87 nm (when prepared by method C) and a negative surface charge. Macroscopically, the AgNPs solution presented a translucent yellow coloration with a characteristic absorption peak at 407 nm. Microscopically, the AgNPs presented a multiform morphology with small sizes (~50 nm). Skin permeation studies revealed that no AgNPs permeated the skin after 24 h. AgNPs further demonstrated antimicrobial activity against different bacterial species predominant in burns. A chemical burn model was developed to perform preliminary in vivo assays and the results showed that the performance of the developed AgNPs loaded in hydrogel, with smaller silver dose, was comparable with a commercial silver cream using higher doses. In conclusion, hydrogel-loaded AgNPs is potentially an important resource in the treatment of skin burns due to their proven efficacy by topical administration. ## 1. Introduction Skin is responsible for a very different set of functions essential for human survival, including acting as a barrier [1,2,3,4,5]. It is composed of three main layers, the epidermis, dermis and hypodermis, differing in composition and purpose [2,3]. Upon injury, the skin undergoes wound healing, an intricate and dynamic physiological process through which the skin repairs itself, a key process to restore its normal function [6]. To heal, the wound will undergo four stages. The first stage is haemostasias, starting with vasoconstriction and clot formation, acting as a protein reservoir. Next, the wound undergoes an inflammatory process, in which vasodilation increases vascular permeability to promote chemotaxis; consequently, neutrophils and macrophages migrate to the wound site, mediating and ending a debridement process and secreting cytokines and growth factors. The third stage, known as the proliferative stage, consists in epithelization, angiogenesis, granulation tissue formation and collagen deposition, mediated by endothelial cells and fibroblasts. The fourth and last stage is remodeling, with collagen depositing in an organized manner, increasing the tensile strength of the wound. This last stage may last up to one year after wound healing has started [6,7]. In exceptional cases, where the injured tissues are unable to undergo complete regeneration, fibrous tissue will be deposited, creating scars [8]. Burns are one of the most common skin injuries and are the fourth most common type of trauma worldwide [9,10]. Regarding their severity, they can be classified and divided into three degrees, depending on the affected structures and area burned [3,11,12]. The classification of skin burns largely depends on which skin layers have been affected: in first degree burns, only the epidermis is affected; second degree burns usually extend to the dermal layer of the skin; while third degree burns implicate the hypodermis, sometimes completely burning through all the skin layers [13]. Whenever the skin is burned, its barrier function is reduced, making the body much more susceptible to infection [14]. In addition, extensive lesions that progress to adjacent tissue can lead to immunosuppression [12,14]. Moreover, burns can also lead to death by shock or septicemia caused by skin infection [15]. The treatment for burnt skin is described by many guidelines worldwide [16,17]. In general, these guidelines suggest the application of antimicrobial products, such as a silver sulfadiazine cream, followed by the application of a hydrocolloid to promote healing of the affected skin [18,19]. This therapeutic strategy, starting with an antibiotic and followed by a healing agent, is based on the premise that a non-infected burn heals faster. Furthermore, innovative hydrocolloid dressings impregnated with silver also appear to be effective, increasing the interest in these devices [18]. However, the treatment of an infected burn is more difficult, prolonged and sometimes ineffective [20]. Although healing agents may promote skin regeneration, they simultaneously create a favorable environment for bacterial proliferation, which considerably delays healing time [20,21]. Thus, it is important to first apply an antibiotic or proceed with the concomitant application of an antibiotic agent with regenerating properties for these burns. Moreover, wounds can also be treated using dressings such as silver-based dressings that can release silver ions into the wound, simultaneously presenting antibacterial potential and promoting skin regeneration [22,23]. Currently, silver in topical sulfadiazine-based form is one of the marketed silver formulations for treating burns in the western half of the globe [24]. Nanotechnology emerged in the last century and its application in medicine, referred to as nanomedicine, has emerged mainly in the last three decades [25,26]. Silver nanoparticles (AgNPs) are widely used in several fields [25,27,28,29,30,31,32], for example, in the treatment of water, textile products, biosensors and storage of food products [33]. In healthcare, AgNPs are essentially used as an antimicrobial agent, but other metallic nanoparticles also present these properties (i.e., gold nanoparticles) [22,25,27,28,29,34,35]. In addition to antibacterial properties, AgNPs also present antiviral, antifungal, anti-inflammatory, anti-angiogenic, anti-tumoral and antioxidant activity, with applications as a delivery system in imageology and in cosmetics [33,34]. The antimicrobial activity of AgNPs relies on their physicochemical characteristics, such as size, shape, distribution and concentration [35,36,37,38,39]. Due to their high surface to volume ratio, AgNPs will require lower concentration and, consequently, leading to lower toxicity when compared with conventional silver, i.e., silver sulfadiazine or silver nitrate [36,37,38,39]. However, it is required that the formulation is maintained in the burn area. With this aim, a thermoreversible hydrogel was prepared. Gels have a great importance for many applications [40]. The production of polymer-based gels started in the ‘70s, but in recent years, the interest in the physical gelation of polymers has increased [40,41]. This rise in interest is also explained due to hazard and toxic concerns related to conventional vehicles. In situ thermoreversible gels like hydrogels are alternative vehicle systems with many advantages [42]. Hydrogels are gels in which the dispersed phase is composed by water and the gelling agents are polymers [43]. They are three-dimensional (3D) hydrophilic polymeric networks able to retain large amounts of water or biological fluids and characterized by soft and rubbery consistence in analogy to living tissues. These gels are promising biomaterials due to their interesting properties such as biocompatibility, biodegradability, hydrophilicity and lack of toxicity [44]. These and other properties make hydrogels vehicles for many applications in the medical and pharmaceutical fields [44]. Pluronic® F127 is an amphiphilic block copolymer, a poly(ethylene oxide)/poly(propylene oxide)/poly(ethylene oxide) (PEO–PPO–PEO) triblock copolymers, extensively explored for therapeutic applications mainly due to its appealing properties, such as being non-toxic, bioadhesive and stable, and presenting the ability to transform into a gel at physiologic temperature [45,46,47]. At low temperatures, Pluronic® F127 is a solution but, as temperature increases, the hydrogen bonds in the hydrophilic chains of the Pluronic® F127 copolymer desolvate, favoring hydrophobic interactions between the polyoxypropylene domains, forming a stable gel [46]. Due to its thermoreversible nature, Pluronic® F127 becomes a gel upon administration, forming a physical and protective barrier [46,47]. This work aims to develop a semi-solid formulation of AgNPs, incorporated in a Pluronic® F127 hydrogel, for topical application with antimicrobial and skin regeneration properties using three different methodological approaches. Compared to a commercial formulation based on silver ions, it is expected to achieve a comparable therapeutic effect. Several techniques and methodologies were applied to evaluate the skin permeation, antibacterial activity, in vitro and in vivo efficiency and safety properties of this new formulation. ## 2.1. Physicochemical Characterization AgNPs were prepared following three different preparation methods, using the same reagents but differing in the temperature of the reagents used. The three methods (A, B and C) resulted in the successful preparation of AgNPs, but especially the described method C. As presented in Figure 1, the macroscopic appearance of the AgNPs prepared by this method resulted in a yellow translucid dispersion, in contrast to the AgNPs produced by methods A and B. Spectrophotometry analysis of AgNPs prepared by method C showed a characteristic absorption peak at 407 nm. Table 1 presents results regarding mean size, polydispersity index (PdI) and zeta potential of AgNPs prepared using the different methods (A, B and C). Regarding particle size, the AgNPs prepared following method C presented a similar average size compared to AgNPs prepared following method A. However, the size dispersion of the AgNPs prepared by method C was lower than that of A and B, exhibiting lower PdI values, representing a more monodisperse formulation. Regarding its surface charge, determined by measuring the zeta potential, all of the prepared AgNPs have negative values of surface charge, close to neutrality (−10 to 10 mV), regardless of the preparation method employed, suggesting a potential biocompatibility. The morphology of the different AgNPs was assessed by AFM (Figure 2). Macroscopically, AgNPs suspension prepared by methods A, B or C were visually different, as previously mentioned, and the observed color changes are closely related to the presence of AgNPs’ aggregates. By analyzing the images obtained by AFM (Figure 2), it is possible to conclude that the AgNPs prepared by method A, presenting a greyish tone, contained clusters of much greater dimensions than that expected for free AgNPs, this being in accordance with data obtained by DLS (Table 1). The AFM images (Figure 2) show the presence of these aggregates for the AgNPs synthetized by methods A and B, corroborating the large PdI observed by DLS. In general, AgNPs present a non-spherical shape and sizes lower than 50 nm. Especially for AgNPs prepared by method A, large aggregates of AgNPs were consistently present, with dimensions between 100-200 nm. Taking into account these results, method C was selected for further tests as it yielded more homogeneous AgNPs, with a PdI below 0.2, when compared with the AgNPs prepared following methods A and B. With method C selected, different recovery processes were also assessed to select the most suitable: centrifugation, lyophilization or solvent evaporation. The macroscopic results of recovering AgNPs using the mentioned processes are shown in Figure 3. AgNPs centrifugation led to the formation of a very dense pellet, impossible to resuspend, leading to the rejection of this recovery method. The same was observed for lyophilization, as the AgNPs were very difficult to resuspend in water. As for the vacuum rotary evaporator recovery method, it was much faster than lyophilization, and the AgNPs were easy to resuspend. To assess if recovering AgNPs by solvent evaporation using a vacuum rotary evaporator influenced AgNPs morphology, AFM images of AgNPs following solvent evaporation were obtained, for the non-diluted and diluted AgNPs (Figure 4). A very dense sample with considerable aggregates was observed in the non-diluted AgNPs recovered by a vacuum rotary evaporator. However, when the sample was diluted, it was very similar to the sample prepared by method C (Figure 4). This fact suggests that temperature has greater influence during the AgNPs formation (i.e., reagents temperature), but does not seem to have influence after AgNPs formation (i.e., AgNPs recovery). Thus, solvent evaporation using a vacuum rotary evaporator was the selected recovery method. ## 2.2. Antimicrobial Preliminary Efficacy Assessment The bacterial strains used in this study were selected according to their tendency to colonize and infect burnt skin (i.e., Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa) [48]. Table 2 displays the main results. Minimum inhibitory concentrations (MICs) were determined by broth microdilution for the three bacteria under study and for the different samples. The colloidal dispersion of AgNPs under the conditions of synthesis showed very little or no inhibition for most of the strains. In turn, when concentrated, whether by lyophilization, centrifugation or by evaporation, the bacterial inhibition was more expressive. Though there is inhibitory activity, MIC values are beyond the range of concentrations tested. However, AgNPs concentrated by lyophilization presented a more accentuated inhibitory activity against P. aeruginosa, and AgNPs concentrated by evaporation presented a more pronounced inhibitory activity against E. coli and P. aeruginosa, thus presenting higher efficacy against these strains. ## 2.3. In Vitro Permeation Studies An in vitro skin permeation study was conducted for 24 h using an artificial membrane that mimics the skin. After 24 h, the amount of AgNPs that permeated the membrane was below the limit of detection of the analytical method used. This non-permeation of the AgNPs through the deep layers of the skin can be considered a good safety indicator. ## 2.4. Physical Characterization of Pluronic® F127 Hydrogel In order for the AgNPs to remain in the burned region of the skin, a thermoreversible Pluronic® F127 hydrogel was prepared and characterized regarding its viscosity and textural properties. Pluronics or Poloxamers are non-toxic FDA-approved poly(ethylene oxide)/poly(propylene oxide)/poly(ethylene oxide) (PEO-PPO-PEO) triblock copolymers. A variety of Pluronics is available on the market, differing for the molecular weight of the building blocks and the ratio between hydrophobic and hydrophilic units. Pictures of the prepared gel were taken at two different temperatures, in order to macroscopically characterize the gel. The obtained images are shown in Figure 5. At 4 °C (storage temperature), the hydrogel is in a liquid form, as can be seen in Figure 5A, and at 37 °C, physiologic temperature and the temperature at which the hydrogel will be applied, Pluronic® F127 is in its gelled form, as shown in Figure 5B. Viscosity is also a very important parameter for any semi-solid formulation. The viscosity was determined at different temperatures and results are shown in Table 3. Looking at the results, the thermoreversible properties of the Pluronic® F127 hydrogel are apparent, as at 4 °C, the gel presented a viscosity of 75.7 ± 0.5 mPas, in its liquid form, and at 37 °C, a notorious higher viscosity, of 7333.3 ± 23.1 mPas. The textural properties of the gel were also evaluated in triplicates, and the maximum peak force of displacement (Fmax), also denoted hardness, obtained was 0.6 ± 0.01 N. Viscosity and textural properties were also evaluated after the incorporation of the lyophilized AgNPs powder in the Pluronic® F127 hydrogel, and its properties remain stable when compared to the hydrogel without nanoparticles. ## 2.5. In Vivo Efficacy and Safety Assessments Skin burns started to appear on the second day of SDS application, and all animals completed the assay. Moreover, the animals did not present signs of stress or pain during the duration of the preliminary assessments. The body weight of the animals was recorded for all groups, and the results are shown in Figure 6. The body weight of the animals decreased for all the groups following the burn induction but recovered after the beginning of treatment. Moreover, the body weight of all groups followed the same trend except for the negative control (Pluronic® F127 hydrogel), in which body weight presented a smaller decrease following the skin burn when compared to the other groups. Furthermore, when comparing the body weights of the different groups on the last day of the assay, the group treated with AgNPs incorporated in Pluronic® F127 hydrogel presented the best results, as the animals in this group recovered $96\%$ of their initial body weight. Representative images of mice from each group are shown in Figure 7, Figure 8 and Figure 9 and were taken daily after the beginning of the treatment. By evaluating the photographic records, it is possible to note that the AgNPs incorporated in Pluronic® F127 hydrogel (Figure 7) caused rapid skin regeneration in the test group, with the skin practically healthy at the end of the treatment schedule and without noticeable scarring. In contrast, the negative control (Figure 8), Pluronic® F127 hydrogel, led to a pronounced scar. Regarding the positive control (Figure 9), the commercial topical formulation of silver sulfadiazine led to a complete regeneration of the skin at the same time as the test group, without leaving any noticeable scars on the skin. However, the concentration of silver in the positive control (15.3 μmol of silver per cm2) was not equivalent, i.e., AgNPs were administered at a very low dose (3.3 pmol of silver per cm2). Skin thickness was also evaluated and tended to increase with the progression of the injury due to inflammation and subsequent skin regeneration with the formation of crusts. This increase was quite consistent across the different groups (Figure 10). An ideal result would be the achievement of a similar skin thickness the burn and at the end of the protocol. This did not happen for any of the groups under study. However, results showed a higher tendency of improvement in skin thickness for animals treated with AgNPs incorporated in Pluronic® F127 hydrogel. ## 2.6. Histopathological Analysis The burns were histologically analyzed and representative images are presented in Figure 11. Both skin of mice treated with silver sulfadiazine and AgNPs incorporated in Pluronic® F127 presented epidermal closure with full epidermal differentiation, but skin of mice treated with the positive control presented increased thickness and marked hyperkeratosis. Moreover, the skin of mice in the positive control group presented scanty granulation tissue and mixed orientation of the collagen fibers, while granulation tissue was absent in the test group, and the collagen was present in a horizontal pattern. Moreover, a previously described skin regeneration scoring system was used to compare the burns of all animals that participated in this preliminary study (Table 4). The chosen scoring system takes different aspects into account such as the presence of an ulcer or if the wound is completely closed, the degree of epidermis differentiation, the amount of granulation tissue present, and lastly, the collagen fibber orientation and pattern. A score of 16 indicates full regeneration of the skin lesion while lower scores refer to the presence of histologic changes compatible with skin injury. The highest score was of animals treated with AgNPs incorporated in Pluronic® F127 hydrogel (test group, score of 14.60 ± 3.13). It is to be noted that the lowest score was seen in the animals of the positive control group, treated with a commercial formulation of silver sulfadiazine. This animal model was previously validated by our group, and the same scoring system was used to score the untreated burns of this animal model, obtaining a score of 12.0 ± 2.8 [9]. Thus, the skin of mice treated with AgNPs incorporated in Pluronic® F127 presented an advanced wound healing. In the set of seven mice, only two of them did not have a maximum score indicating why the skin was not fully regenerated. One of these animals was allocated to the group treated with the commercial formulation of silver sulfadiazine. The skin was ulcerated and showed no differentiation at the level of the epithelium. The granulation tissue and the inflammatory infiltrate had moderate levels. Collagen appeared in a vertical orientation with a reticular pattern. The total score for this mouse in the group treated with the commercial formulation of silver sulfadiazine was 6. The other mice that did not reach the maximum score were part of the group treated with AgNPs incorporated in Pluronic® F127 hydrogel. In this case, contrary to the situation described above, the lesion was already closed and the collagen was already in a more approximate to normal disposition. However, both inflammatory infiltrate and granular tissue are present, indicating that skin regeneration would not be completed. Thus, the final score for this animal was 9. Although the skin of most animals, independently of treatment or control groups, had completely recovered, as corroborated by the scoring system, the macroscopic analysis allowed for the classification of animals into two distinct categories: recovered with scarring and recovered without scarring. As seen in Figure 8, the animal representative of the negative control group had regenerated skin but presented a pronounced scar, unlike what happened with the animals of the other groups (Figure 11). The spleen was also subjected to analysis to check for possible toxicity, and all animals in the study showed no changes in this organ. ## 3. Discussion Different methods have been described to prepare AgNPs, and these can be of three types: physical (i.e., evaporation–condensation, laser ablation), chemical (i.e., reduction, electrochemical and photochemical methods) or biological (i.e., based on oxidation-reduction reactions mediated by microorganisms such as bacteria, fungi or plant extracts) [49,50,51,52]. Amongst these methods, the most commonly used is chemical reduction, as it has a low cost of production, high performance and is fairly simple [49,52]. As described above, this method uses a solution of NaBH4 to reduce Ag+ to Ag0, forming a cluster and originating colloidal AgNPs [49,50,52]. To obtain a monodisperse AgNPs, all nuclei must be formed at the same time, to consequently have the same growth, and this is dependent on pH and temperature [53]. In this work, it is worth noting that preparation methods A, B and C differed in the temperature of the reagents, and it seems that the temperature of the reagents used in the preparation of the AgNPs influences their size, dispersion, morphology and the presence of aggregates. On the other hand, AgNPs suspension produced by method C showed a yellow color that previous studies claim to be indicative of the formation of AgNPs without aggregates. In a previous study, AgNPs produced at 50 °C presented a brown color after synthesis. When analyzed by AFM, the produced AgNPs were large (~50 nm), non-spherical and presented several aggregates. When the same preparation method was used but using cooled reagents, at a temperature of 10 °C, the reaction speed was slower and the initial yellow color, seen right after synthesis, only shifted after a few hours [53]. The influence of the particle size on the antimicrobial activity of AgNPs is not consensual [33,54,55,56]. Although different bacterial species differ in the size ranges of AgNPs that inhibit their bacterial growth, most bacterial growth is inhibited by smaller AgNPs (<50 nm) [57,58]. Martínez-Castañón et al. compared the MIC’s of AgNPs with different sizes (7, 29 and 89 nm), demonstrating that smaller AgNPs (7 nm) presented higher antibacterial activity against E. coli and S. aureus [59]. In another study, Jeong et al. also compared the antimicrobial activity of AgNPs with 10 and 100 nm. AgNPs with the size of 10 nm showed comparable antibacterial activity against Methylobacterium spp. with the positive control (methanol) [60]. Skin penetration rates and depth-of-penetration play significant roles in determining the therapeutic potential of topical agents and their systemic toxicity. Theoretically, the smaller the size of the particles, the higher the rate of penetration. In our case, AgNPs produced by method C presented greater homogeneity (PdI of 0.180). Although NPs are mostly preferred for their large surface area, smallness should not be a core goal, as the physicochemical properties of NPs can be efficiently utilized in topical antimicrobial formulations. A study carried out with gold NPs showed that smaller particles with 15 nm reached the deepest layers of the mouse skin while NPs with sizes of 102 and 198 nm only reached the epidermis and the dermis [61]. Another study with polymeric NPs demonstrated that particles with a diameter of approximately 300 nm did not permeate the human skin during the 6 h after their application without mechanical stress (passive permeation) [62]. This was shown by Ezealisiji et al., who tested the skin penetration of AgNPs with 22, 58, 76 and 378 nm [63]. Thus, considering the size of the herein developed AgNPs formulation following method C (ca. 50 nm), our AgNPs should only reach the most external layers of the skin, aiming at the goal of this study and potentially decreasing the probability of the systemic absorption. Silver itself is non-toxic to humans within the reference dose, i.e., oral reference dose (RfD) = 5× 10−3 mg/kg-day [64]. Overconsumption of silver, however, may lead to argyria, which results in permanent blue-grayish pigmentation of the skin, eyes and mucous membranes. Systemic toxicity can be caused by rapid accumulation of NPs at capillary/lymphatic junctions in the dermal layer, membrane pores/ligand-mediated endocytosis and physically breached leaky endothelium. Rapidly penetrating NPs could circumvent macrophage-mediated immunological responses and can enter the blood circulatory system. In contrast, slow penetration offers better efficacy of NPs against the infected cells and provides adequate time for the body’s immune system to detoxify NPs through phagocytosis. In this work, the skin permeation study did not detect any measurable value of silver, which is a good indicator regarding putative systemic toxic effects. Besides the particle size, surface charge, determined by zeta potential, is a measure of stability of colloidal dispersion, for which higher values in the module indicate greater physical stability of the dispersion under analysis and less tendency to form aggregates. It is described how negatively charged AgNPs diffuse through the skin at a greater speed [65]. In turn, particles that have a positive zeta potential (above 10 mV) are more likely to bind cells and be recognized by the immune cells [29]. Therefore, particles developed in this work presented a surface charge close to neutrality (between −10 and 10 mV) and would be ideal due to presenting low or no skin penetration and increased biocompatibility. Martínez-Higuera et al. developed negatively charged AgNPs, incorporated in a Carbopol® hydrogel with Mimosa tenuiflora extracts, demonstrating the wound healing potential of AgNPs [66]. The condition of the skin (i.e., whether intact or damaged) is another factor that influences the ability of AgNPs to penetrate the skin. A study conducted by Larese et al. showed that AgNPs with a mean size of 25 nm had an increase in skin penetration when the skin was damaged (2.32 ng/cm2) when compared to intact skin (0.46 ng/cm2), in vitro [67,68]. When the AgNPs are able to penetrate the skin, several works have shown that these particles usually precipitate at the stratum corneum, preventing AgNPs from precipitating into deeper layers of the skin [68,69,70]. Contrary to these studies, an in vivo study conducted by George et al. on normal intact skin showed that AgNPs penetrate into deeper layers of the skin, the reticular dermis, and thus, AgNPs are not retained in the stratum corneum. Regardless, none of these studies reported the presence of AgNPs in systemic circulation [68]. In the present study, it was also observed that the therapeutic effect of the resultant AgNPs prepared by method C varied for different tested bacteria, probably due to the disparity in the way AgNPs interact with different bacterial strains [48,71,72,73]. When compared to the commercialized formulation, the developed AgNPs outperformed the commercial silver sulfadiazine regarding in vitro antimicrobial activity for the tested strains, using lower concentrations of silver. The exact mechanism of action is not completely understood. After contacting with the skin, one of the main concerns with AgNPs is the possible depletion of mitochondrial function with the production of ROS. Results from an in vitro study carried out on 3D-fibroblast cultures demonstrated that the reduction in mitochondrial activity only occurred temporarily and did not affect their viability [74]. In addition, an in vivo study of a biopsy of AgNPs-treated skin of a single patient showed a large amount of AgNPs without showing signs of apoptosis or necrosis, which corroborates the absence of toxicity [74]. From the results of these studies, it can be concluded that the toxicity conferred by silver is not as accentuated as initially thought. Regarding the possible systemic toxicity of AgNPs, the skin permeation study did not show any silver over time. Semi-solid formulations have been indicated for better consumer acceptance of the treatment and allow for good skin-spread ability of the formulation [75]. In particular, hydrogels leave a semi-transparent layer over the burn, allowing a burn protection from the external environment and accelerate the wound healing processes [43]. Thus, in order to increase the contact time of AgNPs with the burn site and promote the wound healing properties of the AgNPs, the lyophilized AgNPs were incorporated in a semi-solid formulation, a Pluronic® F127 hydrogel. AgNPs synthesized by other groups have been incorporated into hydrogels based on other polymers, e.g., Masood et al. impregnated a chitosan-PEG hydrogel with AgNPs [76], Nguyen et al. loaded AgNPs into chitosan/Polyvinyl Alcohol hydrogel [77], Ahsan et al. used a PVA hydrogel for AgNPs-hydrogel patches [78], Xie et al. reinforced chitosan hydrogels with AgNPs [79] and Badhwar et al. loaded quercetin hydrogels with AgNPs [80], with wound-healing applications. In fact, Pluronic® F127 presents unique features, such as being thermoreversible, even at low concentrations, being liquid at temperatures lower than the physiological temperature, at which it becomes a gel [47,81,82,83]. This has led Pluronic® F127 to be vastly researched for dermal and transdermal applications [46,83]. The wound-healing process was treatment dependent. The test group, treated with AgNPs incorporated in Pluronic® F127 hydrogel, had a skin regeneration score below 16. Although this indicates that the wound of the animals in the test group is yet to completely heal, it was still higher than the skin regeneration score of the animals in the positive control group, treated with a commercialized silver sulfadiazine cream. As the AgNPs incorporated in Pluronic® F127 hydrogel presented a much lower concentration of silver in comparison to the commercialized silver sulfadiazine cream, this result is very promising, as the AgNPs were more effective in treating the skin burns in this chemically induced burn in vivo mice model than the positive control. Zhang et al. analyzed the delayed treatment of burns with AgNPs and besides results being also very promising, the wound healing was slower and a higher concentration of silver was used [84]. Comparing our in vivo results with the work developed by Stojkovska and colleagues, this was also the case, in which burns were treated with formulations containing alginate and AgNPs but, again, with a higher concentration of silver [85]. Posteriorly, biochemical analysis, to quantify inflammatory and pro-inflammatory factors in the animals’ serum, and identification of the bacterial species colonizing the scarred burn were also performed. Biochemical analysis demonstrated that the animals in the test group did not present an inflammatory response, as the values for IL-6 and TNF-α were 0 pg/mL and <4 pg/mL, respectively. Moreover, the bacterial strains identified at the scarred burn site were common between the different experimental group and were consistent with environment and/or fecal contaminations and/or colonization (i.e., S. aureus, Enterococcus faecalis, Staphylococcus xylosus and Micrococcus luteus), as these strains are commonly found in the environment, feces and/or commensal bacteria of the skin, indication that the burns did not become infected. We hope that our proof-of-concept study could facilitate a new paradigm for understanding NPs while developing an ideal antimicrobial topical formulation. ## 4. Conclusions The current study explores the application of silver nanoparticles (AgNPs) as a topical treatment of skin burns, one of the most common types of skin injury in the world, with antibacterial and wound-healing properties. In addition, this work explores the use of a thermoreversible hydrogel to deliver those AgNPs. Skin therapy using hydrogel drug delivery systems has been gaining attention because of its dual functionality to simultaneously supply moisture and loaded actives onto infected sites on the skin. AgNPs were successfully prepared by a chemical reduction method with sodium borohydride at a specific temperature. Different methods were assessed, differing in reagents’ temperature only. According to the results obtained in the various assessments, it was observed that temperature differences when producing AgNPs have a significant impact on its physicochemical characteristics. Moreover, the method that yielded AgNPs with more desirable physicochemical characteristics used sodium borohydride at a lower temperature, after being stored at −18 °C for 10 min, allowing the reaction to occur at a slower speed. Of the different recovery processes assessed, solvent elimination by evaporation resulted in AgNPs with efficient bacterial growth inhibition that were easily dispersed in water and maintained AgNPs properties. In vivo studies using a chemical burn model showed that AgNPs incorporated in Pluronic® F127 hydrogel, at a lower concentration of silver, performed similarly to the positive control, a commercialized formulation of silver sulfadiazine with higher silver concentration, in terms of skin thickness and wound healing. The water-rich structure of the hydrogel and wound-healing properties of AgNPs seem to have the required dual-characteristics for the treatment of skin burns, since they have high efficacy when topically used, since it requires smaller concentrations of silver for the treatment of burns compared to formulations found in the market and skin regeneration was effective using new safe and hydrogel-based materials. ## 5.1.1. Reagents Silver nitrate (AgNO3, Cat. No. 209139), Pluronic® F127 (Cat. No. P2443) and sodium borohydride (NaBH4, Cat. No. 71320) were purchased from Sigma Aldrich (Steinheim, Germany). Milli-Q water was obtained in filtration equipment from Millipore Corporation (Burlington, MA, USA). A commercial silver-based formulation in which each gram of cream contains 1 mg of micronized silver sulfadiazine was purchased. Reaction buffer for NZY TaqII DNA polymerase (Cat. No. MB354), NZYTaq II DNA polymerase (Cat. No. MB355) 5 U/μL, magnesium chloride 50 mM, a standard solution, NZYDNA Ladder VI (Cat. No. MB089), agarose powder of routine grade, GreenSafe Premium dye (Cat. No. MB13201) and NZYGelpure (Cat. No. MB011) kit were purchased from Nzytech (Lisbon, Portugal). All other reagents were of analytical grade. ## 5.1.2. Microbial Strains The in vitro antimicrobial study was carried out using Gram-positive bacteria (Staphylococcus aureus, ATCC 29213) and Gram-negative bacteria (Escherichia coli, ATCC 25922 and ATCC 8739, and Pseudomonas aeruginosa, ATCC 27853). ## 5.1.3. Animals Eight-week-old female CD-1 mice (25–40 g), obtained from Charles River (Barcelona, Spain) were housed in polypropylene cages in a 12–12 h light-dark cycle with a constant temperature environment of 20–24 °C, relative humidity of 55 ± $5\%$ and received standard diet and water ad libitum. All animal experiments were conducted according to the recommendations of the Animal Welfare Board (ORBEA) of the Faculty of Pharmacy, Universidade de Lisboa, approved by the competent national authority Direção-Geral de Alimentação e Veterinária (DGAV) for project with reference PTDC/BBBBMD/$\frac{0611}{2012}$, DGAV/2013, and per the EU Directive ($\frac{2010}{63}$/EU), the Portuguese laws (DL $\frac{113}{2013}$, $\frac{2880}{2015}$, $\frac{260}{2016}$ and $\frac{1}{2019}$), and all relevant legislation. ## 5.2.1. Preparation of AgNPs A solution of AgNO3 (1 mM) was added dropwise to a NaBH4 (2 mM) solution under constant stirring. The optimization of the synthesis protocol was achieved by varying the temperature of the solutions used for preparation (Table 5) and thus, different batches of AgNPs were prepared (method A, B and C). The AgNPs suspension was protected from light with aluminum foil, at 4 °C. Synthesis of AgNPs was confirmed by spectrophotometry (UV-Vis Spectrophotometer, Hitachi U-2000 Dual-Beam UV-Vis, Oxford, United Kingdom) in which an absorption peak around 400 nm should be present. Recovery of the AgNPs was performed by three different methods. The first one consists of centrifuging the prepared AgNPs (Sigma 3-30KS, Sigma Zentrifugen, Osterode am Harz, Germany) at 60,000× g for 20 min, followed by 40 min at 40,000× g at 4 °C; the solvent elimination by rotary vacuum evaporator method was adapted from a previous study in which a vacuum rotary evaporator (Butchi RE 111, Butchi, Switzerland) in a hot bath (70 °C, Butchi 461 Water Bath, Butchi, Switzerland) was used; lastly, lyophilization was carried out by leaving AgNPs in a freeze dryer (Modulyo, Edwards, CO, USA) at 102 mbar for 24 h. ## 5.2.2. AgNPs Characterization AgNPs diluted in Milli-Q water (1:10, pH 7) were analyzed in terms of size and polydispersity index (PdI) by Dynamic Laser Scattering (DLS) (Nano Z Zetasizer, Malvern Instruments, Malvern, United Kingdom). The AgNPs’ surface charge was measured through Laser Doppler Anemometry (Nano Z Zetasizer, Malvern Instruments, Malvern, United Kingdom). For this measurement, samples were diluted in NaCl 0.1 M solution (1:10, pH 7). The morphology of the nanoparticles was assessed by atomic force microscopy (AFM). Briefly, 40 µL of the sample was placed on a freshly cleaved mica surface. The mica was left to dry overnight and analysis was performed the following day. Images were acquired by Multimode 8 HR coupled to Nanoscope V (Bruker, Billerica, MA, USA) using Peak Force Tapping and ScanAssist AFM mode and silicon nitride ScanAsyst-Air probes (spring constant of ca.0.4 N/m, Bruker). Finally, AgNPs were quantified using spectrophotometry. A calibration curve was prepared, and the following equation was obtained (R2 = 0.998):[1]Abs=0.198C+9.209×10−3 where Abs is absorbance, and C is AgNPs concentration. ## 5.2.3. Antimicrobial Preliminary Efficacy Studies All strains used were kept stored at −80 °C and were previously grown overnight at 37 °C in Muller–Hinton Agar plates. All antimicrobial assays were carried out by broth microdilution in Muller–Hinton broth in non-treated 96-well plates containing two-fold dilutions of the compound/formulation tested. The inoculum was prepared by suspending overnight bacterial growth in sterile distilled water as to obtain a bacterial suspension adjusted to a 0.5 McFarland standard, followed by 1:100 dilution in Muller Hinton broth. An equal volume to that present in each well was used to inoculate the plates to obtain a final inoculum concentration of ca. 5.0 × 105 colony-forming units (CFU)/mL. Compound-free and non-inoculated wells were included in each plate as positive and negative controls, respectively. The plates were incubated overnight at 37 °C and the Minimum Inhibitory Concentration (MIC) determined as the lowest concentration that inhibited visual growth by each strain. ## 5.2.4. In Vitro Skin Permeation Studies Permeation studies were conducted with Franz cells using a silicon membrane, in a water bath at 32 °C for 24 h. The donor chamber was filled with 300 μL of AgNPs (2 mg/mL) using Tween®80 ($0.04\%$, v/v) as a dispersing agent, and the receptor chamber was filled with PBS at pH 7.4 (USP39) under constant stirring (100 rpm). Samples were collected every hour for the first eight hours, as well as all of the cell content at the end of the study being finished. The concentration of AgNPs was accessed by spectrophotometry, following the above-described method. ## 5.2.5. Preparation and Physical Characterization of Pluronic® F127 Hydrogel For in vivo assessment of AgNPs and to deliver these nanoparticles to the target (burn) area, a thermoreversible hydrogel was prepared. Twenty-eight grams of Pluronic® F127 were added to 100 mL of phosphate buffer solution (PBS pH 7.4, USP32) in a beaker, according to a previous study [86]. The solubilization was then carried out using a magnetic plate (100 rpm, 2 h). The prepared Pluronic® F127 hydrogel was stored at 4 °C. Viscosity was determined using 100 rpm with needle n.º 3, in triplicate ($$n = 3$$), at different temperatures (4, 25 and 37 °C) using a Brookfield® Rotational Viscometer (Middleborough, MA, USA). Texture analysis (firmness and adhesiveness) was performed using the Stable Micro Systems TA-XT2i Texturometer (Godalming, United Kingdom). A test probe P/25P (25 mm/s) was used, with a test speed of 3 mm/s, distance 5 mm, load cell with 5 k and Trigger Force of 0.049. ## 5.2.6. In Vivo Efficacy and Safety Assays: Proof of Concept Animals were randomly allocated into three experimental groups: a group dosed with the vehicle of the test formulation ($$n = 3$$); a group dosed with a commercial cream of silver sulfadiazine (10 mg/g of cream) ($$n = 3$$); a test group dosed with AgNPs (66 nM) dispersed in Pluronic® F127 hydrogel ($$n = 5$$). Each application of commercial cream of silver sulfadiazine corresponds to 15.3 μmol of silver per cm2 and each application of AgNPs dispersed in Pluronic® F127 hydrogel corresponds to 3.3 pmol of silver per cm2. Before experimentation began, animals were lightly anaesthetized with isoflurane and an area of 2 cm2 of the back of each mouse was shaved with a commercial depilatory cream to expose the skin. Then, a chemical burn was induced by topical application of 100 μL Carbopol 940® gel containing $40\%$ of SDS for two consecutive days. All formulations were administered topically (100 µL) with a syringe and performed daily, during 5 days of protocol, after light sedation with isoflurane. After administration, the water provided to the animals contained codeine (30 mg/500 mL) to reduce any pain and ensure welfare. During the 8-day experiment (since time zero), the body weight, skin thickness (FisherbrandTM TraceableTM Carbon Fiber Calipers 6”, FisherScientific, Hampton, NH, USA) and welfare of all animal groups were monitored. The burns were photographed each day to record burn evolution. A sterile swab was used to collect the skin flora of animals in each group for bacterial strain identification. After 8 days, the animals were sacrificed and approximately 1 cm2 of the burn area was harvested from each mouse, along with the spleen, and stored in formalin for histological analysis. Biochemical analysis was performed on the serum of all animals in each group to quantify IL-6 and TNF-α. ## 5.2.7. Histology Specimens of skin and spleen were excised and fixed in $10\%$ buffered formalin for a minimum period of 48 h and were routinely processed, embedded and sectioned into 3 μm thick sections, and stained with H&E. Slides were analyzed with a CX31 microscope (Olympus Corporation, Tokyo, Japan), and images were acquired with the NanoZoomer-SQ Digital slide scanner C13140-01 (Hamamatsu Photonics, Shizuoka, Japan). A scoring system for wound healing was developed by adaptation of previously published scoring systems [9,87]. Briefly, skins were scored for epidermal closure (0—ulcerated skin, 1—closed wound); epidermal differentiation (0—absent, 1—spinous epidermal, 2—granular layer); amount of granulation tissue (1—profound, 2—moderate, 3—scanty, 4—absent); inflammatory infiltrate (1—plenty, 2—moderate, 3—few); collagen fiber orientation (1—vertical, 2—mixed, 3—horizontal) and pattern of collagen (1—reticular, 2—mixed, 3—fascicle). ## 5.2.8. 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--- title: 1-Methylcyclopropene and UV-C Treatment Effect on Storage Quality and Antioxidant Activity of ‘Xiaobai’ Apricot Fruit authors: - Yunhao Lv - Anzhen Fu - Xinxin Song - Yufei Wang - Guogang Chen - Ying Jiang journal: Foods year: 2023 pmcid: PMC10048762 doi: 10.3390/foods12061296 license: CC BY 4.0 --- # 1-Methylcyclopropene and UV-C Treatment Effect on Storage Quality and Antioxidant Activity of ‘Xiaobai’ Apricot Fruit ## Abstract The ‘Xiaobai’ apricot fruit is rich in nutrients and is harvested in summer, but the high temperature limits its storage period. To promote commercial quality and extend shelf life, we investigated the effectiveness of Ultraviolet C (UV-C) combined with 1-methylcyclopropene (1-MCP) treatment on ‘Xiaobai’ apricot fruit stored at 4 ± 0.5 °C for 35 days. The results revealed that the combination treatment of 1-MCP and UV-C performed better than either UV-C or 1-MCP alone in fruit quality preservation. The combination treatment could delay the increase in weight loss, ethylene production, and respiration rate; retain the level of soluble solid content, firmness, titratable acid, and ascorbic acid content; promote the total phenolics and flavonoids accumulation; improve antioxidant enzyme activity and relative gene expression, and DPPH scavenging ability; and reduce MDA, H2O2, O2.− production. The combined treatment improved the quality of apricot fruit by delaying ripening and increasing antioxidant capacity. Therefore, combining UV-C and 1-MCP treatment may be an effective way to improve the post-harvest quality and extend the storage period of the ‘Xiaobai’ apricot fruit, which may provide insights into the preservation of ‘Xiaobai’ apricot fruit. ## 1. Introduction The ‘Xiaobai’ apricot is a characteristic of agricultural products in Xinjiang, China. It has a high nutritional value and a pleasant taste and is enjoyed by the locals [1]. However, because of the mature season in summer and its origin in southern Xinjiang, the high ambient temperature makes long-term storage and long-distance transportation difficult [2]. Therefore, it is critical to determine how to extend the storage period of the ‘Xiaobai’ apricot while maintaining its commercial quality. So far, several apricot fruit preservation methods have been proposed, including coating preservation, salicylic acid treatment, calcium treatment, and near-freezing temperature storage [3,4,5,6]. Ultraviolet-C (UV-C) irradiation (200–280 nm), a non-thermal disinfection method used in fruits and vegetables, can cause DNA injury in microorganisms by altering pyrimidine dimer formation, which can prevent agricultural product decay [7]. Moreover, UV-C is an abiotic stress that could damage the biological membrane and stimulate the generation of reactive oxygen species (ROS). It can also stimulate the antioxidant system and promote secondary metabolite synthesis [8,9]. In fruits and vegetables, phenolic and flavonoid compounds are important secondary metabolites with antioxidant properties. It was previously reported that UV-C treatment improved the accumulation of phenolic and flavonoid compounds in strawberries, sweet cherries, and blueberries [10,11,12]. In addition to antioxidant compounds, excess ROS would stimulate the expression of enzymatic plant antioxidant systems such as catalase (CAT), superoxide dismutase (SOD), peroxidase (POD), and ascorbate peroxidase (APX) [13]. Rivera–Pastrana et al. [ 14] revealed that UV-C irradiation increased the activity of SOD, POD, and CAT in papaya fruit. In contrast, Sripong et al. [ 15] reported that UV-C irradiation promoted ROS production while also increasing the activity of POD in mangosteen. However, several studies demonstrated that UV-C irradiation increased the respiration rate and ethylene production in white asparagus, zucchini, and tomato [16,17,18]. Ethylene production would accelerate fruit ripening and senescence, which are detrimental to long-term fruit storage. The 1-methylcyclopropene (1-MCP) may inhibit ethylene response by binding specifically to ethylene receptors, delaying fruit and vegetable maturation and senescence [19,20]. Previous studies revealed that 1-MCP treatment could effectively inhibit the ethylene effect, delaying ripening and senescence in pears and pomegranates [21,22,23]. In addition, 1-MCP was found to be effective in retaining antioxidant enzyme activity and alleviating ROS damage in apples, bitter melon, and nectarine [24,25,26]. Therefore, the UV-C and 1-MCP treatments effectively improved the storage quality and extended duration of post-harvest fruit and vegetables. To date, no studies have been conducted to investigate the effects of UV-C irradiation combined with 1-MCP treatment on apricot fruit storage. The present study aimed to explore the efficiency of UV-C irradiation combined with 1-MCP treatment on apricot post-harvest storage and preservation and to provide a theoretical basis for extending the storage period and elevating the quality of post-harvest apricot fruit. ## 2.1. Plant Material and Experimental Design The ‘Xiaobai’ apricot with low maturity (soluble solid content (SSC: 11 ± $0.5\%$, firmness: 23.5 ± 0.5 N) was collected on June 2020 from a plantation in Bugur County, Xinjiang, China. On the day of harvest, the apricot fruit was transported to the laboratory of Shihezi University. After a 24 h precooling period, fruit with uniform size, no mechanical damage or diseases, and a similar maturity were selected for experiments. The selected apricot fruit was then randomly distributed into four groups (each group had 25 kg apricot fruit) as follows: [1] control fruit with no treatment; [2] 1-MCP treatment, the sample apricot fruit was fumigated in a 1 m3 airtight container for 20 h at 20 °C, with a 1-MCP concentration of 1 μL L−1; [3] UV-C treatment, the sample apricot fruit was placed on a clean bench (length: 1.3 m, width: 0.66 m, height: 0.52 m). The fruit was exposed to a UV-C lamp (30 W/G30T8, Philips) for 5 min before being rotated 180° for another 5 min to achieve a total irradiation dose of 1.25 kJ m−2. A UV radiation meter (LS126C, Linshangtech, China) was used to measure the UV dose. [ 4] Combined treatment, fruits were treated with 1-MCP as the method of 1-MCP treatment for 20 h, then treated with UV-C irradiation as the method of UV-C treatment. Following treatment, all four fruit groups were stored in polystyrene foam boxes at 4 ± 0.5 °C and 80 ± $5\%$ relative humidity for 35 days. At each sampling time point (day 0, 7, 14, 21, 28, and 35) during storage, 4 kg fruit from each treatment group was taken for triplicate analysis. ## 2.2. Determination of Ethylene Production and Respiration Rate Gas chromatography (GC-16A, Shimadzu, Kyoto, Japan) was used to measure the ethylene production of ‘Xiaobai’ apricot fruit. A total of 500 g of apricot fruit was placed in a 1 L closed chamber for 1 h, and an injection needle absorbed 0.1 mL of gas. The following were the GC conditions: The column and detector temperatures were 50 °C and 150 °C, respectively. The nitrogen (N2) flow rate was 18 mL min−1. The ethylene production rate is expressed in ng kg−1 s−1 as the amount of ethylene produced by 1 kg fruit per unit time. A sample of 1 kg of fruit was randomly selected and placed in a 1 L sealed tank attached to a carbon dioxide tester (CES-10, Zhonggu, Shanghai, China) for 30 min at 4 °C. Respiration rate was expressed by CO2 production rate in ng kg−1 s−1. ## 2.3. Firmness, Weight Loss, Soluble Solid Content and Titratable Acid Twenty ‘Xiaobai’ apricot fruits were randomly selected, peeled, and placed on the durometer platform. The durometer was vertically inserted into the scale line by pressing the operating lever uniformly. The firmness was expressed as the average reading value in N. The weight loss was measured using the weighing method, with 100 fruits selected randomly in each treatment group and weighed every seven days. The differential weight method was used to calculate the weight loss. The calculation formula is as follows:weight loss (%)=(initial weight−final weight initial weight)×$100\%$ Samples of 10 g of fruit were placed in gauze and squeezed to determine soluble solid content (SSC), and the juice was dropped into a digital refractometer (A1701161, ATAGO, Tokyo, Japan). Each group was repeated ten times, and the results were expressed as the average reading value in percentage (%). Acid-base titration was used to determine titratable acid (TA). A total of 10 g of fruit was homogenized in a mortar with deionized water, transferred to a flask, and extracted for 30 min. Then, 10 mL supernate was filtrated into a triangular flask for acid-base titration with 2 mol L−1 NaOH solution. TA was calculated with the malic acid degree, and the result was expressed in percentage (%). ## 2.4. Ascorbic Acid, Total Phenolics, and Flavonoid Content The detection of ascorbic acid (ASA) content was based on the work of Xu et al. [ 27]. The samples of 10 g of fruit were triturated, extracted with oxalic acid, and then titrated with 2,6-dichloroindophenol until the pink color appeared. The result was expressed in mg kg−1. Total phenolics (TP) content and flavonoids were detected by the method described by Li et al. [ 28]. Briefly, 2 g fruit samples were ground and homogenized in $1\%$ HCl-methanol solution in an ice bath, then extracted at 4 °C for 20 min. TP was measured using the optical density (OD) at 760 nm. Gallic acid was used as a standard curve to express the TP content, which was expressed as g kg−1. To measure total flavonoids, 1 mL of supernate was transferred to a test tube, and 1 mL $5\%$ (w/v) NaNO2 and 0.25 mL $10\%$ (w/v) AlCl3 were added to the tube, respectively. After 5 min, 1 mL 1 mol L−1 NaOH was added to the mixture. As flavonoids, the OD was measured at 510 nm. To express the flavonoid content, a standard curve was made with different concentrations of rutin. ## 2.5. Peroxidase, Ascorbate Peroxidase, Superoxide Dismutase and Catalase Activities Peroxidase (POD) activity was measured by the method described in a study by Sheng et al. [ 29]. Five grams of apricots were homogenized in 5 mL of 50 mM pH 7.8 acetate buffer (containing 1 mM PEG, $4\%$ PVPP, and $1\%$ Triton X-100) and centrifuged at 8000× g for 20 min at 4 °C. The 0.5 mL supernatant was collected and added with 3.0 mL 25 mmol L−1 guaiacol and 200 μL 0.5 mol L−1 H2O2. The change of OD within 2 min was measured at 470 nm, and expressed with the unit of U kg−1. Ascorbate peroxidase (APX) activity was detected by a method described by Xu et al. [ 27]. Five-gram samples were homogenized in 5 mL of 50 mM pH 7.5 potassium phosphate buffer (containing 0.1 mmol L−1 EDTA, 1 mmol L−1 ascorbic acid, and $2\%$ PVPP) and centrifuged at 8000× g for 20 min at 4 °C. The 0.5 mL supernatant was collected and mixed with 0.3 mL 2 mmol L−1 H2O2. The change in OD within 2 min was measured at 290 nm. Superoxide dismutase (SOD) activity was measured by the procedure explained by Xu et al. [ 30]. Five-gram samples were homogenized in 5 mL of 50 mM pH 7.8 sodium phosphate buffer (containing 5 mmol L−1 DTT and $5\%$ PVP) and centrifuged at 8000× g for 20 min at 4 °C. The 0.5 mL supernatant was collected and mixed with 100 μM EDTA-Na2 (0.15 mL), 750 μM nitro-blue-tetrazolium (NBT) (0.15 mL), 130 mM methionine (0.15 mL), and 20 μM riboflavin (0.15 mL). The change in OD within 2 min was measured at 560 nm. Catalase (CAT) activity detection was based on the method described by Li et al. [ 28]. The extraction method is identical to that of SOD. The enzymatic reaction system included 2.9 mL of 20 mmol L−1 H2O2 and 100 µL of enzyme extraction solution. The change in OD within 2 min was measured at 240 nm. ## 2.6. Malondialdehyde, Hydrogen Peroxide Content, and Superoxide Radicals Generation Rate Malondialdehyde (MDA) content was measured through a method explained by Fan et al. [ 3]. Five-gram apricot samples were triturated in 15 mL of $5\%$ (w/v) trichloroacetic acid (TCA) and centrifuged at 8000× g for 20 min at 4 °C. A total of 1 mL supernate was extracted and mixed with 3 mL $0.5\%$ (w/v) thiobarbituric acid (TBA) containing $10\%$ TCA and incubated in a 100 °C water bath for 20 min, and then centrifuged at 8000× g for 10 min at 4 ˚C. The OD of the supernate was measured at 450, 532, and 600 nm, respectively, and the following equation calculated MDA content:MDA content=[6.45×(OD532−OD600)−0.56×OD450]×VVs×m×1000 where V is volume of the extracted sample, Vs is volume of the determination solution, and m is the mass of the sample. The unit of the result was mmol kg−1. The hydrogen peroxide (H2O2) content was determined according to the method described by Xu et al. [ 30] with slight modification. Two-gram samples were homogenized with 5 mL acetone ($100\%$) and centrifuged at 5000× g for 20 min at 4 °C. A 1 mL supernatant sample was extracted and mixed with 0.1 mL titanium tetrachloride-HCl and 0.2 mL ammonium hydroxide, followed by centrifugation at 8000× g for 10 min at 4 ˚C. The H2O2 content was expressed by measuring OD at 412 nm in mol kg−1. The method of Li et al. [ 28] described was referenced to measure the superoxide radicals (O2.−) generation rate. Five-gram apricot samples were ground with 5 mL of 50 mM pH 7.8 phosphate buffer and centrifuged at 5000× g for 20 min at 4 °C. The supernate was incubated for 1 h at 25 °C with 1 mL 1 mM hydroxylamine hydrochloride, then mixed with 1 mL 17 mM 4-aminobenzene sulfonic acid and 1 mL 7 mM α-naphthylamine. The mixture was blended and incubated for 25 min at 25 °C for the chromogenic reaction. The OD was measured at 530 nm. The result was described as U kg−1. ## 2.7. 2,2-Diphenyl-1-Picrylhydrazyl Radical Scavenging Activity Assay The detection of 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging rate was based on the method described by Zhang et al. [ 31]. After homogenizing 1 g of apricot fruit with $80\%$ methanol, the sample was centrifuged. Supernate (0.4 mL) was mixed with 2.4 mL DPPH solution in a tube and kept in the dark at 25 °C for 30 min. The OD was measured at 517 nm. The sample extraction was replaced with $80\%$ methanol in the blank control. The DPPH radical scavenging rate was calculated by the following equation:DPPH radical scavenging activity=(1−AsAb)×$100\%$ As and Ab are the absorbance of the sample and blank, respectively. ## 2.8. Reverse Transcription–Quantitative PCR (RT-qPCR) The RNA extraction kit (CW2598S, CWbio, Taizhou, China) extracted RNA from 0.1 g of frozen powdered apricot sample. The total RNA content was determined by UV-spectrophotometer (NANO 2000, Thermo, Waltham, MA, USA). Then, a cDNA synthesis kit (D7160L, Beyotime Biotechnology, Haimen, China) was used to synthesize cDNA from 2 μg of total RNA. Primer 5.0 (Premier Biosoft, San Francisco, CA, USA) was used to design the primer sequences for q-PCR and listed in Table 1. For normalization, the BoActin protein gene was used as a reference gene. RT-qPCR reactions were carried out in a fluorescent quantitation PCR amplifier (Exicycler 96, Bioneer, Daejeon, Republic of Korea), and the relative gene expression was determined using the 2−ΔΔCt method. ## 2.9. Statistical Analysis The statistical analysis of the obtained data was calculated by IBM SPSS Statistics 20 software. Duncan’s multiple range test was used to calculate the significant differences. The results of two samples were regarded as a significant difference when p-value less than 0.05. Results of each sample are expressed as the mean ± standard deviation (SD). All experimental data were plotted using Origin 2019. ## 3.1. Respiration Rate and Ethylene Production The respiration rate in the four groups of apricot fruit increased and then decreased during storage (Figure 1a). On day 21, the respiration rate in untreated and UV-C-treated fruit peaked, but the respiration rate in UV-C-treated fruit was higher than the control. During the storage period, the respiration rate in the 1-MCP treatment group was always lower than the control, with the peak occurring on day 28. Except for the first seven days of storage, the respiration rate in the combined treatment group was lower than the control but higher than the 1-MCP treatment group, and the peak was also delayed on day 28. The ethylene production trend in post-harvest apricots followed the same pattern as the respiration rate (Figure 1b). On day 21, ethylene production in UV-C-treated fruit was higher than in untreated fruit. Furthermore, during storage, ethylene production in the combined treatment group was higher than in the 1-MCP treatment group but lower than in the UV-C and control groups. ## 3.2. Firmness and Weight Loss The firmness of untreated apricot fruit decreased continuously, reaching a minimum (8.31 N) on day 35 (Figure 1c). Compared to untreated samples, applying UV-C or 1-MCP maintained the firmness of apricot fruit, though 1-MCP maintained greater firmness than UV-C-treated fruit. Furthermore, the firmness of the combined treatment (12.39 N) was higher than that of the single treatment and the control, which were 1.06, 1.18, and 1.49 times compared to 1-MCP, UV-C treated, and untreated samples, respectively. The weight loss of untreated fruit increased steadily during storage, reaching $19.70\%$ on day 35 (Figure 1d). The three treatments could restrain the weight loss in apricot at the end of storage, and the weight loss in 1-MCP, UV-C, and combined treatment were $15.17\%$, $17.64\%$, and $13.06\%$, respectively, which were lower than that in the control. Furthermore, in the first seven days, the weight loss in the UV-C and combined treatments was higher than in control. ## 3.3. SSC and TA SSC in untreated apricot fruit increased from day 0 to 21, then decreased until the end of storage (Figure 1e). SSC in UV-C-treated fruit followed the same trend as in untreated samples but was higher. Before day 21, the SSC in the 1-MCP and combined treatments were lower than in the untreated and UV-C samples but increased on day 28 and was higher than in the UV-C-treated and untreated fruits. The TA in the four groups of apricot fruit decreased during the storage (Figure 1f), but the TA in the control group decreased more quickly than the other three treatment groups. The decrease rate of TA in the UV-C treatment group was the fastest among the three treatment groups, followed by the 1-MCP treatment group, and the slowest in the combined treatment group. After the storage period, the TA in the UV-C, 1-MCP, and the combined treatment groups were 1.07, 1.22, and 1.34 times that of the control, respectively. ## 3.4. ASA, TP, and Flavonoid Content The ASA content in the control and treatment groups decreased continuously with the increase in storage time (Figure 2a). The control group decreased rapidly, reaching 16.58 mg kg−1 on day 35. On day 35, the ASA content in 1-MPC- and UV-C-treated fruit was 21.02 and 23.41 mg kg−1, respectively, delaying the decrease in ASA content during the storage. Similarly, the combined treatment group had an ASA content of 26.07 mg kg−1 on day 35, which was higher than the two single treatment groups, and 1.57 times that of the control. The TP content in untreated fruit increased with storage time, peaking on day 21 (1.79 g kg−1), and then decreased (Figure 2b). TP content in 1-MCP-treated fruit was higher than untreated fruit on days 21 and 35, implying that 1-MCP treatment could delay the TP content decrease during the late storage period. On days 7 to 21, the UV-C and the combined treatments could induce the accumulation of total phenolic content. The TP in the UV-C treatment and the combined treatment groups was 1.21 and 1.28 times that of the control group at day 21, respectively. Furthermore, the decrease in TP in combined treated fruit was slighter than in UV-C-treated fruit, and the highest content was retained at the end of storage. The flavonoid content change was similar to TP content in each fruit group (Figure 2c). On day 21, the flavonoid content in untreated fruit peaked at 19.86 mg kg−1, which was $4.47\%$, $11.10\%$, and $14.36\%$ lower than 1-MCP, UV-C, and the combined treated fruit, respectively. ## 3.5. POD, CAT, SOD, and APX Activities and Corresponding Relative Gene Expression Figure 3a depicts that the POD activity in the control group increased continuously, reached the maximum on day 14, and then decreased. POD activity in three treated samples followed a similar pattern, but the peak was delayed until day 21. Furthermore, POD enzyme activity in the UV-C treatment and combined treatment groups increased from day 7 to 21, reaching 1.18 and 1.23 times that of the control on day 21. The 1-MCP treatment group had no apparent effect on improving POD enzyme activity from day 0 to 7, but did improve POD activity from day 14 to 35. Figure 3b indicates that the relative expression of pmPOD was higher in the UV-C and combined treatment groups than in the control group during storage. As shown in Figure 3c, CAT activity in the control group generally increased during the initial storage stage and reached the maximum on day 14, then gradually decreased with a temporary increase (day 28). CAT activity was higher in 1-MCP- and UV-C-treated fruit than in control during storage; moreover, CAT activity in UV-C treatment was higher than in 1-MCP treatment. On day 21, the CAT activity in the combined treatment group was $5.22\%$, $13.61\%$, and $27.52\%$ higher than in the UV-C treatment, 1-MCP treatment, and control groups. Similar to the activity of CAT, the expression of CAT in the four groups increased for the first 21 days and then decreased (Figure 3d). However, the expression of pmCAT in UV-C treatment and combined treatment was higher than the control. Figure 3e,g indicate the changes in SOD and APX enzyme activities. SOD and APX enzyme activities in untreated fruit increased continuously, reaching the maximum on day 28, and then decreased. UV-C or 1-MCP treatment, like POD and CAT, maintained a higher SOD and APX activity level in apricot fruit than in untreated fruit. Moreover, APX and SOD activity in the combined treatment group was 8.52 and 5.81 U g−1 on day 28, respectively, and improved by $25.2\%$ and $35.6\%$ compared to the untreated. The change of pmSOD (Figure 3f) and pmAPX (Figure 3h) expression during storage was more significant in the UV-C and combined treatments than in the control and 1-MCP treatments, which was similar to the activity of SOD and APX. ## 3.6. MDA, H2O2 Content, and O2.− Generation Rate During storage, the MDA content in four groups of apricot fruit increased continuously (Figure 4a). The MDA content in the untreated group reached its maximum on day 35 (1.84 nmol kg−1). The MDA content in the UV-C and combined treatment groups was higher than the control group for the first 14 days, then the rise slowed down from day 21 to 35, reaching 1.76 and 1.68 nmol kg−1, respectively, on day 35, amounts which were lower than the control. During storage, MDA content in the 1-MCP treatment group increased slowly and was always lower than in other treatment groups, reaching 1.64 nmol kg−1 on day 35. The H2O2 content in the four groups of apricot fruit increased with storage time (Figure 4b), with the untreated fruit reaching 96.97 mol kg−1 on day 35. The H2O2 content in the 1-MCP treatment group was 75.76 mol kg−1 on day 35, which was lower than the control group. However, H2O2 content in the UV-C treatment group was higher than the control on days 7 and 14, then gradually increased slowly to 89.51 mol kg-1 on day 35, which was $7.7\%$ lower than the control. The combined treatment group had higher H2O2 content than the 1-MCP treatment group for the first seven days, then the increase slowed and was the lowest compared to the other three groups on day 35 (72.12 mol kg−1). Figure 4c indicates that the O2.− generation rate in untreated fruit increased with storage time, reached a maximum on day 21 (174.65 U kg−1), and then decreased to 157.29 U kg−1 on day 35. The O2.− generation rate trend in the 1-MCP treatment group was similar to the control group but lower during storage ($p \leq 0.05$) than control. The UV-C treatment group had a higher O2.− generation rate than the control on days 7 to 14, but decreased rapidly and was lower than in the control group from day 21 to 35. The combined treatment group demonstrated the same pattern as the UV-C group but was higher than the control group on day 7 and lower than the two single treatment groups from day 21 to 35. ## 3.7. DPPH Scavenging Rate The DPPH scavenging rate in untreated fruit increased with storage time, peaked on day 21, and began to fall (Figure 4d). During storage, the DPPH scavenging rate in all three treatment groups followed the same pattern and was higher than the control. When the peak values in each group on day 21 were compared, the DPPH scavenging rate in the 1-MCP, UV-C, and combined treatment groups was $13\%$, $26.7\%$, and $30.1\%$ higher than the control, respectively. The findings revealed that the three treatments could efficiently improve the DPPH scavenging rate of ‘Xiaobai’ apricot fruit during storage, with the UV-C treatment and combined treatment having a more effective increase in DPPH clearance rate. ## 3.8. Correlation Analysis Figure 5 depicts the correlation analysis result between each detected index during storage. The results indicated that TA and firmness were negatively correlated with ethylene production. In contrast, weight loss, SSC, and MDA were positively associated with ethylene production. Weight loss and ROS (H2O2 and O2.−) levels were positively correlated with respiration rate. Moreover, antioxidant enzyme activity and secondary metabolites (TP and flavonoid) content were positively associated with ROS, whereas ASA content was negatively correlated with ROS. Furthermore, antioxidant ability (DPPH scavenging rate) was positively linked to antioxidant enzyme activity and secondary metabolite content. ## 4. Discussion As a typical climacteric fruit, the ‘Xiaobai’ apricot fruit had a clear respiration rate and ethylene production peaks, two important nodes for the climacteric fruit [32]. Fruit and vegetables had the best edible quality in a short period following the appearance of respiration peak, whereas ethylene production can accelerate fruit ripening and senescence. Delaying the respiratory peak and reducing ethylene production is important for extending the fruit storage period [33]. The present study indicated that the respiration rate and ethylene release of UV-C-treated fruit increased sharply at the start of storage, which may be related to the physiological damage caused by UV-C irradiation promoting plant self-healing and energy supply–demand [34]. However, 1-MCP treatment effectively inhibited fruit respiration rate and ethylene production, which were linked to the specific combination of 1-MCP and ethylene receptors in fruit [21]. Concurrently, this effect is also reflected in the combined treatment. Xu and Liu [35] reported that blueberry fruit treated with 1-MCP and UV-C had lower levels of respiration and ethylene production. Tiecher et al. [ 9] indicated that ethylene release in tomato fruit treated with the combination of 1-MCP and UV-C treatment was lower than the UV-C treatment alone, which was consistent with our findings. Therefore, the combined treatment has the potential to effectively reduce the increase in respiration rate and ethylene release caused by UV-C irradiation. SSC and TA are important for determining the edible and flavor quality of harvested fruit. Moreover, the ratio of SSC to TA is frequently used to indicate ripeness in fruit. Apricot ripening, like other fruits, is typically accompanied by the accumulation of sugar and the degradation of organic acid [36]. In addition, the firmness of the apricot fruit reflected its maturity. Fruit softening during post-harvest ripening would result from the degradation of cell wall components such as pectin and cellulose [37,38]. In the present study, it was found that UV-C irradiation accelerated the accumulation of SSC in fruit, which could be attributed to the promotion of fruit ripening by ethylene production [39]. During the post-ripening of apricot fruit, 1-MCP could effectively restrain the increase in SSC and inhibit the decomposition of TA. Previous studies have revealed that 1-MCP treatment can effectively prolong the post-harvest ripening process of the French prune while hindering the increase in SSC [40]. Similarly, the rise of SSC and decreased TA was slower in combined treated apricot fruit, while the TA was higher in single-treatment groups. Therefore, when compared to a single UV-C irradiation treatment, the combined treatment could more effectively delay the apricot fruit post-harvest ripening. Phenols and flavonoids are important secondary metabolites that contribute significantly to improving the antioxidant capacity of fruit [41]. The present study indicated that the combined treatment could effectively limit the decrease in ASA content and accelerate the production of phenols and flavonoids. In two single treatments, 1-MCP suppressed the reduction in total phenolic and flavonoid contents but did not affect production. In contrast, UV-C promoted the accumulation of total phenolics and flavonoids, implying that UV-C irradiation can accelerate the secondary metabolic rate of fruits during storage [42,43]. Wang et al. [ 12] described that UV-C irradiation could accelerate the accumulation of phenolics and flavonoids in blueberries. Therefore, it is necessary to recognize that UV-C treatment was more important than 1-MCP treatment in promoting total phenolics and flavonoid content in combined treatment. ASA is an important nutrient in apricot fruit and an antioxidant-active substance [44]. The present study revealed that all three treatments could effectively delay the decrease in ASA content, with the combined treatment having the best effect, possibly due to the fact that the secondary metabolism and antioxidant capacity of fruit was enhanced by UV-C. Ávila–Sosa et al. [ 45] reported that pre-treatment of hawthorn with UV-C irradiation could maintain the ASA content during storage. In contrast, Xiong et al. [ 40] indicated that ASA content in prune would be restrained by being treated with 1-MCP. Therefore, combining 1-MCP with UV-C treatment could be an efficient way to limit ASA degradation and promote the accumulation of total phenols and flavonoids in the ‘Xiaobai’ apricot fruit. Apricot fruit is susceptible to ripen progress during postharvest storage by the increasing of ROS production, which is an important reason of causing membrane damage, and the commercial quality of fruit would be reduced [12]. H2O2 and O2.− are the most abundant ROS in fruits and vegetables, while MDA is the product of membrane lipid peroxidation that can reflect the membrane lipid oxidation and loss of permeability. The present study revealed that UV-C irradiation stimulated the accumulation of H2O2 and O2.− in the early stages of storage and increased the degree of membrane lipid peroxidation in both UV-C and combined treatments. This could be due to UV-C treatment improving the respiratory metabolism of apricot fruit, which is one of the important sources of ROS accumulation [46]. It was previously reported that mitochondria produced a large amount of ROS when plants were exposed to UV-C irradiation [47]. Furthermore, Vandenabeele et al. [ 48] demonstrated that excessive ROS would damage cells, whereas a low concentration of ROS could act as a chemical signal. For example, ROS positively affects the accumulation of phenolics as a signaling role in fruits and vegetables [13,49]. Whether ROS will act as damaging or signaling molecule depends on the delicate equilibrium between ROS production and scavenging. In low concentrations, ROS act as signaling molecules that mediate several plant responses in plant cells, including responses under stresses [50]. For instance, ROS are produced in plants in response to drought stress, which would trigger oxidative stress and induce the ROS scavenging system that may confer protection or tolerance against stress that is emerging [51]. Therefore, maintaining the homeostasis of ROS in cells is very important for delaying the senescence of fruits after harvest. SOD can catalyze the O2.− to H2O2, and CAT, POD, and APX can convert H2O2 to H2O [52]. Our findings demonstrated that UV-C treatment could effectively stimulate the activity of POD, CAT, SOD, and APX, as well as the expression of relative genes in apricot fruit at the initial stage of storage, and that the effect was superior to 1-MCP treatment, which could be attributed to the toxic excitatory effect of UV-C [53]. Limited accumulation of ROS due to UVC leads to the occurrence of oxidative stress in fruits, which stimulates the expression of antioxidant-related genes. Maurer et al. [ 13] revealed that UV-C treatment could increase the accumulation of ROS, which could stimulate the enzymatic antioxidant system in ‘Isabel’ grapes. Studies in strawberries suggested that UV-C irradiation could boost the activity of CAT, POD, and SOD [28]. Moreover, Xu et al. [ 27] reported that 1-MCP treatment efficiently maintained SOD and APX activity in kiwi fruit. The combined treatment appeared to significantly improve antioxidant enzyme activity, possibly due to the superimposed effect of 1-MCP and UV-C. Meanwhile, the occurrence of oxidative stress reaction gradually increased the antioxidant capacity of UV-C treated to fruit, resulting in the inhibition of MDA and H2O2 accumulation. Moreover, in the combined treatment group, ROS production was further inhibited by 1-MCP and UV-C, the accumulation of H2O2 and O2.− was the lowest at the end of storage, and DPPH scavenging ability was stronger than the single treatment groups. Previous studies indicated that UV-C could boost the antioxidant capacity and inhibit ROS accumulation in mandarin [44]. Furthermore, Huan et al. [ 54] demonstrated that 1-MCP treatment inhibited the accumulation of H2O2 in peaches, and Ma et al. [ 55] identified that 1-MCP treatment improved the DPPH scavenging ability in ‘Jonagold’ apple. Thus, combining UV-C with 1-MCP treatment effectively improves antioxidant ability and reduces ROS damage in ‘Xiaobai’ apricot fruit. In conclusion, 1-MCP treatment could inhibit the ethylene responses, reducing respiratory metabolism and restraining ROS accumulation, thereby delaying apricot fruit ripening and senescence. UV-C treatment, as abiotic stress, can potentially damage cells (particularly membrane and DNA) while promoting respiration metabolism. High levels of respiration metabolism provide the energy for cell repair and accelerate ROS accumulation. ROS can signal the antioxidant system, improving the activity of antioxidant enzymes and the content of secondary metabolites. The improved antioxidant ability could scavenge ROS and reduce oxidative damage, thereby delaying apricot fruit senescence (Figure 6). The combined treatment has the advantages of two treatments that can inhibit endogenous ethylene ripening and improve antioxidant capacity in ‘Xiaobai’ apricot fruit. ## 5. Conclusions The results of our study reveal that application of UV-C irradiation combined with 1-MCP treatment was an effective measure to delay ripening and improve postharvest quality of ‘Xiaobai’ apricot fruit. The combined treatment could effectively delay the increasing of respiration rate, ethylene production, weight loss, and SSC; inhibit the decline of firmness, TA, and ASA; promote the accumulation of TP and flavonoids; improve the activity and expression of antioxidant related enzymes (POD, SOD, CAT, and APX) and DPPH scavenging ability; and reduce the production of MDA, H2O2, and O2.−. In conclusion, UV-C combined with 1-MCP treatment could be a viable strategy to prolong the storage period and improve the edible quality in postharvest apricot fruit. Moreover, the future research would focus on exploring the effect of UV-C irradiation combined with other preservation technologies, such as near-freezing temperature storage or coating preservation, in improving the quality of fruit and vegetables. ## References 1. Cui K., Zhao H., Sun L., Yang L., Cao J., Jiang W.. Impact of near freezing temperature storage on postharvest quality and antioxidant capacity of two apricot (. J. 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--- title: Epigenome-Wide Changes in the Cell Layers of the Vein Wall When Exposing the Venous Endothelium to Oscillatory Shear Stress authors: - Mariya A. Smetanina - Valeria A. Korolenya - Alexander E. Kel - Ksenia S. Sevostyanova - Konstantin A. Gavrilov - Andrey I. Shevela - Maxim L. Filipenko journal: Epigenomes year: 2023 pmcid: PMC10048778 doi: 10.3390/epigenomes7010008 license: CC BY 4.0 --- # Epigenome-Wide Changes in the Cell Layers of the Vein Wall When Exposing the Venous Endothelium to Oscillatory Shear Stress ## Abstract Epigenomic changes in the venous cells exerted by oscillatory shear stress towards the endothelium may result in consolidation of gene expression alterations upon vein wall remodeling during varicose transformation. We aimed to reveal such epigenome-wide methylation changes. Primary culture cells were obtained from non-varicose vein segments left after surgery of 3 patients by growing the cells in selective media after magnetic immunosorting. Endothelial cells were either exposed to oscillatory shear stress or left at the static condition. Then, other cell types were treated with preconditioned media from the adjacent layer’s cells. DNA isolated from the harvested cells was subjected to epigenome-wide study using Illumina microarrays followed by data analysis with GenomeStudio (Illumina), Excel (Microsoft), and Genome Enhancer (geneXplain) software packages. Differential (hypo-/hyper-) methylation was revealed for each cell layer’s DNA. The most targetable master regulators controlling the activity of certain transcription factors regulating the genes near the differentially methylated sites appeared to be the following: [1] HGS, PDGFB, and AR for endothelial cells; [2] HGS, CDH2, SPRY2, SMAD2, ZFYVE9, and P2RY1 for smooth muscle cells; and [3] WWOX, F8, IGF2R, NFKB1, RELA, SOCS1, and FXN for fibroblasts. Some of the identified master regulators may serve as promising druggable targets for treating varicose veins in the future. ## 1. Introduction Varicose vein (VV) disease pathogenesis is of a multifactorial chronic nature; genetic, as well as epigenetic, factors make a considerable contribution to it and may serve as predisposing and affecting factors. Altered (non-uniform) shear stress, a tangential hemodynamic force, accompanies the pathological condition of the vessels and is one of the characteristics of many vascular disorders [1]. The mechanical effect of changes in hemodynamics may result in a cascade of molecular reactions leading to a shift in physiological processes and prompting the development of the disease. Not all factors underlying the initiation and the progression of VV disease have been discovered so far. The three main layers constituting the vein wall are the following. The inner layer, the tunica intima, consists of a monolayer of endothelial cells with underlying collagen and elastin. The middle layer, the tunica media, predominantly consists of several layers of circularly located smooth muscle cells separated by collagen and elastin fibers. The outer layer, the tunica adventitia or externa, consists of collagen and, mainly, fibroblasts [2]. These layers are tightly connected to each other such that all changes happening to one layer are reflected in another layer. Moreover, these changes promptly spread through the whole vein wall, affecting its condition. Indeed, one of the shear-stress responsive genes, LKLF (KLF2), is endothelium-specific, and its expression affects tunica media formation and vessel wall integrity/stabilization [3]. Endothelial cells line the inner surface of the vein wall and, since they come in contact with both circulating blood components and surrounding tissues, they serve as primary sensors of the systemic, as well as the local, stimuli that may modulate the essential endothelial functions. For instance, due to blood flow, such modulation by local stimuli—hemodynamic forces—can lead to short-term vasoactive responses and long-term remodeling of the vessel wall [4]. Endothelial cells possess a set of mechanosensors, such as receptor tyrosine kinases, ion channels, integrins, and G-protein-coupled receptors that convert changes in the hemodynamics into the biochemical signals modulating endothelial cells’ gene expression, morphology, behavior, and phenotype through specialized mechanosensitive signaling pathways [1,5]. The endothelium plays a key role in vascular hemostasis, coagulation, inflammation, regulation of angiogenesis and vascular tone, and vascular permeability. Therefore, by sensing biochemical and biomechanical signals, as well as modifying its functional phenotype, the inner layer of the venous wall contributes to the maintenance of vascular homeostasis and the development of vascular pathology [4]. It is widely accepted that local hemodynamics play a crucial role in risk prediction [6]. Endothelial cells possess phenotypic and functional heterogeneity depending on the type of blood vessels, and, therefore, demonstrate vascular-bed-specific properties that define their response to fluid shear stress exposure (laminar, oscillatory, or pulsatile) [1]. There are different in vitro 2D and 3D (two-dimensional and three-dimensional) models mimicking the hemodynamics of the vascular system, but these cannot take into account all aspects of the complex in vivo environment, since various factors, including vessel geometry, blood viscosity and velocity, and blood pressures, affect hemodynamics in the vasculature. Nevertheless, these models are quite useful because they may help to investigate cellular responses and interactions under different patterns of fluid shear stress [6]. Of particular interest for us is the endothelial cells’ response to oscillatory shear stress (which is most effective for mimicking varicose vein conditions) and the transmission of the molecular signals to the adjacent layers of the vein wall. Thereby, the affected layers may respond to such molecular signals with changes within themselves and, subsequently, outside of themselves—into their environment. This is the case of the middle layer, which is responsible for constriction of the vessel wall and venous tone maintenance. Studies have demonstrated that upon exposure to an altered hemodynamic environment, the vascular endothelium becomes activated (including ROS (reactive oxygen species) generation/production) and acquires a proinflammatory phenotype. This is caused by mechanoactivation of the NF-KB signaling pathway and characterized by the expression of endothelial inflammatory markers, augmented endothelial cell turnover, and increased endothelial cell apoptosis/loss [1,4,6]. Hemodynamic forces such as shear stress control the transcriptional activity of a large and diverse set of genes (noteworthily, not necessarily endothelium-specific genes) expressed by the vascular endothelium that plays a key role in transducing biomechanical forces into biochemical signaling [6,7]. We hypothesized that the oscillatory flow present in incompetent veins and primarily affecting endothelial cells leads to epigenomic changes in these cells and cells of other types constituting adjacent layers of the venous wall. The influence of epigenetic factors contributes to the multifactorial nature of varicose vein disease [8]. Such factors change the structure of DNA without changing the DNA sequence itself. Thus, DNA methylation can affect the transcription and, consequently, gene expression. Epigenomic modifications, i.e., epigenetic changes across the whole genome, are unique in terms of the type and amount of chemical modification at each location on a chromosome, and can vary from cell to cell; moreover, they are very dependent on environmental factors, so they can be modulated externally with small molecules [9]. This gives us hope that there could be awesome possibilities for the treatment of vascular diseases in the future. “OMIC” approaches allow the profiling of multiple genes/their modifications and products simultaneously [10]. Recording “OMIC” data to measure gene activities, protein expression, and metabolic events is becoming a standard approach to characterize the pathological state of an affected tissue. Increasingly, several of these methods are applied in a combined approach, leading to large multi-“OMIC” data sets. Still, the challenge remains how to reveal the underlying molecular mechanisms that render a given pathological state different from the norm. The disease-causing mechanism can be described by a re-wiring of the cellular regulatory network, for instance as a result of epigenetic alterations influencing the activity of relevant genes. Reconstruction of the disease-specific regulatory networks can help to identify potential master regulators of the respective pathological processes [11,12,13,14]. Knowledge about these master regulators can point to ways to block a pathological regulatory cascade. Suppression of certain molecular targets as components of these cascades may stop the pathological process and cure the disease. In case of the epigenome, and methylome in particular, epigenome-wide association studies (EWAS) significantly accelerated the field of epigenetics research. The aim of this study was to reveal epigenome-wide methylation changes in the cells-representatives of the venous wall layers, exerted by oscillatory shear stress towards the endothelium, which may result in the consolidation of gene expression alterations upon vein wall remodeling during varicose transformation. It is essential to investigate how hemodynamics is involved in VV disease initiation/progression, since it may render to the identification of potential drug targets in the molecular network that governs the studied pathological process. Yet, future mechanistic studies on the pathogenesis of the disease should provide new insights into potential targets for VVs treatment. ## 2.1. Identification of Target Genes In the first step of the analysis, target genes were identified from the uploaded experimental data. There were 300 genes (among all mapped to differentially methylated sites), with the highest number of transcription factor binding sites found in the regions ±400 bp from these methylated sites. Initially, raw methylation data were analyzed using Illumina GenomeStudio (Methylation Module) software, which mapped the differentially methylated sites to: (a) 36 differentially methylated genes (17 hypo- and 19 hypermethylated) for endothelial cells (ECs), (b) 92 differentially methylated genes (19 hypo- and 73 hypermethylated) for smooth muscle cells (SMCs), and (c) 353 differentially methylated genes (222 hypo- and 131 hypermethylated) for fibroblasts (FBs), as listed in Supplementary Tables S1–S3. This prompts us to speculate that the ratios of hypo- to hypermethylated genes may reflect possible changes after oscillatory shear stress exposure in overall transcription processes within the corresponding cell type of the vein layer: 0.89 < 1 (which means quenching) for ECs, 0.26 << 1 (which means considerable quenching) for SMCs, and 1.69 > 1 (which means activating) for FBs. Then, we applied the software package “Genome Enhancer” (geneXplain platform) to a data set processed in GenomeStudio. The ultimate goal of this pipeline was to identify potential drug targets in the molecular network that governs the studied pathological process. According to the Genome Enhancer, there were twenty main genes (presented in Table 1) that changed their methylation statuses in ECs after being exposed to oscillatory shear stress. After SMCs cells representing an adjacent (to the endothelium) vein wall layer—tunica media—were treated with cell culture media taken from +/− exposed ECs, they also changed their DNA methylation statuses (as shown in Table 2 for top ten hypo- and top ten hypermethylated genes). After FBs representing an adjacent (to the tunica media) vein wall layer—tunica adventitia—were treated with cell culture media taken from +/− treated SMCs, they also changed their DNA methylation statuses (as shown in Table 3 for top ten hypo- and top ten hypermethylated genes). Epigenome-wide DNA methylation profiling revealed the changes in the cell type of each venous wall layer, not only in the venous endothelium exposed to oscillatory shear stress, but also in the cells-representatives of the adjacent layers. For a graphical illustration of the data shown in Table 1, Table 2 and Table 3, we created a cluster heatmap where the average beta values reflecting the methylation levels of the genes (assigned to CpG loci in the group of samples) are represented by colors, and the rows and columns of the data matrix have been ordered according to the output from clustering (Figure 1). In Figure 1, a heatmap of differentially methylated genes is shown for each cell type separately. One can observe that there are clusters of genes that synchronously increase or synchronously decrease in methylation. Additionally, we combined all three sets of genes, each of them being differentially methylated in a certain cell type, with the corresponding beta values in all those cells (treated and untreated), and applied a cluster heatmap analysis in order to compare samples from different cells with each other (Supplementary Figure S1). Interestingly, the result was that they did not differ much (the difference between “treatment” and “control” in one cell type was often higher than the difference between control samples in other cell types). Additionally, in general, these genes had fairly stable methylation statuses in different cell types—they are represented with similar colors along the entire length of the heatmap. However, we focused not just on the hypo-/hypermethylated sites, but rather on their regulators and potential master regulators. Differentially methylated sites were mapped to genes, and the top 300 genes with the highest number of transcription factor binding sites (found in the 400 bp regions around these methylated sites) were selected for further analysis. ## 2.2. Functional Classification of Genes A functional analysis of the top 300 genes near differentially methylated sites was conducted by mapping the input genes to several known ontologies, such as the Gene Ontology (GO)_biological process and the ontology of signal transduction and metabolic pathways from the TRANSPATH® database. Statistical significance was computed using a binomial test. Figure 2, Figure 3 and Figure 4 show the most significant categories for ECs, SMCs, and FBs, correspondingly. GO analysis of the genes associated with differentially methylated sites showed that a number of genes potentially affected by differential methylation upon exposure of ECs to Oscillatory Shear Stress played roles in such processes as negative regulation of muscle cell differentiation (hit names: PDGFB, PLPP7, YY1), positive regulation of cellular component biogenesis (CCP110, HGS, TESK1, TIGD5, TPPP2), regulation of protein autophosphorylation (PDGFB, TESK1), platelet-derived growth factor receptor signaling pathway (HGS, PDGFB), steroid hormone mediated signaling pathway (AR, NR3C2, NR4A2), cellular response to organic cyclic compound (AR, LARP1, NR3C2, NR4A2, PDGFB), vesicle targeting/coating and rough ER to cis-Golgi and vesicle budding from membrane (CNIH2, RAB1B), positive regulation of exocytosis (HGS, RAB9A), regulation of systemic arterial blood pressure (AR, PDGFB), retrograde vesicle-mediated transport, Golgi to endoplasmic reticulum (RAB1B, RER1), macroautophagy (HGS, LARP1, RAB1B), etc. ( see Supplementary Table S4). Full classification of GO categories for SMCs may be seen in Supplementary Table S5. GO analysis revealed that a number of genes potentially affected by differential methylation upon exposure of SMCs to the preconditioned media from ECs (+/− oscillatory shear stress-exposed) played roles in such processes as negative regulation of cellular processes, regulation of nitrogen compound metabolic processes (hit names: C5AR2, CCND2, CD38, CD9, CDH2, CDKN3, CHFR, CHMP6, CLSPN, DCLK1, DDX5, etc.), regulation of metabolic process (C5AR2, CCND2, CCNJL, CCNT2, CCT7, HGS, etc.), negative regulation of cell communication (C5AR2, CD38, CDH2, DRD1, F2R, HGS, HIF1AN, ING2, P2RY1, PSMD13, SIRT3, SPRY2, etc.), regulation of the production of small RNAs involved in gene silencing by RNA (DDX5, LIN28A, TERT), presynaptic active zone organization (ERC2, PCDH17), regulation of protein phosphorylation (C5AR2, CCND2, CCNJL, CCNT2, CDH2, CDKN3, CHMP6, CLSPN, DRD1, F2R, HGS, MICAL1, NCKAP1L, P2RY1, etc.), regulation of blood vessel diameter and size (CD38, CPS1, DRD1, F2R, P2RY1), cell junction maintenance (ERC2, F2R, SYNGAP1), regulation of synaptic vesicle clustering (CDH2, PCDH17), regulation of cyclin-dependent protein serine/threonine kinase activity (CCND2, CCNJL, CCNT2, CDKN3), negative regulation of the epidermal growth factor receptor signaling pathway (CHMP6, HGS, SPRY2), vascular processes in the circulatory system (CD38, CPS1, DRD1, F2R, P2RY1), regulation of presynaptic cytosolic calcium ion concentration (ATP2B2, P2RY1), regulation of exosomal secretion (CHMP6, HGS), and smooth muscle contraction (CD38, DRD1, F2R). HIF1AN (also known as FIH1)—a HIF1-alpha inhibitor—is the cellular oxygen sensor factor inhibiting hypoxia-inducible factor 1 alpha [15] by preventing its transcriptional activity and leading to adaptive responses to hypoxia. HIF1AN plays a critical role in controlling the survival of vascular ECs through interacting with Notch2 and repressing its activity [16]. This may point to an interconnection between ECs and SMCs belonging to adjacent layers. It is worth noting that the HIF1AN antagonist—HIF1-alpha—is not only regulated by the hypoxic stimulus, but can also act as a target for potentiating the protective effects from some adaptogenic triggers [17]. A full classification of GO categories for FBs may be seen in Supplementary Table S6. GO analysis revealed that a number of genes potentially affected by differential methylation upon exposure of FBs to the preconditioned media from SMCs (treated with the preconditioned media from +/− oscillatory shear stress-exposed ECs) played roles in such processes as negative regulation of GTPase activity (hit names: GPS1, IQGAP2, PTPRN2, RCC2), protein glycosylation (B3GALT6, DOLK, GALNT1, GALNT17, MAN2A2, RFNG, TET2, TMTC2, etc.), protein-containing complex assembly (CDC42EP2, CDC42EP4, CHMP6, F8, FMC1, GPX4, etc.), intrinsic apoptotic signaling pathway by p53 class mediator (PTTG1IP, RPL11, SHISA5, WWOX), actin filament polymerization (CDC42EP2, CDC42EP4, IQGAP2, MSRB2, PPP1R9A, TIGD5), the inositol phosphate catabolic process (IMPA2, NUDT3), the oxidation–reduction process (ADHFE1, CYP4B1, DHCR7, DHFR, F8, FAHD1, FXN, GPX4, HIF1AN, MSRB2, NDUFA7, NDUFS3.., etc.), and regulation of the apoptotic signaling pathway (FXN, INHBB, MCL1, NACC2, NDUFS3, NR4A2, PTTG1IP, RPL11, WWOX). For instance, it was shown that activation of CDC42 is involved in the hypoxia-induced production of angiogenesis-promoting factors such as vascular endothelial growth factor (VEGF) [18], as well as in actin filament polymerization [19]. Proteins of this family also play roles in cytoskeletal remodeling and signaling, cell shape, directed migration and differentiation, and pathological fibroblast activation [20]. The aforementioned HIF1AN, participating in the oxidation–reduction process, is also present in FBs. The results of the additional functional analysis of the input genes mapped to the ontology of signal transduction and metabolic pathways, according to the TRANSPATH® database, are shown in Supplementary Figures S2–S4. The affected genes were significantly enriched with specific pathway ontology categories. A full classification of those categories for each cell type may be seen in Supplementary Tables S7–S9. The result of overall GO analysis of the genes near differentially methylated sites can be summarized by the following diagram (Figure 5), which reveals the most significant functional categories overrepresented among the observed genes. Thus, we can report that our epigenome-wide analysis revealed important changes that accompany the effect of oscillatory shear stress on the venous endothelium, which spreads to SMCs and FBs that represent the middle and outer layers of the vein wall, correspondingly. To better understand the relation of differentially methylated genes to the pathological condition, we developed an interactive illustration for the most significant of those genes in different cells and their cellular functions with respect to pathological consequences (Figure 6). The figure shows how the functions of these genes (with the highest value of \|DiffScore\|) could be linked to the pathological processes involved in varicose transformation of the vein wall. ## 2.3. Analysis of Enriched Transcription Factor Binding Sites and Composite Modules In the next step, a search for transcription factor binding sites (TFBS) was performed in the regulatory regions of the target genes by using the TF binding motif library of the TRANSFAC® database. We searched for so-called composite modules acting as potential condition-specific enhancers of the target genes in their upstream regulatory regions (−1000 bp upstream of transcription start site (TSS)) and identified transcription factors regulating the activity of the genes through such enhancers. Classically, enhancers are defined as regions in the genome that increase the transcription of one or several genes when inserted in either orientation at various distances upstream or downstream of the gene [21]. Enhancers typically have a length of several hundreds of nucleotides and are bound by multiple transcription factors in a cooperative manner [22]. In the current work, we used epigenomics data from the tracks (Supplementary Tables S1–S3) to predict the positions of potential enhancers regulating the genes near differentially methylated sites revealed by comparative epigenomics analysis. We took genomic regions −550 bp upstream and 550 bp downstream from the middle point of each interval of the track and checked whether these regions were located inside the 5 kb flanking areas of the genes near differentially methylated sites (or inside the bodies of the genes). In such cases, these genomic regions are used for the search for potential condition-specific enhancers. In all other cases, when the genes near differentially methylated sites did not contain epigenomic peaks in their bodies or in the 5 kb flanking regions, we used the upstream regulatory regions of these genes (−1000 bp upstream and 100 bp downstream of TSS) for our search for condition-specific enhancers. We applied the Composite Module Analyst (CMA) method [8] to detect such potential enhancers as targets of multiple TFs bound in a cooperative manner to the regulatory regions of the genes of interest. CMA applies a genetic algorithm to construct a generalized model of the enhancers by specifying combinations of TF motifs (from TRANSFAC®) whose sites are most frequently clustered together in the regulatory regions of the studied genes. CMA identifies the transcription factors which, through their cooperation, provide a synergistic effect and, thus, have a great influence on the gene regulation process. To build the most specific composite modules, we chose genes as the input for the CMA algorithm. The results of this search are represented in Figure 7. The model consisted of two modules. In Figure 7A–C, the following information is shown for each module: PWMs (position weight matrixes) producing matches, scores of the best matches, and the number of individual matches (N) for each PWM. Through this analysis, we identified TFs whose binding to their control regions may be significantly altered by CpG methylation, leading to shifts in the expression of many genes in our experiment. The CMA algorithm identified enriched combinations of TFs with high statistical significance (Wilcoxon p-value = 2.33 × 10−36 for ECs, 4.34 × 10−35 for SMCs, and 1.62 × 10−48 for FBs). The AUC of the model for FBs achieved a value (=0.81) significantly higher than expected for a random set of regulatory regions (Z-score = 3.81), which means that there are significantly more TF site pairs in CpG regulatory regions compared to the background. On the basis of the enhancer models, we identified transcription factors potentially regulating the target genes of our interest. We found 14, 17, and 7 transcription factors controlling the expression of target genes for ECs, SMCs, and FBs, correspondingly (see Table 4). In the table, ≤10 TFs for each cell type are shown. Full lists of TFs may be seen in Supplementary Tables S10–S12. The key transcription factors which were predicted to be potentially regulating genes near differentially methylated sites in our experiment were: JUN, CDX2, and POU2F1 for ECs; SREBF2, LEF1, and IRF3 for SMCs; and RELA, ESR1, and TFAP2A for FBs. The relevance of these TFs is discussed later on, in the Section 3. ## 2.4. Finding Master Regulators in Networks In the second step of the upstream analysis, common regulators of the revealed TFs were identified. We considered master regulators to be the keynodes with positive feedback loops; master regulator protein controls the activity of TFs which, in turn, activate the gene encoding the master regulator protein. The sorting of master regulators is conducted by total rank. The total rank is a kind of average rank that takes into account both whether this keynode is at the top of the regulatory pyramid (keynode score) and to what degree the gene encoding this keynode is regulated by predicted TFs (CMA score), as well as whether or not this gene contains a hypomethylation or hypermethylation site (epigenomics data). These master regulators appear to be the key candidates for therapeutic targets, as they have a master effect on the regulation of the intracellular pathways that activate the pathological processes which are the focal points of our study. The identified master regulators may be seen in Table 5, where the top 10 master molecules for ECs, SMCs, and FBs, correspondingly, are shown. Full lists of the master molecules for each type of cells may be seen in Supplementary Tables S13–S15. The intracellular regulatory pathways controlled by the aforementioned master regulators are depicted in Figure 8, Figure 9 and Figure 10, where positive feedback is represented by dotted lines. This diagram displays the connections between the identified TFs that play important roles in the regulation of genes near the differentially methylated sites and the selected master regulators that are responsible for the regulation of these TFs. For ECs, master regulators HGS and PDGFB are involved in the platelet-derived growth factor receptor signaling pathway; AR and PDGFB are involved in epithelial cell development and regulation of systemic arterial blood pressure; and AR, HGS, and PDGFB are involved in the regulation of protein phosphorylation and cellular protein metabolic process (according to GO processes presented in Supplementary Table S4). Furthermore, the key pathways for master regulators in ECs are: the AR pathway; the IGF-1 pathway; MKK4 ---JNK1---/AR; PDGF A, PDGF B ---> AKT; PDGF B ---> STATs; PDGF B---/Ras; and the PDGF pathway (according to the pathway categories presented in Supplementary Table S7). For SMCs, master regulators CDH2, HGS, and SPRY2 are involved in such key processes as regulation of the nitrogen compound metabolic process, regulation of cell population proliferation, and blood vessel morphogenesis. CDH2, HGS, P2RY1, and SPRY2 are involved in regulation of cellular metabolic process, negative regulation of cell communication, and phosphorylation. HGS and SPRY2 are involved in negative regulation of the ERBB signaling pathway, regulation of protein kinase activity, and regulation of the epidermal growth factor receptor signaling pathway. CDH2 and P2RY1 are involved in regulation of the synaptic vesicle cycle and neurogenesis. HGS, P2RY1, and SPRY2 are involved in positive regulation of gene expression; HGS and P2RY1 are involved in export from the cell and secretion by the cell (according to GO processes presented in Supplementary Table S5). The key pathways for the master regulators in SMCs are: Spry2 ---> ErbB1; EGF pathway; activin A ---> Smad3; activin A ---> Smad2; TGFbeta pathway; and N-cadherin ---Eplin---> actin; N-cadherin network (according to the pathway categories presented in Supplementary Table S8). For FBs, the master regulators WWOX, F8, and FXN are involved in oxidation–reduction processes; FXN and WWOX are involved in regulation of the apoptotic signaling pathway (according to GO processes presented in Supplementary Table S6). The key pathways for master regulators in FBs are: Src ---/p73beta; ErbB4 mediated signaling; p73 pathway; IL-5 pathway; and SOCS-1 ---/STAT5; IL-2—STAT5 pathway (according to the pathway categories presented in Supplementary Table S9). The Tables with master regulator molecules that have been converted into genes are Supplementary Tables S16–S18 for ECs, SMCs, and FBs, correspondingly. After we performed intersection of those three tables, we found that the first two cell types shared one potential master molecule {HRS(h),HRS(h){pY334},HRS(h){pY},HRS-isoform1(h),HRS-isoform2(h),HRS:PtdIns[3]P:SARA:SMAD2,HRS:PtdIns[3]P:SARA:SMAD3} that corresponded to potential master regulators HGS, SMAD2, SMAD3, and ZFYVE9 (see the Venn diagram in the Figure 11). The second two cell types—SMCs and FBs—representing the middle and outer layers of the venous wall, correspondingly, also shared one potential master molecule {ZNRF1(h),ZNRF1-isoform1(h),ZNRF1-isoform2(h} that corresponded to the potential master regulator ZNRF1. In addition, ZNRF1 was in the fourth place (according to the DiffScore = −30.56, p-value < 0.001) among the genes hypomethylated in SMCs upon our treatment, and this gene was also hypomethylated in FBs, albeit to a lesser extent (DiffScore = −22.02). For the final summary, Genome Enhancer software chose the potential master regulators that were the most interesting and promising in terms of druggability. These master regulators control the activity of certain TFs regulating the genes near differentially methylated sites. Thus, the most targetable master regulators appeared to be the following: [1] HGS, PDGFB, and AR (corresponding to the master molecules {HRS}, {PDGFB}, and {AR-isoform1}), which control the activity of transcription factors JUN, CDX2, and POU2F1 for endothelial cells; [2] HGS, CDH2, SPRY2, SMAD2, ZFYVE9, and P2RY1 (corresponding to the master molecules {HRS}, {N-cadherin}, {Sprouty2}, {HRS:PtdIns[3]P:SARA:SMAD2}, and {P2Y1}), which control the activity of transcription factors SREBF2, LEF1, and IRF3 for smooth muscle cells; and [3] WWOX, F8, IGF2R, NFKB1, RELA, SOCS1, and FXN (corresponding to the master molecules {WOX1}, {F8B}, {IGF-2R}, {p50:NF-kappaB-p65:SOCS-1}, {FXN}, and {SOCS-1}), which control the activity of transcription factors RELA, ESR1, and TFAP2A for fibroblasts (see Figure 12). ## 3. Discussion This study is the first attempt, to the best of our knowledge, to assess the changes to methylation profiles in the cells representing the corresponding layers of the vein wall in response to oscillatory shear stress, which somehow reflects the events happening in incompetent veins (VVs). Such an effort to resolve the heterogeneity of cell composition of the vein as an organ may deliver new insights into the mechanism of VV pathogenesis. Our 2D experimental model may not have been 100 percent realistic, but it definitely covered some aspects of the pathological condition. It is worth mentioning that our experimental design, which utilized oscillatory shear stress, partially represented proatherogenic conditions that are characterized by low-magnitude and oscillatory shear stress [1]. The cellular monolayer lining the tunica intima is normally subjected to biomechanical stimuli resulting from shear stress and from strain due to stretching of the vein wall. Shear stress has been implicated in altering the structure and functional properties of ECs at the cellular and molecular levels, with profound effects on physiology [23]. The vascular endothelium in vivo acts as a signal transduction interface for hemodynamic forces which determine the cytoskeletal organization, shape, and function of ECs, allowing the vessels to cope with physiological or pathological conditions [24]. This must be true for conditions in vitro. Interestingly, in response to shear stress, ECs increase NO production leading to an enhancement of the shear stress response of leukocytes [25]. On the other hand, we cannot exclude the outside-in hypothesis, according to which the tunica adventitia may be a sensor of vascular wall disruption and dysfunction, as well as an early responder and activator of the blood vessels’ response to injury [26]. The tunica media is in between, and must react and adjust to all possible effects from the inside and outside of the vein wall. In this work, we have shown that the exposure of the venous endothelium to oscillatory shear stress not only resulted in epigenome-wide changes within this layer, but exerted even more prominent changes in the neighboring layers of the vein wall. Moreover, we observed different ratios of hypo- to hypermethylated genes, which may reflect possible changes caused by oscillatory shear stress in the overall transcription processes within the corresponding cell type of the vein layer: quenching for ECs (0.89 < 1), considerable quenching for SMCs (0.26 << 1), and activation for FBs (1.69 > 1). In turn, this may overlap with morphological changes during the varicose transformation of the venous wall, when endothelial dysfunction, impairment of the functional smooth muscle layer, and accrescence of adventitia are often observed. It is amazing how an impact of oscillatory shear stress on the inner layer alone is capable to launch much bigger changes in the adjacent layers of the vessel. In the current work, we were limited by the methylation studies, but a combination of DNA methylation with gene expression could provide much more information about the molecular mechanisms involved. Herein, we used 27 K CpG arrays that represented only a fraction of the CpG positions in the genome (located near gene bodies and covering mainly predominately invariant methylation regions) that can become methylated and potentially affect gene regulation; thus, other regions not covered by these arrays were not included in our analysis. Despite this, we were able to discover a number of differentially methylated CpGs mapped to the genes whose functions were linked to the transformative changes that occurred in the vein wall (Figure 6). In addition, in our study, we conducted an analysis of the individual DMSs (differentially methylated sites) located in various genes, but we did not analyze DMRs (differentially methylated regions) that could potentially provide additional information about epigenetic changes in the genome and draw attention to some specific genes, though DMRs usually have nothing to do with gene regulation. Conventional approaches of statistical “OMICs” data analysis provide only very limited information about the causes of the observed phenomena, and, therefore, contribute little to the understanding the pathological molecular mechanism. In contrast, the “upstream analysis” method [11,12,13,14] applied herein is designed to provide a casual interpretation of the data obtained for a pathology state. This approach comprises two major steps: [1] analyzing promoters and enhancers of differentially expressed genes for the transcription factors (TFs) involved in their regulation and, thus, important for the process under study; [2] reconstructing the signaling pathways that activate these TFs and identifying master regulators at the top of such pathways. For the first step, the database TRANSFAC® [27] is employed together with the TF binding site identification algorithms Match [28] and CMA [21], so that pipeline discovers TFs that regulate genes’ activities in a pathological state. The activities of these TFs are controlled by so-called master regulators, which are identified in the second step of analysis. The second step involves the signal transduction database TRANSPATH® [22] and special graph search algorithms [29] implemented in the “Genome Enhancer” software. After a subsequent druggability checkup, the most promising master regulators are chosen as potential drug targets for the analyzed pathology. In the present work, we have revealed the master regulators that control the activity of certain TFs regulating genes near the differentially methylated sites for ECs, SMCs, and FBs, which represent the three layers of the venous wall. Herein, we will discuss only those potential master regulators which are the most promising in terms of druggability [30]. JUN, CDX2, and POU2F1 have been revealed as the key transcription factors predicted to potentially regulate genes near differentially methylated sites in our experiment for ECs exposed to oscillatory shear stress. JUN (Jun proto-oncogene, AP-1 transcription factor subunit) is a member of the Jun family of proteins, which are primary components of the activating protein transcription factor [31]. It is inducible by hypoxia related to endothelial cell barrier dysfunction [32]. CDX2 (caudal-type homeobox-2) is a member of the caudal-related homeobox transcription factor gene family. Aberrant expression of the CDX2 gene is associated with intestinal inflammation [33]. Researchers hypothesize that it is related to the reconstruction of the blood vessels [34]. POU2F1 (POU class 2 homeobox 1) is shown as a transcription factor regulated by DNA damage [35] and as a transcriptional repressor for genes expressed in ECs [36]. The POU2F1 gene may play an important role in the development of primary VVs [37], as well as generally, in the condition of the vascular system [38]. The most obvious master regulators of the aforementioned TFs were HGS, PDGFB, and AR. HGS (hepatocyte growth factor-regulated tyrosine kinase substrate) is involved in tight junction protein trafficking and ECs permeability [39]. HGS is necessary for maintaining cerebrovascular stability [40]. PDGFB (platelet-derived growth factor subunit B) is expressed at a very low level in healthy vessels [41]. It contributes to the migration and proliferation of SMCs [42] and plays a role in cell growth, apoptosis, and actin reorganization [41]. It is believed that platelet-derived growth factor is necessary for tunica intima growth and to prevent regression of its thickening [43]. Platelet-derived growth factors are involved in tissue homeostasis regulation due to control of the interstitial fluid pressure [44]. ARs (androgen receptors) play a role in vascular calcification [45], vascular SMC migration [46], endothelial dysfunction [47,48], and induction of vascular SMC apoptosis [49]. In veins from organ donor extraction (from patients without VVs) ARs were located in the adventitia. The redistribution of ARs through the venous wall was observed in VV conditions, and as a result, AR-positive cells were found in the neointima [50]. SREBF2, LEF1, and IRF3 have been revealed as the key transcription factors predicted to potentially regulate genes near differentially methylated sites in our experiment with SMCs treated with culture media from ECs ± exposed to oscillatory shear stress. In vascular endothelial cells, SREBF2 (sterol regulatory element binding transcription factor 2) is activated by sterol loss [51] and oscillatory shear stress [52]. It also promotes TGF-β1-induced cell movement [53]. LEF1 (lymphoid enhancer binding factor 1) plays an important role in embryogenesis and tumorigenicity [54]. LEF1 suppresses the expression of epithelial/endothelial–mesenchymal transition-relevant genes, which contributes to the malignancy of colonic adenocarcinomas [55]. IRF3 (interferon regulatory factor 3) is member of a family of transcription factors for genes associated with innate and adaptive immune responses [56]. In response to low shear stress, IRF3 is activated, which leads to endothelial inflammation [57]. For SMCs, the most obvious master regulators of the TF identified were HGS, CDH2, SPRY2, SMAD2, ZFYVE9, and P2RY1. CDH2 (cadherin 2) is essential for vascular SMC survival [58]. Inhibition of CDH2 function retards SMC migration and the promotion of ECs survival [59]. Blockade of SPRY2 (sprouty RTK signaling antagonist 2) (together with blockade of Dll4) leads to augmentation of the expression of venous markers in arteries [60]. SPRY2 is upregulated in response to fibroblast growth factor 2 in primary dermal ECs [61]. SMAD2 (SMAD family member 2) mediates the signal of the transforming growth factor beta, which allows for the regulation of cell proliferation, apoptosis, and differentiation. Low fluid shear stress activates SMAD2, leading to inward remodeling in atherosclerotic vessels [62]. ZFYVE9 (zinc finger FYVE-type containing 9) participates in the transforming growth factor beta signaling pathway. ZFYVE9 recruits the aforementioned master regulator SMAD2 to the transforming growth factor beta receptor complex by controlling its subcellular localization [63]. P2RY1 (purinergic receptor P2Y1) functions as a receptor for extracellular ATP and ADP. The expression of the P2Y1 in vascular ECs has also been shown [64]. It has previously been demonstrated that P2Y1 mediates ADP stimulation of MAPK pathways and ECs migration [65]. RELA, ESR1, and TFAP2A have been revealed as the key transcription factors predicted to potentially regulate genes near differentially methylated sites in our experiment with FBs exposed to culture media from pretreated SMCs. RELA (RELA proto-oncogene, NF-kB subunit) is a member of the NF-kB family [66]. The NF-kB pathway can be activated by different stimuli, including cytokines, oncogenes, oxidative stress, and DNA damage [67,68]. RELA inhibition led to the inactivation of proinflammatory molecules [69]. ESR1 (estrogen receptor 1) gene expression in the VVs of women around menopause noticeably increases. ESR1 is present in the endothelium, SMCs, and some adventitial cells in the femoral veins [70]. TFAP2A (transcription factor AP-2 alpha) is expressed in the neural tube, neural crest, facial prominences, and limb bud mesenchyme throughout embryogenesis [71]. In ECs, TFAP2A plays a role in cell proliferation [72]. For FBs, the most obvious master regulators of the TF identified were WWOX, F8, IGF2R, NFKB1, RELA, SOCS1, and FXN. WWOX (WW domain containing oxidoreductase) is involved in cell proliferation, differentiation, and metabolism [73]. Mutations in the WWOX gene cause neurodevelopmental and brain degenerative disorders [74]. F8 (coagulation factor VIII) participates in the intrinsic pathway of blood coagulation. Defects in this gene result in hemophilia A, a common recessive X-linked coagulation disorder [75]. Activation of cardiac IGF2R (insulin like growth factor 2 receptor) results in cardiomyocyte hypertrophy, cardiomyocyte proliferation, binucleation, or apoptosis [76]. NFKB1 (nuclear factor kappa B subunit 1) gene mutants affect the expression of mitochondrial morphology-related proteins, leading to excessive mitochondrial fission [77]. SOCS1 (suppressor of cytokine signaling 1) is a member of the STAT-induced STAT inhibitor. A decrease in SOCS1 promotes immune activation of SMCs [78]. FXN (frataxin) is a mitochondrial protein [79] expressed mainly in tissues with high metabolic rates (e.g., heart and brown fat) [80]. After three tables showing the most promising master regulators for each cell type (Tables S16–S18, where all master molecules were converted into genes) were intersected, it was revealed that ECs and SMCs share such potential master regulators as HGS, SMAD2, SMAD3, and ZFYVE9, and SMCs and FBs share a potential master regulator—ZNRF1. It is known that rs17684886 in ZNRF1 is associated with diabetic retinopathy [81], and its expression is induced in peripheral nerves after injury [82], so its overexpression causes neurite-like elongation. It has been found that expression of the SMAD2 protein is progressively increased in reactive lesions and oral submucous fibrosis (OSMF) [83], and SMAD3 contributes to ascending aortic dilatation independently of transforming growth factor-beta in bicuspid and unicuspid aortic valve disease [84], which is consistent with our data. A meta-analysis of epigenome-wide association studies in trauma-exposed cohorts revealed the association of the HGS differential methylation in whole blood-derived DNA with post-traumatic stress disorder [85]. Recently, a novel physiological role of endogenous HGS—a key component of the endosomal sorting complex required for transport (ESCRT)—has been explored in the vascular system. It was discovered that in mice, knockout of this gene in brain ECs led to impaired endothelial apicobasal polarity and brain vessel collapse; thus, the product of this gene was essential for vascular endothelial (VE)-cadherin recycling to the plasma membrane, pointing to a crucial function of HGS in the maintenance of endothelial cell polarity and cerebrovascular stability [40]. All of these studies appear to be supportive of the data analyzed in this study. ## 4.1. Sample Preparation and Cell Culture Experiments Non-varicose great saphenous vein segments (adjacent to varicose vein segments) left after surgeries on 3 patients with VVs (C2-C3 CEAP [86] clinical classes) were immediately placed in cell culture media and transported to the laboratory for the subsequent production of primary cell cultures. Mechanically crushed fragments of the vein segments were treated with a solution of type II collagenase. A growth medium was added to the obtained suspensions of pieces and cells and centrifuged for 5 min at 300× g; then, the supernatant was taken and the sediment was resuspended in the medium for endothelial growth before being seeded on adhesive plates coated with type IV collagen. Pieces with cells (in the medium, but not covered with it, to prevent floating) were cultured under conditions of $5\%$ CO2, 37 °C. When there was a sufficient density of cells that had grown from the pieces (in reality, this was a mixture of endothelial cells, smooth muscle cells, and fibroblasts), they were removed from the plastic with a TrypLE Express solution (Life Technologies, Carlsbad, CA, USA) and sorted by magnetic immunosorting using a CD31 MicroBead Kit (Miltenyi Biotec) according to the manufacturer’s instructions. CD31+ endothelial cells were seeded in endothelial growth medium (EGM-2 Endothelial Medium, Lonza, Basel, Switzerland), CD31− cells were seeded in the growth media for SMCs and FBs (SmGM-2 Smooth Muscle/FGM-2 Fibroblast BulletKits, Lonza), respectively, on adhesive plates coated with type IV collagen (primary cell cultures are illustrated in Supplementary Figure S5). The endothelial cells grew much more slowly compared to SMCs and FBs. ECs were either exposed to oscillatory shear stress for 1 day (using a Multitron Cell shaker-incubator (INFORS HT) at 37 °C and $5\%$ CO2, with platform oscillation only in the plane along the XY axis) or left in static conditions (37 °C and $5\%$ CO2). Then, SMCs were treated for 1 day with preconditioned media from ECs, and FBs were subsequently treated for 1 day with preconditioned media from SMCs. Each experimental condition was performed in triplicate. After every exposure, the cells were harvested and subjected to DNA isolation using TRIzol Reagent (Life Technologies, Carlsbad, CA, USA). The experiment design is schematically shown in Supplementary Figure S6. DNA from all samples was further processed according to the manufacturer’s instructions (Illumina, Inc., San Diego, CA, USA), and then taken for methylation microarray analysis. ## 4.2. Epigenome-Wide DNA Methylation Analysis DNA methylation microarray analysis was carried out according to the standard Illumina protocol. A total of 1 μg of gDNA was bisulfite converted using a EZ DNA Methylation™ Kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s protocol. After that, for genome-wide screening of methylation events, we used Infinium HumanMethylation27 BeadChips (Illumina), which cover 27,578 CpG sites spanning 14,495 genes per sample. Arrays were scanned on the Illumina iScan. Overall chip performance and the quality of the raw data were checked using Illumina GenomeStudio (methylation module) software in accordance with the manufacturer’s instructions (GenomeStudio Methylation Module v1.8 User Guide [87]). The raw intensity data were quantile-normalized. The methylation level of each CpG locus was calculated as methylation beta-value (β = intensity of the methylated allele (M)/(intensity of the unmethylated allele (U) + intensity of the methylated allele (M) + 100). Differential methylation (hypo- or hypermethylation) of the CpG sites was determined based on a DiffScore cut-off of ±13. DiffScore is the measure of the likelihood of variability between the compared groups. It is directly derived from the p-value, for which it provides methylation change directionality. It is a log10 transformation of the p-value and provides the p-value with scale and direction: p-value = [10^(DiffScore/10)] for hypomethylated genes, and [10^(−DiffScore/10)] for hypermethylated genes. The higher (or lower) the DiffScore is, the more likely it is that a change in methylation has taken place. Statistically significant values were determined (p-value < 0.05 that corresponds to \|DiffScore\| > 13): DiffScore < 0 corresponds to hypomethylated genes; DiffScore > 0 corresponds to hypermethylated genes. Differentially methylated (DM) sites were associated with genes using the Custom Model of the Illumina GenomeStudio. Then, the software package “Genome Enhancer” from the geneXplain platform was applied to a data set analyzed with GenomeStudio and Microsoft Excel. A cluster heatmap analysis was performed within the geneXplain platform. ## 4.3. Advanced Bioinformatics Analyses Transcription factor binding sites (TFBS) in promoters and enhancers of genes near differentially methylated sites were analyzed using known DNA-binding motifs described in the TRANSFAC® library, release 2022.2 (geneXplain GmbH, Wolfenbüttel, Germany) (https://genexplain.com/transfac (accessed on 22 December 2022)). The motifs were specified using position weight matrices (PWMs), which gave weights to each nucleotide in each position of the DNA-binding motif for a TF or a group of them. We searched for TFBS that were enriched in the promoters and enhancers under study as compared to a background sequence set, such as promoters of genes that were not differentially regulated under the conditions of the experiment. We denoted study and background sets briefly as Yes and No sets. In the current work, we used a workflow considering promoter sequences of a standard length of 1100 bp (−1000 to +100). The error rate in this section of the pipeline was controlled by estimating the adjusted p-value (using the Benjamini–Hochberg procedure) in comparison to the TFBS frequency found in randomly selected regions of the human genome (adjusted p-value < 0.01). We applied the CMA (Composite Module Analyst) algorithm for the purpose of searching for composite modules [28] in the promoters and enhancers of the Yes and No sets. We searched for a composite module consisting of a cluster of 10 TFs in a sliding window of 200–300 bp that statistically significantly separated sequences in the Yes and No sets (minimizing the Wilcoxon p-value). Then, we searched for master regulator molecules in signal transduction pathways upstream of the identified TFs. The master regulator search used the TRANSPATH® database (BIOBASE), release 2022.2 (geneXplain GmbH, Wolfenbüttel, Germany) (https://genexplain.com/transpath (accessed on December 2022)). A comprehensive signal transduction network of human cells was built by the software on the basis of reactions annotated in TRANSPATH®. All signal transduction reactions from TRANSPATH® (including ligand binding reactions, phosphorylation and dephosphorylation reactions, complex formation reactions, ubiquitination, and other reactions known from the scientific literature) were considered as a weighted and directed graph. The main algorithm of the master regulator search has been described in earlier works [13,14]. The goal of the algorithm was to find nodes in the global signal transduction network that may potentially regulate the activity of a set of TFs found at the previous step of the analysis. Such nodes are considered as most promising drug targets, since any influence on such a node may switch the transcriptional programs of hundreds of genes that are regulated by their respective TFs. In our analysis, we ran the algorithm with a maximum radius of 12 steps upstream of each TF in the input set. The error rate of this algorithm is controlled by applying it 10,000 times to randomly generated sets of input transcription factors of the same set size. Then Z-score and FDR (false discovery rate) value of ranks were calculated for each potential master regulator node on the basis of such random runs (see detailed description in [22]). The error rate was controlled by an FDR threshold of 0.05. ## 5. Conclusions The present in vitro study on methylation profiling identified epigenome-wide changes in the cells that represent the corresponding layers of the vein wall in response to oscillatory shear stress towards the endothelium. These epigenomic changes may be implicated in creating altered phenotypes of those cells, which reflects the morphological changes observed in incompetent veins (VVs). 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--- title: A Multimethodological Approach for the Valorization of “Senatore Cappelli” Wheat Milling By-Products as a Source of Bioactive Compounds and Nutraceutical Activity authors: - Giuliana Vinci - Sabrina Antonia Prencipe - Federica Armeli - Rita Businaro journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048793 doi: 10.3390/ijerph20065057 license: CC BY 4.0 --- # A Multimethodological Approach for the Valorization of “Senatore Cappelli” Wheat Milling By-Products as a Source of Bioactive Compounds and Nutraceutical Activity ## Abstract Wheat is the third most cultivated cereal in the world and represents the major contributor to human nutrition. Milling wheat by-products such as husks (17–$20\%$ of the total processing output weight), even if still containing high-value-added bioactive compounds, are often left untreated or unused, thus resulting in environmental and human health burdens. In these regards, the present study is aimed at evaluating in a multimethodological approach the nutraceutical properties of durum wheat husks belonging to the ancient cultivar “Senatore Cappelli”, thus assessing their potential as bioactive compound sources in terms of phytochemical, cytotoxic, and nutraceutical properties. By means of HPLC-FD analyses, wheat husk samples analyzed revealed a higher content of serotonin, amounting to $35\%$ of the total BAs, and were confirmed to occur at biogenic amines quality index (BAQI) values <10 mg/100 g. In addition, spectrophotometric assays showed a significant variable content in the phenolic (189.71–351.14 mg GAE/100 g) and antioxidant compounds (31.23–37.84 mg TE/100 g) within the wheat husk samples analyzed, according to the different cultivar areas of origin. Considering wheat husk extracts’ anti-inflammatory and antioxidant activity, in vitro analyses were performed on BV-2 murine microglia cells cultured in the presence or absence of LPS, thus evaluating their ability to promote microglia polarization towards an anti-inflammatory phenotype. Cytotoxicity assays showed that wheat extracts do not affect microglia viability. Wheat husks activity on microglial polarization was assessed by analyzing the expression of M1 and M2 markers’ mRNA by RT-PCR. Wheat husk antioxidant activity was assessed by analysis of NRF2 and SOD1 mRNA expression. Moreover, the sustainability assessment for the recovery of bioactive components from wheat by-products was carried out by applying the life cycle assessment (LCA) methodology using SimaPro v9.2.2. software. ## 1. Introduction Cereals and cereal-based products represent one of the major components of human nutrition, thus representing the base of the food pyramid and accounting for more than $55\%$ of the total consumption in the Mediterranean diet. Among cereals, durum wheat (Triticum turgidum L. subsp. durum) has a core role in the Italian diet and in the national economy, thus resulting in a production ranging between 3850 and 3900 million tons in 2021, with an increase of around +$1.5\%$ over 2020 [1]. Numerous studies have confirmed that cereals exert a protective action on human health, and they are a rich source of bioactive components, thus providing an excellent amount of dietary fiber, proteins, and antioxidants that can have health-promoting effects (i.e., cholesterol-lowering properties, anti-inflammatory effects, chronic diseases prevention, etc.) [ 2]. In particular, the reasons for the protective effects of cereals on human health could be mainly ascribed to the physio-chemical properties and structure of the grain (quantity, grain size, type of fiber, amount, and quality of phytochemical compounds as well as amylopectin and amylose content) [3]. Recent studies concerning the health benefits of wheat-based functional products have been increasingly focusing on the importance of introducing phytochemical compounds through the use of different wheat cultivars. Consequently, there is a renewed interest in the ancient varieties, particularly with regard to their potential nutraceutical quality [4]. During the milling process, most cereals, including wheat, undergo a series of treatments aimed at separating the outer fractions of the seed from the endosperm, intended for processing and transformation into cereal-based products (i.e., flour, pasta, bread, etc.). Nevertheless, increasing mechanization and industrialization have provided both food technologists and researchers with challenging problems arising from the production of processing by-products [5]. In particular, wheat husk (WH) is the major by-product of wheat milling. It is the outer layer of the grain, also called pericarp, that surrounds the endosperm and germ of the wheat grain. In whole wheat kernels, the WH is a multilayered tissue that accounts for 15–$20\%$ of the total processing weight, representing nowadays about 30 million tons of wheat milling by-products produced in the European Union [6]. Considering its physicochemical and organoleptic properties, WH consists of raw lignocellulosic material with a compact structure made up of cellulose (36–$39\%$), hemicelluloses (18–$21\%$), and lignin ($16\%$) [7], which still contains a high content of bioactive compounds, particularly antioxidants such as phenolic compounds, carotenoids, etc. [ 7,8]. In these regards, different studies in the literature focus on the re-use of agricultural waste including wheat husks for renewable energy production [5,7], biofuel production, and biogas generation [9] as well as for the production of functional and value-added food products, cosmetics, feed for livestock use, natural bio-fertilizers, etc. [ 10]. Nevertheless, these residues or agro-industrial wastes are often left untreated or unused, so disposal is through dumping on land, incineration, or landfilling, thus resulting in the deposition of contaminants in the ecosystem and human health [11]. Therefore, the valorization of agricultural by-products through the extraction and recovery of molecules with high nutritional value (e.g., polyphenols, antioxidants, serotonin, etc.) as a new resource to be reused in other production processes could represent an alternative to incineration or composting. In these regards, the present study is aimed at evaluating in a multimethodological approach the nutraceutical properties of two ancient Italian durum wheat husks belonging to the ancient cultivar “Senatore Cappelli” from two different cultivation areas (Puglia and Tuscany), thus assessing their potential as bioactive compound sources in terms of phytochemical, antioxidant, and anti-inflammatory properties. To this purpose, the content of total phenolic compounds (TPC) and total flavonoid (TFC) and antioxidant activity were carried out by ABTS and DPPH assays. Neurological disorders such as AD and PD are characterized by the accumulation of misfolded proteins that contribute to chronic microglial hyperactivation. The release of pro-inflammatory mediators that trigger neuroinflammation, exacerbated by oxidative stress, leads to neuronal death [12]. Much attention is now focused on bioactive molecules present in functional foods and in industrial processing waste for their anti-inflammatory and antioxidant properties to counteract neuroinflammation [13]. Considering that wheat husk extracts anti-inflammatory and antioxidant activity, in vitro analyses were performed on BV-2 murine microglia cells cultured in the presence/or absence of lipopolysaccharide (LPS), thus evaluating their ability to promote microglia polarization towards an anti-inflammatory phenotype. Neuroinflammation is the main driver of several chronic neurodegenerative diseases including Alzheimer’s disease (AD), Parkinson’s disease (PD), and major depression. Microglial cells possess the mechanisms to worsen the inflammation or on the contrary to lead to the repair of the damage depending on the stimuli they receive from the microenvironment [14]. When microglia cells are activated by an inflammatory stimulus, they take on a pro-inflammatory M1 phenotype associated with the expression of markers such as inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) that mediate inflammatory signaling through Toll-like receptor 4 (TLR4) [15]. The M1 phenotype is also associated with the release of pro-inflammatory cytokines, interleukins, and chemokine ligands such as CCL2 [16]. When the injury resolves, the microglia polarizes toward an anti-inflammatory M2 phenotype and, with the release of anti-inflammatory cytokines associated with Arginase-1 (Arg-1) and expressed by macrophages, plays a key role in immune response regulation, primarily through the competition between intracellular iNOS and Arg-1 for arginine. M2-activated microglia upregulate the expression of another anti-inflammatory mediator, namely CD206, a mannose receptor pattern-recognition receptor [17]. An additional anti-inflammatory marker is chitinase-like 3 (Chil3), which encodes for the protein Ym1 [18]. To evaluate M1 or M2 states in microglial cells untreated and extract-treated in the absence or presence of LPS, we analyzed the mRNA levels of M1 and M2 markers. In addition, we investigated the antioxidant effect of wheat-husk-derived extracts by analyzing the mRNA expression of key genes involved in the cellular antioxidant system. In addition, to evaluate the quality and safety of raw matrices, the detection of eight biogenic amines, namely 2-phenylethylamine (B-Pea), putrescine (Put), cadaverine (Cad), histamine (His), tyramine (Tyr), spermine (Spm), spermidine (Spd), and serotonin (Ser), was investigated by means of high-performance liquid chromatography coupled with fluorometric detection (HPLC-FD). Moreover, the sustainability assessment for the recovery of bioactive compounds from wheat by-products was carried out through the application of the life cycle assessment (LCA) methodology by using SimaPro v9.2.2. software. ## 2.1. Chemicals 2-phenylethylamine (B-Pea), putrescine (Put), cadaverine (Cad), histamine (His), tyramine (Tyr), spermine (Spm), spermidine (Spm), serotonin (Ser), Dansyl-Chloride (DSN-Cl), sodium bicarbonate (NaHCO3), ammonium hydroxide (NH4OH), sodium hydroxide (NaOH), sodium carbonate (Na2CO3), Folin–Ciocalteu reagent (H3[P(W3O10)4]/H3[P(Mo3O10)4], 2,2-Diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis (ABTS), sodium nitrite (NaNO2), and aluminum chloride (AlCl3) were purchased from Sigma-Aldrich (Milan, Italy). The following solvents were purchased from Sigma-Aldrich (St. Louis, MO, USA): acetone (C3H6O), perchloric acid (HClO4), acetonitrile, ACN (CH3CN), methanol (CH3;OH), and distilled water (d-H2O), all of which were HPLC-grade. Further used materials include Dulbecco’s Modified Eagle’s Medium High Glucose (DMEM, Sigma Aldrich, St. Louis, MO, USA); Fetal Bovine Serum (FBS, Sigma Aldrich, St. Louis, MO, USA); L-glutamine of penicillin-streptomycin, non-essential amino acids, and sodium pyruvate (Sigma Aldrich, St. Louis, MO, USA); 1× Tripsin-EDTA (Aurogene, Rome, Italy); Trypan Blue solution (1:1) (Corning, Glendale, AZ, USA); Lipopolysaccharide (LPS Sigma Aldrich, St. Louis, MO, USA); Qiazol Lysis Reagent (Qiagen, Hilden, Germany); and Power SYBR® Green PCR Master Mix (Applied Biosystem, Foster City, CA, USA). RNA was extracted miRNeasy Micro kit (Qiagen, Hilden, Germany). The cDNA was generated using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). ## 2.2. Instruments The instruments used for the analyses were the following: NEYA 10R refrigerated centrifuge (Exacta Optech, Modena, Italy), IKA T18 digital Ultra–Turrax (IKA-group, Saufen, Germany), Bandelin Sonorex RK100H water and ultrasonic thermostatic bath, Whatman 0.45 µm 100 (PTFE) syringe filters (Sigma Aldrich, Milan, Italy), and UV–vis spectrophotometer (Jenway, Stone, UK). The chromatographic analysis of biogenic amines was performed using an ATVP LC-10 HPV binary pump with an RF-10° XL fluorometric (FD) detector (Shimadzu, Kyoto, Japan) working at λ emission = 320 nm and λ excitation = 523 nm. A Supelcosil LC-18 column (250 mm × 4.6 mm, 5 µm) with a Supelguard LC-18 (Supelco, Bellefonte, PA, USA) pre-column was used for the analysis of biogenic amines as well as a Steri-Cycle CO2 Incubator (Thermo Electron Corporation, Waltham, MA, USA). Cell culture 6-well and 48-well plates (Sarstedt, Nümbrecht, Germany) were used; RNA was quantified with NanoDrop One/OneC (Thermo Fisher Scientific, Waltham, MA, USA), and quantitative real-time PCR (qPCR) was performed on an Applied Biosystems 7900HT fast real-time PCR system (Applied Biosystem, Cheshire, UK) using the SDS2.1.1 program (Applied Biosystem, Foster City, CA, USA), ## 2.3. Sampling The study analyzed two wheat husk (WH) samples of the ancient “Senatore Cappelli” (SC) cultivar from two different wheat production chains. The first SC cultivar was located in the hilly territory of Val d’Orcia (WH1) in the Tuscany region, while the WH2 samples were cultivated on the karstic Murge upland, which is located in the Puglia region, as shown in Figure 1. In particular, owing to the peculiar pedo-climatic characteristics of these areas (mild climate, distributed rainfall throughout the year), the soil is characterized by a high percentage of loam and clay and a lower percentage of sand; it is also flat and has good drainage. Both samples were cultivated from October 2021 to June 2022. Approximately 200 g of each SC durum wheat husk was previously ground finely using a blender and then sieved using a sieve with 0.7 to 2.0 mm diameter holes. The obtained particle size fractions were collected and stored at refrigerated temperature, namely T = −18 °C, until the day of analysis. Raw matrices WH1 and WH2 had a moisture content of $6\%$ when analyzed. Both raw matrices and hydroalcoholic wheat husk extracts were characterized by HPLC chemical, spectrophotometric, anti-inflammatory, and cytotoxicity analysis, as shown in Figure 2. ## Biogenic Amines Extraction from Wheat Husk The biogenic amines (BAs) determination was carried out according to a previously published method [19] with some modifications. Briefly, 1 g (±0.01 g) of SC wheat husk was added with 12 mL of 0.6 M HClO4. The samples were then homogenized and centrifuged at 2700 rpm for 10 min at $T = 25$ °C. The supernatant was collected in a flask. The BAs extraction procedure was repeated twice. Then, the second extract was added to the first one and filtered through a 0.45 µm membrane syringe filter. The final volume was adjusted to 25 mL with 0.6 M HClO4. For the derivatization procedure, a 1 mL aliquot of the final extract was added to 200 µL of 2 M NaOH, 300 µL of saturated NaHCO3 solution, and 2 mL of dansyl chloride solution (10 mg/mL in acetone). After stirring, the samples were left in the dark for 60 min at 45 °C. To stop the dansyl chloride reaction, ~100 µL of $25\%$ NH4OH was added. The final volume was made up to 5 mL by adding acetonitrile. The dansylated extract was filtered using a 0.45 µm filter and injected into the HPLC system. Analytes were eluted using Supelcosil LC-18 column (250 mm × 4.6 mm; 5 µm) in reverse phase with Supelguard LC-18 pre-column coupled with fluorometric detection. The flow rate was set at 1.2 mL/min, and the column temperature was set at $T = 30$ °C. The elution sequence started with 3 min of isocratic elution ($50\%$ ACN; $50\%$ water), reaching $100\%$ ACN after 18 min. Subsequently, the starting isocratic condition ($50\%$ ACN; $50\%$ water) was restored. Method accuracy (recovery > $95\%$), and precision (RSD < $4.6\%$) were evaluated by analyzing the SC extracts at three different concentrations of BAs. The results obtained from the triplicate analysis were expressed through a calibration curve for each BA, ranging from 0.1 and 25 mg/L. The biogenic amines quality index (BAQI) was calculated based on BAs results to determine the SC samples’ quality loss. For BAQI values <10, the product can be considered safe [20]. It was calculated as follows and expressed in µg/g:BAQI =(PUT+CAD+HIS)(1+SPM+SPD) ## 2.5.1. Hydroalcoholic Extraction of SC Wheat Husk Sample extraction was performed according to the method of Zhang et al. [ 2021] with slight modifications [21]. About 10 g (±0.01 g) of each representative husk sample was weighted and placed into 50 mL glass centrifuge tubes, and 25 mL of ethanol in aqueous solution (80:20, v/v) was added. The samples were homogenized in an ultrasonic and thermostatic bath (Bandelin Sonorex, RK100H) at 400 MHz and at room temperature for 5 min and then centrifuged at 2700 rpm for 10 min at $T = 25$ °C with a NEYA 10R refrigerated centrifuge (Exacta Optech, Modena, Italy). The supernatant was collected in a 50 mL flask. The residue was added with 25 mL of ethanol in aqueous solution (80:20, v/v), mixed, and again centrifuged for 10 min. Then, the second extract was added to the first one and filled with ethanol/water (80:20, v/v) to the mark. For targeted analysis of “Senatore Cappelli” durum wheat husk samples (polyphenols, antioxidants anti-inflammatory activity), the extracts were filtered using 0.45 µm filter (Whatman® Puradisc filters, Sigma Aldrich, Milan, Italy). Extractions were performed on the day of analysis, and extracts were stored at $T = 4$ ± 2 °C. ## 2.5.2. Total Phenolic Content (TPC) The total phenolic content of wheat husk samples was determined according to the Folin–Ciocâlteu method, as Gobbi et al. [ 19] reported. The absorbance of the samples was read at 750 nm against blank solution (EtOH:H2O, 80:20 v/v). The results were expressed as milligrams of gallic acid equivalents per g of wheat husk (mg GAE/g wheat husk). The results were obtained through a calibration curve ranging from 20 to 250 mg/L (R2 = 0.9998). All the measurements were carried out in triplicate. ## 2.5.3. Total Flavonoids Content (TFC) The TFC of wheat husk samples was evaluated according to the aluminum-chloride method described by Abdel-Naeem et al. [ 2021], with some modifications [22]. To 0.5 mL of the hydroalcoholic extract, 2 mL of d-H2O and 150 µL of NaNO2 ($5\%$ w/v) were added to a 5 mL volumetric flask. The solution was stirred and incubated in the dark for 5 min, then 150 µL of AlCl3 ($10\%$ w/v) was added, and the solution was put back in the dark for 5 min. Next, 2 mL of NaOH (1 M) was added to the solution and left in the dark for a further 15 min. Subsequently, samples were made up to final volume of 5 mL by adding d-H2O. The absorbance of the wheat husk samples was read at 510 nm against EtOH:H2O, 80:20 v/v. TFC results were expressed as milligrams of rutin equivalent (RE) per g of the wheat husk (mg RE/g) by linear regression, ranging between 50 and 1000 mg/L (R2 = 0.9995). The results were expressed as means ± standard deviation (SD) of three replicates. ## 2.5.4. Antioxidant Activity Determination by ABTS and DPPH Assays The antioxidant activity of wheat husk samples was evaluated using two different spectrophotometric analyses: DPPH and Trolox-equivalent antioxidant capacity (TEAC) assays. The free radical scavenging activity of wheat husks was evaluated by the DPPH assay, according to a previously reported method [23]. The scavenging activity was measured at 517 nm. All experiments were assessed in triplicate, and values were reported as a mean of EC50 ± standard deviation (SD); the EC50 corresponded to the concentration of the wheat husk samples (mg/mL of extract) that provided $50\%$ of the radical scavenging activity. Eight different concentrations of gallic acid diluted in methanol (100–1 mg/L) were prepared and used as a positive control. The TEAC of the wheat husk samples was estimated by the ABTS radical scavenging assay, according to Pierre et al. [ 2015] with slight modifications [24]. Briefly, 0.4 mL of wheat husk extract was added with 3.6 mL of ABTS radical solution and left in the dark for 15 min. ABTS radical decolorization was evaluated by measuring the absorbance at 734 nm. The results were expressed as milligrams of Trolox equivalent (TE) per g of wheat husk (mg TE/g) by a calibration curve ranging from 0.5 to 200 mg/TE (R2 = 0.9963). ## 2.5.5. Trypan Blue Assay The mouse microglia cell line (BV2) was seeded in DMEM containing $5\%$ FBS, $1\%$ L-glutamine, $1\%$ penicillin-streptomycin, $1\%$ non-essential amino acids, and $1\%$ sodium pyruvate at 37 °C in a humidified atmosphere with $5\%$ CO2. BV2 cells were arrayed in 48 wells (30,000 cells/400 µL) [25]. Treatment with the extracts obtained from the wheat husk (10, 50, and 100 ng/mL) was added, and the cells were incubated for 24 h at $T = 37$ °C. After 24 h, the cells were detached with Trypsin-EDTA 1× and counted through Burker’s chamber in Trypan Blue solution (1:1). Both live and dead cells were counted. ## 2.5.6. Real-Time Quantitative PCR Analysis BV-2 cells were seeded onto 6-well plates at a density of 106/well in 1 mL of DMEM. After a 45 min pre-treatment with extracts obtained from husks at concentrations of 10 and 100 ng/mL, cells were added with LPS 1 ng/mL and incubated at 37 °C for 4 h. The inflammatory stimulus was added with LPS 1 ng/mL for 4 h. After 4 h, cells were detached in 700 µL of Qiazol Lysis Reagent and stored at −80 °C. RNA was extracted and quantified from BV-2 cells. The cDNA was originated using the High-Capacity cDNA Reverse Transcription Kit. Quantitative real-time PCR (qPCR) was performed for each sample in triplicate on an Applied Biosystems 7900HT fast real-time PCR using the Power SYBR® Green PCR Master Mix. Primers for real-time PCR amplification were designed with UCSC GENOME BROWSER (http://genome.cse.ucsc.edu/ accessed on 11 January 2023); University of California, Santa Cruz) (Table 1). The comparative threshold cycle (CT) method was used to analyze the real-time PCR data. The target quantity, normalized with respect to the endogenous β-actin primer reference (ΔCT) and relative to the untreated control calibrator (ΔΔCT), was calculated using equation 2−ΔΔCT [25]. ## 2.6. Sustainability Evaluation of Wheat By-Products by Life Cycle Assessment (LCA) The study evaluated the sustainability assessment of wheat by-products, which account for 17–$20\%$ of the total production, through the application of the life cycle assessment methodology in accordance with ISO 14040, 2006 and ISO 14044, 2006 [1]. SimaPro v9.2.2., software was used for the evaluation of environmental impacts. ## 2.6.1. Goal and Scope Definition The goal of the study is to assess the environmental impacts associated with durum wheat by-products. In particular, the functional unit (FU) was identified as the production of 180 g of processing by-products (including husks) resulting from the milling process of 1 kg of wheat, thus making the assumption that this by-product corresponds to about $18\%$ of the total wheat production according to a cradle-to-gate approach, as shown in Figure 3. The wheat husk system production includes the upstream process activities (i.e., seedling, water for irrigation, fuels, fertilization, etc.) for the agricultural production of 1 kg of wheat. Primary information was obtained through face-to-face interviews with farmers and mill owners based on the in-field activities for agricultural and milling phases. The milling phase includes the stages from cleaning to polishing, thus considering the use of water and electricity. In addition, secondary source data from the literature were used for the energy-production phase. In the first scenario, wheat husks are destined to landfill disposal as a unitary quantity of 180 g used for zootechnical application and soil conditioners. Otherwise, the wheat husks extraction scenario considered both the extraction and processing phase of wheat husks. ## 2.6.2. Life Cycle Inventory (LCI) The unit processes of each phase were considered as well as the inputs from the agricultural phase to alternative scenarios for the extraction of bioactive compounds, thus considering the milling by-products generated. In particular, the inputs referred to a national average durum wheat production in the year 2020, thus focusing on the milling process of 1 kg of wheat; meanwhile, for the recovery of bioactive compounds from wheat milling by-products (husk), the inputs referred to the extraction process carried out on both samples, i.e., WH1 and WH2, analyzed in the study. Inputs for the wheat husk system production and recycling scenarios are shown in Table 2. All data referred to the same FU of 180 g of wheat husk resulting from the milling process of 1 kg of wheat, thus making the assumption that this by-product corresponds to about $18\%$ of the total wheat production based on production estimations at national level [6]. LCI calculations were performed to model inputs for equipment, solvents, and electricity (i.e., ultrasonic bath, centrifugation, etc.) used during the extraction process. About 180 g of wheat husk was extracted for the recovery of bioactive compounds conventionally by using the hydroalcoholic solution of EtOH:H2O (80:20, v/v) as extractant. EcoInvent version 3.8, Agribalyse v3.0.1, and World Food LCA Database (WFLDB) databases were used to calculate the environmental impacts of the extraction and processing phase of the wheat husks. ## 2.6.3. Scenario Analysis To highlight the amount of CO2 avoided as a result of the possible reuse of the by-product and their environmental compatibility, two scenarios were proposed: (S1) relating to disposal of the by-product in the landfill and (S2) concerning the valorization of wheat by-products through the recovery of bioactive compounds. The scenarios were subsequently compared through carbon footprint (CF) calculation. CF was calculated based on the LCI and LCIA results. CF is a measure expressing the greenhouse gas emissions (GHGs) caused by a product, service, or process. In accordance with the Kyoto protocol, CF is expressed in kilograms of CO2 equivalent (kg of CO2 eq), and it was calculated according to Forster et al. [ 2007] [26] based on Equation [1]: Carbon footprint = ∑G.G.i × ki[1] where G.G.i represents the amount of GHGs produced, and ki corresponds to the CO2-equivalent coefficient for that gas. The CF was obtained employing the Green Gas Protocol V1.03/CO2 eq (kg) method (GHGP 2020) by using SimaPro v.9.2.2. software. ## 2.7. Statistical Analysis The data were obtained from the analysis of three replicates and were expressed as mean ± standard deviation (SD) from experiments. The significance of differences between the extracts was tested using a one-way analysis of variance (ANOVA) with $p \leq 0.05.$ After ANOVA, multiple comparison tests were performed for statistically significant variables, using Dann’s post hoc test (homogeneity of variance was assumed) at the level of $p \leq 0.05.$ Statistical analyses were performed using unpaired Student’s t-test (GraphPad Software Inc., San Diego, CA, USA). ## 3.1. Quality and Safety of Wheat Husks by Quantitative Determination of Biogenic Amines (BAs) The content of eight BAs was evaluated in raw wheat husks by high-performance liquid chromatography with fluorescence detection (HPLC-FD). The analyzed wheat husk samples showed a variable content ($p \leq 0.05$) of total biogenic amines (Table 3). In particular, WH2 samples showed a higher total BAs content (43.26 mg/100 g) than WH1 (35.66 mg/100 g) (p-value < 0.05). Thus, for both cases, SER is the most abundant biogenic amine, accounting for about $35\%$ of the total content. This was in line with the literature results, which found an SER content ranging from 5.2 to 22 mg/100 g dw in wheat by-products [27]. Different authors reported that the occurrence of SER in plant-origin foods was affected by plant variety, degree of microbiologic contamination, and specific conditions to proliferate cells (pH, temperature, access to oxygen, etc.) [ 28]. Furthermore, it is well established that SER demonstrates positive effects on human health (psychoactive effects, vasoconstrictive properties, etc.); therefore, wheat husks relatively rich in SER could be of interest both to consumers and the food industry. It is relevant to underline BAs’ higher amounts of PUT, SPD, and SPM, detected in the highest amount in WH1 (1.81 ± 0.19 mg/100 g dw; 7.86 ± 0.81 mg/100 g dw; and 4.61 ± 0.47 mg/100 g dw, respectively). According to different authors [29], polyamines putrescine as well as spermine and spermidine are ubiquitous and endogenous in all plant-origin foods, and they have a relevant role in increasing food shelf life, thus representing a food-spoilage index [28,29]. In particular, the literature results highlighted PUT (0–8.6 mg/100 g), SPD (0–33 mg/100 g), SPM (0–4 mg/100 g), and SER (0–13 m/100 g) as the major BAs detected in durum wheat cultivars; their content in the outer layers of the grain (i.e., bran) is about 40–$60\%$ higher than in milling products such as whole and white flours [27]. Furthermore, the presence of exogenous monoamines CAD, HIS, and TYR detected in WH2 samples at a concentration of 2.1 ± 0.11 mg/100 g dw, 6.60 ± 0.79 mg/100 g dw, and 1.12 ± 0.09 mg/100 g dw, respectively, may be related to the storage and processing conditions of husks, thus representing a quality process marker. BAs such as HIS, CAD, and TYR are responsible for food-born illnesses such as Scombroid syndrome, cheese reaction, and food allergies, even if present at small concentrations [27]. The present results showed a variable content of these BAs among wheat milling by-products, but they were not as high as amounts reported for meat, fish, alcoholic beverages, cheese, and fermented vegetables (up to 1000–2000 mg/kg), thus representing the most involved foods in toxicological or allergic reactions. These trends are in line with the literature results reporting low BAs contents in cereals in comparison with the above-mentioned foods [27,28,29]. However, the biogenic amines quality index (BAQI) showed that the BAs amounts detected in wheat husk samples do not pose a health risk to the consumer since they presented values <10 mg/100 g [20]. ## 3.2. Phenolic and Antioxidant Properties of “Senatore Cappelli” Durum Wheat Husks During the wheat milling process, huge amounts of by-products are generated, accounting for 17–$20\%$ (w/w) of the total raw wheat. They generally consist of husks, outer germ layers, and bran, which still have biological and phytochemical properties such as polyphenols and antioxidants that could enhance health-promoting effects. The phenolic and antioxidant profile of SC wheat husks was evaluated by means of spectrophotometric assays, as shown in Table 4. Results highlighted a variable content of phenolic and antioxidant compounds among husk samples depending on the area of origin. In particular, WH2 resulted in the highest total phenolic content (351.14 ± 5.91 mg/100 g dw), total flavonoids (156.90 ± 2.31 mg RE/100 g dw), and antioxidant activity considering the ABTS assay (37.84 ± 4.69 mg TE/100 g dw). Meanwhile, wheat husk samples from the Val d’Orcia wheat chain (WH1) showed a $40\%$ ($p \leq 0.05$) lower phenolic and antioxidant potential than WH from the Puglia chain. However, the results of the DPPH assay differed from those of the ABTS assay; this may be probably attributed to the different types of radical agents used. The DPPH reagent, in fact, is a stable nitrogen radical that interacts mainly with peroxide radicals involved in lipid peroxidation, whereas the ABTS reacts with both hydrophilic and lipophilic radicals. Therefore, the reactivity of DPPH is only limited to the lipophilic fraction [19]. In particular, the different content of phenolic and antioxidants among wheat husk samples analyzed may be attributable to the varying pedo-climatic characteristics for the wheat’s area of origin despite belonging to the same cultivar (“Senatore Cappelli”). To this purpose, Dinelli et al. [ 2013] affirmed that bioactive compound content may greatly vary depending on durum wheat genotype as well as differences in genetic and agricultural crop management [3]. In these regards, it is well established that the biosynthesis and accumulation of phenolic compounds during kernel development is greatly influenced by the wheat variety, environmental conditions, as well as abiotic and biotic stresses [30]. Considering TPC values, most literature data reported similar trends in durum wheat by-products in terms of free soluble fraction, which accounts for 50–$75\%$ of the total amount of phenolics, thus highlighting flavonoids and phenolic acids as the most abundant in wheat milling by-products [31]. In these regards, considering TPC, values ranging from 80.90–610.49 mg GAE/g were found in durum wheat by-products [32] as well as values that ranged within 10.84–26.73 mg TE/g and 3.61–1194.8 mg/mL for ABTS assay [33]. Nevertheless, antioxidant activity by DPPH assay showed different trends strictly depending on the chemical composition of cereals; in particular, this result may be due to the lipophilic fraction contained in the wheat by-product, which may have contributed to the extract’s activity during the extraction process [33]. ## 3.3. Cytotoxicity of “Senatore Cappelli” Durum Wheat Husks in BV2 Cells Microglia play a key role in driving neuroinflammation, a mechanism underlying several neurodegenerative diseases; therefore, they represent a suitable model for investigating the anti-inflammatory and antioxidant activity of plant-derived bioactive molecules. Our results dealing with the cytotoxic activity of husk extracts on microglial cell cultures are depicted in Figure 4. The percentage of live cells in WH1 in the total number of cells at the concentration of 100 ng/mL is $92.6\%$; at the concentration of 50 ng/mL, it is $77.9\%$ and at the concentration of 10 ng/mL is $93.2\%$. The percentage of live cells in the untreated cells (CTRL) out of the total corresponds to $78.64\%$. The percentage of live cells for WH2 is $90.2\%$ at the concentration of 100 ng/mL; at 50 ng/mL, the percentage of live cells is $86.2\%$ and at 10 ng/mL is $91.7\%$. The percentage of live cell control is $85.7\%$. Extracts obtained from husk, from the two different supply chains mentioned above, added to the in vitro cultures showed no toxicity at the concentrations of 100, 50, and 10 ng/mL. Indeed, BV2 cells treated with the husk extracts at 100 and 10 ng/mL showed a significantly higher proliferation compared to untreated cells (CTRL). An increase in dead cells was observed in WH1 compared to controls, although there was an increase in cell number. At 50 ng/mL concentration, no significant differences with untreated cells were recorded in both WH1 and WH2. ## 3.4. M1 mRNA Markers Expression To evaluate the possible pro-inflammatory effect of the chaff-derived extracts of the two aforementioned dies, we analyzed the mRNAs of key M1 markers in BV2 cells. Results for mRNA expression of iNOS, COX2 and CCL2 mRNAs, and M1 markers highlighted a significant increase following LPS treatment compared with untreated cells (CTRL) (Figure 5). Pretreatment with husk-derived extracts from both strands (WH1 and WH2) did not modulate iNOS, COX2, and CCL2 mRNAs expression. Husk extracts are devoid of any pro-inflammatory activity because they do not increase the expression of M1 markers. Several randomized control trials have shown that the intake of whole grains compared to refined grains reduces the expression of pro-inflammatory cytokines such as IL-6 and TNF-alpha and a causes a reduction in serum levels of C-reactive protein in obese patients or patients with metabolic syndrome [34]. In a 2016 study, obese patients under 50 years of age experienced an improvement in diastolic blood pressure after a whole-grain diet [35]. Considering the high mortality risk associated with chronic diseases such as cardiovascular disease, low-calorie diets based on whole-grain foods may reduce this risk [36]. ## 3.5. M2 mRNA Markers Expression To evaluate the M2 status of BV-2 cells here, we performed RT-qPCR analysis and assessed mRNAs expression of Arginase-1 (Arg-1), which is associated with repair mechanisms [16]; and CD206 and Chil3 (Figure 6). Expression of ARG-1 mRNA was induced by the addition of extracts obtained from WH1 and WH2 husk at both 100 and 10 ng/mL concentrations and compared with CTRL. Pretreatment with 10 ng/mL of WH2 husk extracts significantly increased ARG-1 mRNA expression also after the addition of LPS. The mRNA expression of CD206 increased significantly compared with CTRL after addition of WH1 100 and 10 ng/mL and WH2 10 ng/mL. In both chains, ARG-1 expression increased in cells pretreated with the extracts obtained from Pula in the presence of LPS. Chil3 mRNA expression was induced by the extracts alone in both chains. In the presence of LPS, the WH2 extracts at the concentrations of 100 and 10 ng/mL and WH1 extracts at the concentration of 100 ng/mL stimulated Chil3 mRNA expression. Therefore, the extracts obtained from husk by themselves do not induce any inflammatory effect; instead, they stimulate mRNA synthesis of M2 markers, reverting microglia toward an anti-inflammatory phenotype. Neuroinflammation and oxidative stress are hallmarks of neurodegeneration, contributing to the etiopathogenesis of diseases such as AD, PD, and depression. There are still no approaches that resolve or prevent the onset of these diseases, so the identification of preventive therapeutic approaches seems urgent [37]. The ancient variety “Senatore Cappelli” has more polyphenolic components characterized by nutraceutical properties than modern varieties [38]. Polyphenols introduced through the diet manage to cross the blood–brain barrier by modulating microglia cell-mediated inflammation in neurodegenerative diseases [39]. ## 3.6. Antioxidant Activity of SC Durum Wheat Husk Neuroinflammation underlies many neurodegenerative diseases that are incurable to date; in light of these observations, research is focusing on new therapeutic targets. The transcription factor Nrf2 regulates the expression of antioxidant genes that assist anti-inflammatory mechanisms [40]. Therefore, we evaluated by RT-PCR the mRNAs expression of NRF2 and SOD1 (superoxide dismutase1), which are enzymes protecting against oxidative stress [41]; they were significantly decreased in the presence of LPS compared with CTRL (Figure 7). WH1 and WH2 extracts stimulated NRF2 and SOD1 mRNA expression at both 100 and 10 ng/mL, both in the absence or in the presence of LPS. NRF2 signaling pathways could become a promising therapeutic target. Extracts of “Senatore Cappelli” wheat derivatives restore NRF2 expression related to the upregulation of SOD1, an antioxidant gene. It is known that NRF2 pathways counteract ROS production and inflammation in neurodegenerative disorders, suggesting that stimulation of NRF2 factor could play a key role as a therapeutic approach [11]. These results are in accordance with a study from 2016, where different in vitro assays showed that wheat chaff with ultrasound or hydrothermal or alkali pretreatments for enzymatic conversion showed antioxidant activity correlated with the concentration of reducing sugars [42]. The increase in mortality due to the rising incidence of cardiovascular diseases has focused attention on identifying wheat species with antioxidant and anti-inflammatory properties. Ancient varieties exhibit these characteristics to a greater extent than modern wheat cultivars. Studies on rats fed a diet of ancient wheat showed lower concentrations of reactive oxygen metabolites in plasma compared to rats fed a diet of modern wheat [43]. ## 3.7. Life Cycle Assessment of Wheat By-Products Life cycle assessment represents a well-established tool to measure the basis of environmental sustainability of a product’s or process’s life cycle across its entire value chain from extraction of raw materials to its disposal or recycling [11]. The inventory results were analyzed in order to calculate the environmental impacts for each impact category, namely GW, global warming; LU, land use; TEC, terrestrial ecotoxicity; TA, terrestrial acidification; FR, fossil resource scarcity; and WC, water consumption, as indicated in Table 5. The ReCiPe 2016 Midpoint (H) V1.05 method was used for the impact calculations. In the wheat production system, the results were calculated for agricultural production and the milling process of 1 kg of wheat and relative impacts associated with milling wheat by-products, representing nearly $18\%$ of the total production. The milling process has the least impact in all investigated environmental categories. The results showed that wheat production greatly impacts the environment, showing high values for GW (2.39 × 10−1 kg CO2 eq), LU (1.2 m2a crop eq), TA (2.64 × 10−3 kg SO2 eq), and WC (1.02 × 10−2 m3). Out of these values, the wheat by-products account for 23–$34\%$ of the total impacts related to the entire production process, as highlighted in Figure 8, where the results were characterized and expressed as a relative impact, where the scenario with the highest value in the impact category is set as the reference value [100], and the other is calculated accordingly. Principally, these impacts are mainly attributable to the emissions associated with using fertilizers from crop fields and fuel consumption for in-field operations, which contribute to $86\%$ of the environmental impacts. According to the reviewed literature, most LCA studies on agro-food production mainly highlighted the energy-intensive production of fertilizers and the large amount applied to crop fields as the first factor responsible for climate-altering emissions [11,44,45,46], contributing most to the acidification, eutrophication, and respiratory effect categories [47]. In addition, it is worth noting that wheat production also greatly impacts human non-carcinogenic toxicity (HNCT), thus generating an amount of 4.59 × 10−1 kg 1.4-DCB, from which wheat husks contribute $24\%$ of the total impacts related to human toxicity. In particular, the environmental problems causing human toxicity are mainly linked to the release of heavy metals (i.e., nickel and arsenic) and polycyclic aromatic hydrocarbons into the air [48]. ## Carbon Footprint of Alternative Scenarios for Wheat Husk System Production Considering the environmental analysis of wheat production, it is worth noting that $25.6\%$ of the environmental impacts in the GW category are associated with husk. As most studies highlighted, agricultural by-products are often discharged, thus creating the main disposal and environmental issue for the wheat-processing industry. In these regards, the possibility of evaluating potential scenarios for mitigating CO2 emissions related to wheat by-products could represent a strategic option from an environmental perspective. In these regards, evaluating the potential CO2 emissions associated with the landfill disposal of 180 g of husk (obtained from the milling process of 1 kg of wheat), which is commonly used for animal breeding or soil conditioning purposes [10], it can be seen that it generates 0.073 kg CO2 eq, corresponding to $19.8\%$, of the total emissions generated. In the case of recycling this by-product for the extraction of high-value-added bioactive compounds, about 0.054 kg CO2 eq would be generated, accounting for about $14.5\%$ of the total GHGs. Therefore, considering the possible valorization of the husk as an alternative to landfill disposal results in an avoided CO2 amount of 0.0193 kg CO2 eq, as shown in Figure 9. Different studies in the literature focused on applications possibilities for wheat milling by-products, thus focusing on their recycling processes for renewable energy production [7,44,49], biofuel and bio-gasification processes [9], as well as for the production of feedstock to produce various products including biosurfactants [50]. However, by comparing this value to the total production of durum wheat in Italy, which today amounts to about 3.5 million tons/year produced in 2022 [51], it is worth noting the possibility of avoiding nearly about 12,160 kg CO2 eq per year. ## 4. Conclusions The present work was aimed at valorizing two wheat milling husks of the ancient “Senatore Cappelli” cultivar by the recovery of bioactive compounds, thus evaluating their phenolic and antioxidant potentials and nutraceutical activity. To this purpose, a multi-methodological approach was carried out to assess both the quality and safety of raw matrices as well as the antioxidant and nutraceutical properties of wheat husk extracts. By means of HPLC-FD analyses, wheat husk samples analyzed revealed a higher content of SER amounting to $35\%$ of the total BAs and were confirmed to occur at BAQI values <10 mg/100 g, thus denoting no loss in the quality of analyzed samples. In addition, spectrophotometric assays showed a significant variable content in the phenolic (189.71–351.14 mg GAE/100 g) and antioxidant compounds (31.23–37.84 mg TE/100 g) within the wheat husk samples, according to the different cultivar areas of origin and the related pedoclimatic characteristics, which influence the different distribution in the content of bioactive compounds. Considering the growing interest in the renewability of food resources, this multi-methodological study builds on the potential neuroprotective role in terms of reduction of neuroinflammation and oxidative stress of waste products from sustainable agricultural supply chains. Based on in vitro assays on BV2 cells, the two wheat husks of the ancient cultivar “Senatore Cappelli” are non-cytotoxic and increased mRNA expression of anti-inflammatory markers such as ARG-1, CD206, and Chil3. They also stimulated the expression of genes involved in the antioxidant system. Moreover, through the application of LCA methodology, it was possible to highlight that the impacts associated with the disposal of wheat milling by-products account for approximately $9\%$ of total wheat production. Nevertheless, the extraction of high-value-added compounds from the wheat husk can mitigate environmental and health impacts, thereby inducing $0.41\%$ CO2 savings per year (12,160 kg CO2 eq) compared to the overall wheat-production chain. In this framework, it could be worth putting forth greater efforts in terms of energy efficiency and water productivity, thus ensuring a rationalized and sustainable production by a unit of input. This will result in long-term environmental benefits in terms of resource saving and reduced environmental pollution, which could also affect human health. To improve the environmental performance of wheat by-products, we mainly focused on the recovery of bioactive compounds by conventional extraction methods using hydroalcoholic solvents. 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--- title: 'Cutaneous Perfusion Dynamics of the Lower Abdomen in Healthy Normal Weight, Overweight and Obese Women: Methods Development Using Infrared Thermography with Applications for Future Wound Management after Caesarean Section' authors: - Charmaine Childs - Harriet Nwaizu - Elizabeth Bullivant - Jon Willmott - Matthew Davies - Karen Ousey - Hora Soltani - Richard Jacques journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048797 doi: 10.3390/ijerph20065100 license: CC BY 4.0 --- # Cutaneous Perfusion Dynamics of the Lower Abdomen in Healthy Normal Weight, Overweight and Obese Women: Methods Development Using Infrared Thermography with Applications for Future Wound Management after Caesarean Section ## Abstract Background: Evidence has shown an association between obesity and an increased risk of wound infection after caesarean section. This study was designed to examine if abdominal subcutaneous adiposity impacts upon cutaneous perfusion dynamics. Methods: Mild cool challenge, followed by real-time video thermography, was developed to map the appearance of abdominal ‘hot spots’. Correspondence of marked ‘spots’ with audible Doppler and colour and power Doppler ultrasound was performed. Results: 60 healthy, afebrile, women (20–68 years; BMI 18.5–44 kg/m2) were recruited. Hot spot appearance consistently corresponded with audible Doppler sounds. Colour and power Doppler ultrasound revealed vessels at depths of 3–22 mm. No statistically significant interactions for BMI, abdominal circumference or environmental parameters were observed for hot spot count. The temperature of cold stimulus was significant for effects on spot count, but only for the first minute ($$p \leq 0.001$$). Thereafter, effects on spot numbers were not significant. Conclusions: Cutaneous ‘perforator’ mapping of the abdomen (via hot spot appearance) in healthy women, as a potential and future method for risk of perfusion-dependent wound healing complications, reveals that bedside mapping of skin perfusion is feasible over a short interval. Hot spot number was not influenced by BMI or indicators of central fat distribution (abdominal circumference) indicating variability in an individual’s vascular anatomy. This study provides the underpinning methodology for personalised perfusion assessment after incisional surgery which may be a more reliable indicator of potential healing complications than body habitus as is currently the norm. ## 1. Introduction As a major surgical procedure in women of child-bearing years [1], caesarean section (CS) is not without complications [2]. In the post-operative period, slow healing and wound infection increases morbidity [3] and in rare cases, life-threatening infections [4]. One of the major indications for CS birth is obesity [5], a health risk that also increases the incidence of infection at the incision site; surgical site infection (SSI) [6,7]. Of the factors that predispose one to SSI, recent studies report an association with temperature. Differences in the thermal map, produced using infrared thermography (IRT) of incisional wound, during the first days and weeks after CS, showed temperature differences between healthy and wound sites exceeding 2 °C in women with established wound infection [8]. More specifically, it was low temperature ‘cold spots’ along and around the wound that emerged as feature signatures of later wound infection. Subsequent studies [6] confirmed a significant association between low abdominal temperature and wound outcomes; the lower the temperature the higher the odds of SSI. For example, low abdominal temperature at post-operative day 2 made a substantive, and by day 7 a significant (three-fold), contribution to increased risk of SSI. As shown by Savastano [9], the high fat content of adiposity and increase in tissue insulation results in lower abdominal skin temperatures compared with normal weight individuals, a feature also observed by Siah and Childs [10] in a healthy SE Asian population of participants. In health, changes in skin temperature are primarily dependent upon oscillations in skin blood flow adjusting cutaneous perfusion [11]. Under the control of the efferent limb of the thermoregulatory centre, variation in skin temperature is due to heat flux from body core to the environment. In effect, skin perfusion, and thus temperature, is seldom uniform. When visualised using specialised thermal cameras, skin appears as a thermal ‘mosaic’ [12]. The interplay between skin perfusion and skin temperature can also be a direct consequence of systemic disease. In critically ill patients, for example, the effect of circulatory shock can result in cutaneous hypoperfusion, especially at the extremities, worsening healing rates in pre-existing chronic wounds and ulcers [13]. Here, evidence for the effect of poor skin perfusion and concomitant effects on skin integrity can be seen in the most severely ill where perfusion ‘deprivation’, indirectly measured by mean arterial pressure (MAP of <80 mmHg), is linked to wound deterioration [14]. The underpinning dependence of wound healing on cutaneous perfusion reinforces the significance of poor skin perfusion in prolonged or stalled wound healing [15]. Whilst MAP is a broad and useful guide to risk of poor healing in clinical practice, alternative techniques exist which measure the physiological state of tissue attributed to blood flow [15]. In view of concerns and vulnerability of obese patients to surgical site infection (SSI) [16,17] more information is warranted on the relationship between adiposity, skin temperature and perfusion in obese people undergoing surgery because recovery occurs only on complete healing of the skin and wound. To achieve this, skin must be viable. It must receive an optimum supply of blood and nutrients and this, in health, is achieved by perfusion of blood to skin via abundant vascular networks arising from deep source vessels, their angiosome territories [18], perforators and branches which supply the subdermal-plexus. In the context of lower abdominal surgery, cutaneous perfusion in the region of a Pfannenstiel incision [19] is via source vessels of deep systems, primarily deep inferior epigastric artery (DIEA) and its perforating branches [18,20]. Perforators of DIEA course through rectus musculature and overlying fascia to reach the subdermal plexus, the main supply to the skin [21]. Abdominal skin blood supply is also from the superficial inferior epigastric artery (SIEA) [22]. Arising from the femoral artery, approximately 1 cm below the inguinal ligament, the SIEA travels superficially and superiorly between abdominal fascia towards the umbilicus [23]. Unlike the DIEA, the SIEA is not ‘classically’ considered a perforator, due to a lack of uniformity of branches lying deep to Scarpa’s fascia [24]. However, SIEA does have a deep origin and at some point, one or more of its branches pierce Scarpa’s fascia [25] to follow a fascio-cutaneous course. Thus, in studying cutaneous perfusion, both deep and superficial systems play a role in influencing cutaneous perfusion to lower abdominal skin regions and ultimately temperature patterns across the abdomen. The objective of this study was to explore whether a relationship exists between BMI, and cutaneous perfusion by mapping the number of abdominal perforators. Dynamic infrared thermography (DIRT) was the method under test in response to mild cold challenge. The first step, undertaken in healthy women, was to test feasibility and acceptability of the method of induction of thermal challenge and to verify perforator location as evidenced by cutaneous ‘hot spots’ on DIRT and to verify DIRT methods against three independent, non-invasive imaging modalities; hand-held vascular Doppler, power and colour Doppler. ## 2.1. Study Design A prospective imaging methods development study with intervention feasibility was conducted in three phases over a period of 10 months. Phase 1, detector calibration and cold challenge method testing; phase II, perforator location and number across different BMI categories using DIRT, phase III, correspondence and validation of DIRT identified cutaneous perfusion (hot spot location) with independent measures of skin perfusion/blood flow. Sheffield Hallam University institutional ethical approval for studies on human participants was obtained before research on healthy subjects commenced. All equipment used was safety checked and risk assessed. All investigators and participants followed NHS COVID-19 guidelines for infection prevention and control. ## 2.2. Phase I: IR Detector Calibration Thermography was undertaken using a FLIR Systems A655sc (640 × 480 pixel resolution), uncooled, science grade, microbolometer with 25° lens and with thermal sensitivity <30 mK. Images and data analysis were via ethernet streaming (30 Hz) to a laptop PC running FLIR Research IR Max software V4.40.11.35 (64 bit). The IR detector was calibrated, before study start, and at mid-recruitment point, using a black body source (Ametek-Land, Dronfield, UK) (Figure 1). IR detector readings were compared to a certified (UKAS, UK) independent type 100 Ω platinum thermometer (PRT100, ISOTECH, Skelmersdale, UK) placed in situ within the black body. Over a period of 4 h, and at an incremental temperature span of 10 °C (27.5 °C to 37.5 °C), the difference between IR detector readings and PRT ranged from 0.20 to 0.5 °C (median 0.41 °C). ## Selecting a Suitable Method of Mild Thermal (Cold) Challenge Six different methods were tested for their performance and acceptability to lower skin temperature within a period of 2–5 min; metal floor standing fan, plastic lightweight fan, large ‘Magic Gel’™ cold compress (38 × 28 cm), wet wipes (alcohol containing/no alcohol), operating table torso pad (Anetic Aid, Baildon West Yorkshire, UK) and plastic (with sealable tap) inner packaging of a 10 Litre ‘bag in box’ system (Vigo Ltd., Honiton, Devon, UK) filled with 5.4 L of tap water. With the exception of the plastic insert from Vigo’s ‘bag in a box’, none of the methods tested, be it forced convection (fan), local conduction (gel pads), latent heat of vaporization (wet wipes) proved practical or reliable in lowering skin temperature for more than a few seconds. Skin rewarmed rapidly on removal of the material. By contrast, the sealed tap water-filled plastic container from the ‘Bag in a Box’ packaging proved robust, leakproof and maintained a consistent water temperature, sufficient to lower skin temperature by approximately 10 °C. In the summer months the bag was placed in a larder refrigerator for not more than 15 min to lower water temperature before use. ## Thermal Challenge and Dynamic Infrared Thermography Mapping of Cutaneous ‘Perforators’ Environmental monitoring: Ambient conditions for air temperature (°C), relative humidity (RH%) and air velocity (m·s−1) were recorded every 10 min with a weather meter (Kestrel 3000, Richard Paul Russell Ltd., Hampshire UK) positioned at the site of the tripod. The infrared detector, mounted on a tripod at 45–50 degrees to the abdomen, was stabilised using a 1 Kg weight suspended centrally (Figure 2) and connected via ethernet cable to a laptop personal computer. The tripod legs were aligned to ensure that the thermal detector was positioned towards the abdomen to provide a central field of view (FOV). Protocol for DIRT and perforator mapping of healthy women: Participants: Healthy women over the age of 18 years were eligible for recruitment irrespective of parity (nulliparous women were eligible) or past mode of delivery (vaginal birth or caesarean section). Participants were recruited via study posters, social media, word of mouth and networking. Purposive sampling, with a target of 60 participants, was chosen to reflect a cross-section of healthy women across three BMI categories, healthy (normal) weight (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2) and obese (≥30 kg/m2) (https://cks.nice.org.uk/topics/obesity/diagnosis/identification-classification/, 14 February 2023). Women attended University equipped clinical consultation rooms under standard ambient conditions during the period from November 2021 to July 2022. Air conditioning was not used. On arrival, study activities were reviewed and signed informed consent obtained. Demographic information, obstetric history and pre-existing medical conditions were documented along with body temperature measured at the tympanum (Thermoscan, (Model LF 40, Braun, Lausanne, Switzerland), weight, height and abdominal circumference at the level of the umbilicus. Ambient conditions, including temperature, relative humidity (RH%) and air velocity (m·s−1) were recorded throughout the 50 min of study. Lights were turned off and blinds closed. Lying supine, the abdomen was exposed, from the level of the umbilicus to the bikini line, for a period of approximately 5 min whereby women were reassured of the cool challenge procedure and allowed to settle and feel comfortable. One sheet was placed to cover the upper abdomen and chest and a second to cover the pubic region at the level of the groin. This was followed by a further 10 min of timed cooling. The objective was to achieve environmental acclimation following guidelines of the International Association of Certified Thermographers [2020] (https://iactthermography.org/standards/medical-infrared-imaging/, 14 February 2023). Subjects were requested to lie still throughout, with arms and hands at their sides or over the chest. IR image capture: IR parameter settings: Emissivity was set to 0.98, distance to 60 cm, with reflective ambient temperature of 20 °C and ‘greyscale’ image palette selected. At the end of 15 min acclimation period (5 min + 10 min), a digital image of the abdomen was taken, along with baseline static IR image (Figure 3). A ROI was drawn to cover the central abdominal area from which data to obtain mean, maximum and minimum temperature vales within the boundary was acquired. From the ROI data, an isotherm feature (selected red to contrast within the greyscale image) was set (a span of 3 °C below the highest measured temperature within the ROI before cooling). This feature makes for better contrast and identification of hot spots (temperature recovery) as they appear. Video recording was set to 10 min at 30 Hz and commenced immediately on removal of cold challenge. Cold Challenge: Two minutes of mild cold challenge was applied to the lower abdomen, with tap water temperature at 18–20 °C. The bag was ‘rolled’ gently over the abdomen, redistributing water to achieve a uniform temperature reduction. Immediately upon removal, video recording commenced. After removal of cold challenge, a lowering of skin temperature was apparent (Figure 3B). The appearance of a ‘hot spot’ appeared against the greyscale background and location was marked on the skin with metallic, non-permanent, marker pens. On completion of the video, recordings were saved for post-processing. Ultrasound gel was then applied over all marked spot locations and a hand-held Doppler probe (8 Mz) (Huntleigh, Cardiff UK) applied to listen for arterial sounds and to view the concomitant waveform (CC and HN). ## 2.5. Correspondence between DIRT Identified ‘Perforators’, Vascular Doppler and Colour and Power Doppler In a sub-group of participants, an in-depth Doppler ultrasound examination (Aplio 300, Toshiba Systems, CA, USA) was performed by an experienced sonographer (EB). Participants were positioned supine, and with an empty urinary bladder, on an ultrasound scanning couch. A 12 MHz linear probe was used to perform the ultrasound examination, using a pre-set programme of ultrasound settings for consistency and reproducibility of parameters (gain and dynamic range). A starting depth from a skin surface of 20 mm was set. Both colour Doppler and power Doppler were used to assess for vessel presence. A colour box was positioned over the existing gel covered marked hotspot. Colour Doppler imaging was used with the advantage of detecting direction of blood flow [26]. The colour scale was set between 3.8 cm/s and 4.9 cm/s. Power Doppler was also used for its reported higher sensitivity to flow with better edge definition compared to colour Doppler imaging [27]. Each identified vessel was measured with the caliper measurement tool placed at the deepest part of the vessel from the skin surface (Figure 4). Each ultrasound scan was labelled to correspond with the marked spot identified on the thermal image. Data were stored on a secure University network drive. ## 2.6. Statistical Analysis The relationship between the independent variables (age, BMI, ambient temperature, and bag temperature) and the outcome variable (spot count) was investigated over the first 5 min of DIRT recording using Poisson generalised linear mixed effects regression models. A separate model was fit for each of the independent variables with time included as a fixed factor and participant ID included as a random intercept. An interaction term between the fixed factor for time and the independent variable of interest was included to test if the relationship with the outcome changed over time. Negative binomial generalised linear mixed effect regression models were also considered but after inclusion of the random effect for participant ID there was no evidence of overdispersion, and the negative binomial models failed to converge. For each time point the relationship between the independent variables and outcome variable is described as an incidence rate ratio (IRR) per one unit increase in the independent variable (e.g., a one unit increase in BMI) along with a $95\%$ confidence interval (CI) and p-Value. All analysis was conducted using the statistical analysis software R (4.2.1) (https:www. R-project.org/, 14 February 2023). ## 3. Results A total of 60 afebrile healthy women, aged 20–68 (mean 42.3) years, of diverse ethnicity were studied. Abdominal circumference ranged from 65–147 (mean 91.3) cm, body mass index (BMI) 18.5–44.0 (mean 27.4) kg/m2. Over one third ($37\%$) were obese; $50\%$ overweight/obese (Table 1). None of the participants had cardiovascular disease. Two women had type II diabetes and two women smoked. Of 40 parous women recruited, 16 ($42\%$) had given birth by C-section. A total of 15 women ($25\%$) were either menopausal or post-menopausal (Table 1) at the time of study. The environmental temperature of the clinical room was relatively constant during winter to summer months of the study (Table 2). After 15 min acclimatisation, the exposed abdominal temperature ranged from 29.8–34.7 °C. Application of cold challenge (via water cooled plastic ‘bag’) reduced skin temperature by approximately 5–10 °C over a duration of 90–180 min ($$n = 45$$ women cooled for 120 s; $$n = 12$$ cooled for 90 s, $$n = 3$$ cooled for 180 s). During the first 5 min of the 10 min DIRT video recordings, the number of marked hot spots totalled 1112, individual count ranging from 2–51 (mean 18) spots (Figure 5). Hot spot count for 60 participants at the 1 min timepoint: 0–16 (mean 2.8, SD 3.4); at 2 min, 0–26 (mean 6.3, SD 5.7); at 3 min, 1–42 (mean 10.8, 8SD 8.6); at 4 min, 1–51 (mean 14.9 SD 10.5) and at 5 min, 2–51 (mean 18.5 SD 12.0). The major concerns for the methodological approach to thermal challenge were in the interaction between time and independent variables that could influence skin perfusion, as evidenced by hot spot count on DIRT. There was not a statistically significant ($$p \leq 0.122$$) interaction between time and age indicating that the relationship between spot count and age does not change over time. The relationships at each time point were not statistically significant and the incidence rate ratios (IRR) were close to 1 suggesting that there is no real evidence of a relationship between age and spot count. With respect to BMI, the interaction between BMI and age was not statistically significant ($$p \leq 0.954$$) indicating that the relationship between spot count and BMI does not change over time. The relationships at each time point were not statistically significant and the IRRs were close to 1 suggesting that there is no real evidence of a relationship between BMI and spot count. The interaction between BMI and ambient temperature was not statistically significant ($$p \leq 0.913$$) indicating that the relationship between spot count and BMI does not change over time. The relationships at each time point are not statistically significant and again the IRRs are close to 1 suggesting that there is no real evidence of a relationship between ambient temperature and spot count. The interaction between time and bag temperature was statistically significant ($$p \leq 0.001$$) indicating that the relationship between bag temperature and spot count changes over time. At 1 min there was a statistically significant relationship (IRR: 1.240, $95\%$ CI: 1.014 to 1.516, p: 0.036) showing that the spot count rate increases by $24\%$ for each 1 °C increase in bag temperature. However, at 2 min (IRR: 1.101, $95\%$ CI: 0.921 to 1.218, $$p \leq 0.291$$) and 3 min (IRR: 1.042, $95\%$ CI: 0.878 to 1.26, $$p \leq 0.640$$) the rate of increase decreased to $10\%$ and $4\%$, respectively, but these relationships were no longer statistically significant. At 4 min (IRR: 0.983, $95\%$ CI: 0.831 to 1.162, $$p \leq 0.839$$) and 5 min (IRR: 0.971, $95\%$ CI: 0.822 to 1.146, $$p \leq 0.730$$) the point estimates were less than 1 suggesting a decrease in the spot count rate, but the relationships were not statistically significant. ## Correspondence between DIRT Located Hotspots and Independent Measures of Skin Perfusion Of the 60 women studied, audible arterial Doppler sounds were heard at the majority ($98\%$) of marked hot spots during the 10 min DIRT test. For the last 14 participants, Doppler ultrasound (colour and power mode) was used to confirm vessel presence (Figure 4). At first spot appearance vessel depth from skin surface ranged from 3.2–22.1 (median 5.3) mm (T1, Figure 6). For subsequent spot appearances, arbitrarily divided to three periods; (a) within 3 min (T2); (b) >3 min to 6 min; (T3) (c) >6 min to 10 min (T4), vessels corresponding to hot spots appearing 6–10 min after removal of cold challenge, were significantly deeper than those arising within the first 3 min ($$p \leq 0.003$$). ## 4. Discussion Perforator mapping using DIRT techniques has been undertaken extensively in studies where the focus is on flap harvest as well as for monitoring of microvascular insufficiency after flap surgery [28]. Perhaps the most compelling evidence for the use of DIRT has been as a surrogate for blood perfusion in the selection of the most suitable perforator for breast reconstruction with an autologous free flap of the abdomen. Here, knowledge of perforator location and skin perfusion is a key element of successful autologous flap harvest [29,30,31,32,33,34] as well as intraoperative and post-operative monitoring of flap viability. In the context of wound healing problems, especially following abdominoplasty, complications at the lower transverse suture line is a major risk for delayed wound healing linked to poor perfusion consequent upon the undermining of skin and subcutaneous tissue to produce a large skin flap [35]. Extending the role of IRT across surgical specialties, and as a relatively inexpensive, non-invasive technique, interest in its potential to identify microvascular insufficiency has applications in vascular surgery and flow-related obstructive peripheral vascular disease [36] diabetic foot [37,38], and critical limb ischaemia [39]. To our knowledge, as a proof of concept, this is the first study to specifically map abdominal perforator number and location in healthy women using DIRT video imaging. In addition, we have shown the impact of cooling on surface temperature. A reduction in skin temperature of just 8–10 °C is sufficient to allow skin perfusion to provide an excellent endogenous contrast such that the recovery of temperature (as hot spots) is clearly visible to the observer. This is a major advantage for the practical application of DIRT in medicine. The results suggest that DIRT has potential applications for the future as a clinically feasible intervention for post-operative skin and wound perfusion evaluation after CS. Of the physiological factors known to influence skin temperature, the most widely appreciated with potential diagnostic utility in clinical medicine is inflammation and ischaemia. Both are accompanied by changes in local cutaneous blood perfusion and temperature [40]. For example, conditions which lead to high cutaneous blood flow and emitted IR heat include inflammatory skin lesions [41] and the peri-wound regions of chronic ulcers [42]. By contrast, perfusion deficit leading to low skin temperature is a feature of ischaemic wounds [43]. Thermography is usually undertaken in either static or dynamic modes. Although static IRT provides useful information to map skin temperature under ‘steady state’ conditions, it fails to show changes in temperature and, by proxy, perfusion, over time. However, in dynamic mode, IRT provides an indirect technique for monitoring of vascular perfusion following an applied thermal challenge. To achieve a ‘dynamic’ state, a short period whereby an induced thermal challenge produces a modest change (fall or rise) in skin temperature is required. In most circumstances, skin cooling is instigated either by convection [32] evaporation [44,45] or conduction [46,47]. On removal of the challenge, thermal recovery is marked by the appearance of ‘hot spots’; the first ‘spot’ indicating the return of ‘warm’ blood to the skin. This feature of dynamic (or ‘recovery enhanced’ [48]) thermography has been used in flap surgery as an adjunct imaging modality alone, or in conjunction with traditional radiological techniques to locate ‘dominant’ perforators and their branches as they reach subcutaneous tissue. In the planning and monitoring of fascio-cutaneous flaps, DIRT provides a reliable technique for perforator selection [30], a method comparable to ‘gold standard’ computed tomography (CT) angiography [32]. Other imaging modalities also used to confirm DIRT reliability in identifying perfusion include hand-held Doppler ultrasound alone [30] or in combination with CT angiography [34] and indocyanine green (ICG) fluorescence video angiography [49,50,51]. Although several drawbacks exist in the use of conventional radiological techniques with respect to safety (radiation exposure), high cost and user skill, it is clear when comparisons are made between DIRT and the established methods of perfusion dynamics, that DIRT performs well in the early detection of perfusion deficits due to tissue ischaemia. However, little is known of the potential for DIRT as an adjunct to assessment and prognosis of wound healing complications due to stalled healing, infection or ischaemia consequent upon altered skin perfusion, especially in high-risk patients with pre-existing co-morbidities. In this study of healthy women, and using cold water challenge, skin temperature was lowered by an average of 8 °C over a period of 2 min; in essence a moderate stimulus which avoids discomfort. Alongside the cold challenge, dynamic thermography enhances image contrast. In essence, recovery of warm blood to the skin acts as an endogenous contrast ‘agent’. In this study, return of warm blood was observed on video thermography as a pulsating pixel ‘hot spot’, standing out against the cooled skin background. The technique allowed for immediate marking of each ‘spot’ as it appeared and evolved (as a gradually expanding halo around the first appearing pixels) which eventually join up with adjacent hot spots to produce a perforasome [52]; the vascular territory supplied by a single arterial perforator [53]. Throughout the first 5 min of video thermography, a range of hot spot appearances were evident; from as little as 2 to a maximum of 51 (median 16) and with 5 hotspots the most frequent number. By 10 min, the thermal map appearance largely returned to the pre-cooling thermal state; individual hot spots were no longer identifiable, replaced by a near confluent temperature distribution, close to that observed at baseline. It has also been possible to demonstrate that as each hot spot emerged, verification by hand-held Doppler showed that $98\%$ of the marked spots corresponded to an audible arterial sound. Furthermore, confirmation that a hot spot location reflected the site at which underlying direct and indirect [52] vessels were present was shown from the scans obtained using colour and power Doppler ultrasound. This is consistent with reports of Xiao et al. [ 54] when comparing IRT with colour Doppler. Hot spot appearance on IRT had a high degree of consistency. However, whilst hot spots mark the point at which perforators enter the skin- the ‘terminal’ entry point may be direct (travelling vertically) or indirect (travelling horizontally) after piercing the deep fascia. Xiao et al. [ 54] have shown that spot location on thermography when compared to colour Doppler can lead to some deviation in position due to thickness of subcutaneous tissue. Whilst Xiao et al. [ 54] suggest that IRT is particularly useful to detect hot spot location in flap design for those with thin subcutaneous tissue, our experience, at least in terms of using hand-held Doppler to verify vessel presence, was that sounds were heard at marked locations in those with a range of subcutaneous fat thickness as evidenced by BMI. Despite the diversity in BMI of participating women in this study, being overweight or obese (reflected as BMI and abdominal circumference) did not influence hot spot number. This may be due to the limited number of participants at each BMI category, but it may also suggest that vascular perfusion and heat distribution across the skin may be influenced more by vascular anatomy than by thickness of subcutaneous fat depots. For example, 5 min after removal of cold stimulus, the number of marked hot spots in three women with the highest BMI (44.0, 41.74 and 42.76 m2/kg) numbered 30, 6 and 5, respectively, indicating anatomical variability in the number of perforators as they arise from deeper vessels towards the skin surface; a finding reported previously by a number of authors examining the vascular supply of the anterior abdominal wall [19,52,55,56]. For the 15 women who had previously undergone C-section surgery, dissection of adipocutaneous tissue and underlying muscle fascia might be expected to impact on skin perfusion. With the average length of time since surgery (with the exception of one woman) being 8 years (2–19 years) no clear distinction regarding the appearance of hot spots along the vestigial scar was observed.. However, it should be noted that a number of women did report a lack of sensation in the region of the scar suggesting long lasting effects of dissection on skin sensation. Confirmation of vessel depth at the hot spot sites was made using colour and power Doppler ultrasound. Vessel presence and flow direction was observed at each tested hot spot location, albeit with varying depths from skin surface. Whilst hot spot count per se appears unrelated to subcutaneous adiposity (BMI and abdominal circumference), in the absence of skin fold thickness it is, as yet, unclear whether vessel depth influences the speed at which hot spots appear. However, there is some evidence from the data that first appearing hot spots tended to be closer to the skin surface than those hot spots that emerged later. Therefore, whilst subcutaneous thickness did not appear to influence hot spot count, it may have a relationship with speed of recovery of hot spots. As a potential method for future SSI risk stratification of perfusion-dependent wound healing complications, the method is feasible and uncomplicated but further confirmatory work needs to be undertaken with respect to consistency in achieving the desired cold challenge temperature (in this study, the temperature of the water-filled bag) as well as translating the technique from healthy participants to surgical patients. From this methods development study it is possible that, in addition to external factors influencing wound healing, there may be an inherent, individual (anatomical) susceptibility to poor wound healing that could be explained by an individual’s vascular anatomy and ultimately the ‘vitality’ of skin perfusion given the disruption of vascular networks consequent on vessel dissection and tearing during surgery. Here, post-operative perforator mapping has potential as an objective, personalised, medicine strategy, easily tested, in real-time, at the bedside as an indicator of the potential perfusion state of the cutaneous microvasculature in the region of a planned incision. Whether a reduction in perforator number or speed of recovery after moderate cold challenge could be a ‘flag’ for later post-operative wound healing complications remains unclear but from our previous studies, we know that the appearance of cold spots along the wound site marks, in many cases, a prodrome for later SSI. The appearance of low temperature ‘cold spots’ (within a well perfused wound area) likely reflects avascular (or poorly perfused) regions which ultimately influence the skin heat map [57]. Whilst many pre- and post-operative factors, both internal (smoking, immunity, obesity) and external (asepsis, surgical technique, surgeon skill, emergency CS) are associated with SSI risk [58], it is recognised that wounds will not heal if tissue perfusion is inadequate [14]. Thus, knowledge of cutaneous perfusion during the peri-operative period has promise as a method for skin (and wound) perfusion deficits. Whilst there is some evidence, at least from studies in free flap surgery, particularly the SIEA flap, that a reduction in perforator number carries a higher risk of fat necrosis [59], it is not yet possible to tell from results of this study whether differences in the number of perforators supplying skin in the region of the surgical incision has a bearing on perfusion-dependent wound healing outcomes. Finding a method to induce mild cold challenge and subsequent hot spot mapping has promise for future investigations even though some difficulties still need to be resolved, not least in controlling the temperature of the applied stimulus. Whilst a similar, water filled, bag system has been used successfully for intra-operative use [47], the method ultimately lacks a reliable means to adjust water temperature. In this study, bag water temperature was maintained from 18–22 °C across the seasons, a range sufficient to lower abdominal skin temperature effectively by 8–10 °C. Our concern was the relationship between bag temperature and influence on skin temperature recovery marked by hot spot appearance. Control of the temperature of the applied stimulus is warranted to improve consistency. However, for this study at least, after 1 min of recovery, spot count was not significantly influenced by the initial temperature of the bag applied to skin. This gives a benchmark for the timing to thermal recovery, i.e., that the duration to observe for hot spot appearance should be more than one minute. Indeed, whilst a lower bag temperature increases image contrast between hot spot and surrounding skin, it does not alter the location or rate of recovery of the hot spot which defines the anatomical site of the perforator. For pragmatic purposes, 5 min provides a realistic rewarming phase to conclude the thermal recovery period for perforator location and evaluation. ## 5. Conclusions In everyday healthcare objective wound and skin edge imaging is seldom done; clinicians rely more on experience and visual assessment and/or the use of any number of wound assessment tools available to them in their practice. What cannot be seen by eye is missed. However, objective, independent IR imaging technology could help clinicians assess whether avascular regions exist along or around the wound. This would provide an early signature of incipient tissue damage which may ultimately lead to delayed healing or even to a surgical site infection prodrome. Perforator mapping using DIRT could be a potentially valuable tool for stratification of high-risk patients in evidence-based antibiotic prophylaxis. In this study a new approach to skin perfusion assessment was tested in healthy participants using dynamic long-wave infrared video thermography before and after 2 min of a mild cold challenge to abdominal skin. The technique was shown to be feasible over a period of 10 min but for practical purposes perfusion-dependent thermal mapping is achievable and robust over a shorter interval of 5 min. In this study, hot spots marked the position of cutaneous perforators, but number of hot spots was not significantly influenced by adiposity indicating that it is individual variability in the number (and thus position) of perforator vessels rather than subcutaneous adiposity that most influences the dynamics of the abdominal thermal map. For future applications in surgical patients, results of this study may help to shape future approaches to wound complications risk assessment by emphasising the clinical importance of personalised cutaneous perfusion mapping rather than risk stratification by body habitus as is currently the norm. ## References 1. Vahratian A.. Prevalence of overweight and obesity among women of childbearing age: Results from the 2002 National Survey of Family Growth. Matern. Child Health J. (2009) 13 268-273. DOI: 10.1007/s10995-008-0340-6 2. Gomaa K., Abdelraheim A.R., El Gelany S., Khalifa E.M., Yousef A.M., Hassan H.. 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--- title: HDL Function and Size in Patients with On-Target LDL Plasma Levels and a First-Onset ACS authors: - Alberto Cordero - Natàlia Muñoz-García - Teresa Padró - Gemma Vilahur - Vicente Bertomeu-González - David Escribano - Emilio Flores - Pilar Zuazola - Lina Badimon journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048810 doi: 10.3390/ijms24065391 license: CC BY 4.0 --- # HDL Function and Size in Patients with On-Target LDL Plasma Levels and a First-Onset ACS ## Abstract Patients admitted for acute coronary syndrome (ACS) usually have high cardiovascular risk scores with low levels of high-density lipoprotein cholesterol (HDL-C) and high low-density lipoprotein cholesterol (LDL-C) levels. Here, we investigated the role of lipoprotein functionality as well as particle number and size in patients with a first-onset ACS with on-target LDL-C levels. Ninety-seven patients with chest pain and first-onset ACS with LDL-C levels of 100 ± 4 mg/dL and non-HDL-C levels of 128 ± 4.0 mg/dL were included in the study. Patients were categorized as ACS and non-ACS after all diagnostic tests were performed (electrocardiogram, echocardiogram, troponin levels and angiography) on admission. HDL-C and LDL-C functionality and particle number/size by nuclear magnetic resonance (NMR) were blindly investigated. A group of matched healthy volunteers ($$n = 31$$) was included as a reference for these novel laboratory variables. LDL susceptibility to oxidation was higher and HDL-antioxidant capacity lower in the ACS patients than in the non-ACS individuals. ACS patients had lower HDL-C and Apolipoprotein A-I levels than non-ACS patients despite the same prevalence of classical cardiovascular risk factors. Cholesterol efflux potential was impaired only in the ACS patients. ACS-STEMI (Acute Coronary Syndrome—ST-segment-elevation myocardial infarction) patients, had a larger HDL particle diameter than non-ACS individuals (8.4 ± 0.02 vs. 8.3 ± 0.02 and, ANOVA test, $$p \leq 0.004$$). In conclusion, patients admitted for chest pain with a first-onset ACS and on-target lipid levels had impaired lipoprotein functionality and NMR measured larger HDL particles. This study shows the relevance of HDL functionality rather than HDL-C concentration in ACS patients. ## 1. Introduction Coronary artery disease (CAD) is the major cause of mortality worldwide and is characterized by the chronic and initially silent development of atherosclerotic plaques in the coronary arteries [1,2]. Acute coronary syndromes (ACSs) are unstable and abrupt clinical manifestations of atherosclerosis, including a wide range of presentations such as unstable angina, non-ST-segment-elevation myocardial infarction (NSTEMI) and ST-segment-elevation myocardial infarction (STEMI) [3]. High levels of low-density lipoprotein cholesterol (LDL-C) is the leading effector for atherosclerosis development and, also, for recurrent cardiovascular events after an initial ACS [4]. Conversely, high-density lipoprotein cholesterol (HDL-C) is a strong independent predictor that inversely correlates with the risk of CAD and its thrombotic complications [5,6,7,8,9]. However, there are controversial results regarding HDL-C levels and CAD coming from Mendelian randomization studies [10] and pharmacological studies raising HDL-C levels [11]. Therefore, a new concept has arisen considering that cholesterol carried by HDL (HDL-C) does not reflect HDL functionality; in fact, it is the HDL micelle and not HDL-C that has shown different anti-atherogenic properties [12,13,14]. Moreover, traditional measures of cholesterol quantify the cholesterol and triglyceride content of lipoproteins in milligrams per decilitre and use the amount of cholesterol measured to assess risk. However, individuals can vary in their lipoprotein particle numbers and sizes, meaning that even though they might have equivalent cholesterol levels, they can vary in their risk for cardiovascular disease (CVD). Measuring particle number and size by nuclear magnetic resonance (NMR) spectroscopy could be a better read-out for CVD risk assessment [15,16]. Clinical registries have highlighted that a large percentage of patients admitted for an ACS have non-elevated LDL-C levels [8,9] and that atherosclerotic plaques can be detected even in patients with very low levels of LDL-C [17]. Therefore, HDL function and its interplay with other lipid and non-lipid molecules represent a challenge in ACS risk and onset [18,19]. Based on this evidence, we designed a real-world clinical study (as indicated in the Graphical Abstract) to investigate HDL/LDL functionality and lipoprotein particle number and size in patients with a first-onset ACS presentation having LDL-C and non-HDL-C with average levels of 100 ± 3.6 mg/dL and 128 ± 4.0 mg/dL, respectively, and intermediate cardiovascular risk scores. ## 2.1. Patient Characteristics Ninety-seven subjects (72 men, 25 women) with an average age of 64.9 ± 1.2 years who were initially recruited for the study were included in the final analysis. Categorization of the patients according to electrocardiogram pattern on admission (Table 1) found that (NSTEMI) patients were older (mean ± SEM; 70.3 ± 1.9) and more likely to have hypertension, diabetes mellitus (DM) and dyslipidaemia than STEMI patients. Conversely, STEMI patients were younger (mean ± SEM; 60.0 ± 1.7) and had higher smoking habits. No differences regarding body mass index (BMI) were reported between groups ($$p \leq 0.354$$). HDL-C and apolipoprotein A-I (ApoA-I) levels were significantly lower in (ACS) patients compared with non-ACS patients. Nevertheless, no differences were observed in LDL-C and non-HDL-C levels between groups. The STEMI group had lower left ventricular ejection fraction (LVEF) and higher cardiac damage (troponins) than the NSTEMI and non-ACS groups (Student’s t-test, $$p \leq 0.007$$ and $p \leq 0.001$, respectively) and more subsequent intervention for revascularization (χ2 test, $p \leq 0.001$) and coronary stents (χ2 test, $$p \leq 0.007$$). Interestingly, preadmission medication use was higher in the NSTEMI and non-ACS groups, the use of acetylsalicylic acid (ASA) (χ2 test, $$p \leq 0.044$$), angiotensin receptor blocker (χ2 test, $$p \leq 0.048$$), calcium antagonists (χ2 test, $$p \leq 0.022$$) and beta-blockers (χ2 test, $$p \leq 0.004$$) being significantly higher. As shown in Table S1, patients had an average of 100 ± 3.6 mg/dL of LDL-C at arrival, as well as 146 ± 8.8 mg/dL of triglycerides and 128 ± 4.0 mg/dL of non-HDL-C (on-target levels for primary prevention). ## 2.2. Lipoprotein Particle Number and Size As shown in Table 2, ACS patients (especially STEMI) had a decreased number of small HDL particles (HDL-P) (ANOVA test, $p \leq 0.001$) compared with non-ACS individuals. Overall, ACS patients had a larger HDL particle diameter than non-ACS individuals (8.4 ± 0.02 vs. 8.3 ± 0.02 and, ANOVA test, $$p \leq 0.004$$). The triglyceride content (mg/dL) was similar in all lipoproteins. Particle numbers of each lipoprotein class (expressed in percentage) are provided in Table S2. ACS patients (especially STEMI) had a lower percentage of small very low-density but a higher percentage of medium small very low-density particles (VLDL-P) compared with non-ACS individuals (Student’s t-test for unpaired samples, $$p \leq 0.026$$ and $$p \leq 0.020$$, respectively). Furthermore, ACS patients (especially STEMI) had a lower percentage of small HDL particles (HDL-P) (ANOVA test, $$p \leq 0.004$$) but higher medium (ANOVA test, $$p \leq 0.004$$) and large HDL-P percentages (ANOVA test, $$p \leq 0.012$$) compared with non-ACS individuals. As shown in Tables S3 and S4, the reference group of volunteers without cardiovascular disease had a lower proportion of pro-atherogenic LDL particles despite having higher levels of LDL-C, though these differences did not reach statistical significance. However, similarly to non-ACS individuals, the reference group had a smaller average particle diameter of HDL compared with ACS patients (Bonferroni post hoc test: reference vs. NSTEMI $$p \leq 0.004$$ and reference vs. STEMI, $p \leq 0.001$). ## 2.3. Assays for Lipoprotein Functionality Although patients had on-target LDL-C levels (lower than the reference healthy group), LDL susceptibility to oxidation was higher in patients than in the reference individuals ($p \leq 0.001$, Figure 1A), presenting approximately a 10 min faster LDL oxidation (time to half maximum) even in the presence of medication. In addition, HDL antioxidant capacity and cholesterol efflux were also impaired in ACS patients compared with the reference group (Figure 1B,C; Student’s t-test, $$p \leq 0.002$$ and $$p \leq 0.038$$, respectively). Differences in lipoprotein function are shown in Table 3. ACS patients exhibited a diminished capacity to promote cholesterol efflux with respect to the reference subjects (CEC (%):19.5 ± 0.6 vs. 22.3 ± 1.4; Student’s t-test, $$p \leq 0.041$$). By TRAP analysis (% of oxidized LDL inhibition), both non-ACS and ACS groups exhibited an impaired HDL-antioxidant capacity compared with the healthy population (ANOVA test, $$p \leq 0.005$$). ## 2.4. HDL Oxidation Inversely Correlates with Cholesterol Efflux Capacity (CEC) In the reference group, induced HDL oxidation (determined by fluorometry) was inversely correlated with the CEC (Figure 2); interestingly, a similar relationship was found in non-ACS patients (Figure 2A and Figure S1). However, ACS-STEMI patients had a significantly lower slope and Y-intercept value than the reference group ($$p \leq 0.029$$ and $$p \leq 0.012$$, respectively) after inducing HDL oxidation (Figure 2B and Figure S2) ($$p \leq 0.003$$ and $p \leq 0.001$, respectively). In summary, there was a significant alteration in HDL function in ACS patients; in fact, in the STEMI group there was no relation to CEC. Both non-oxidized HDL and oxidized HDL was unable to promote CEC in STEMI patients. We have carefully analysed the time of blood collection with respect to patient admission to observe changes in the lack of HDL function in STEMI patients (Table S5). There was no time effect on the reduced HDL function regarding CEC and TRAP or in particle size distribution in samples collected just after admission or more than 24 h later. ACS patients that had percutaneous coronary intervention and whose blood was collected more than 24 h later had the lowest plasma HDL/ApoA-I levels. Therefore, HDL functionality was not altered during the acute phase in ACS patients. ## 3. Discussion Our study in a cohort of real-world patients with non-elevated LDL-C levels and admitted for a first ACS, demonstrates the importance of taking into account HDL/LDL functionality and lipoprotein particle number/size in ACS patients as an improved read-out for CVD risk assessment, rather than just measuring the cholesterol carried in the lipoproteins. Since clinical features and classical risk factors are similar to other reports [8,9,17], we believe that our results might be representative and translatable to clinical practice. HDL benefits on cardiovascular protection are mainly conferred by its capacity to promote cholesterol efflux, preventing and stabilizing atherosclerotic lesions [14,20,21,22] and its potential to protect LDL from oxidative damage [23]. The results of our study expand the understanding on the effects of HDL and LDL in ACS. A previous analysis from our institution revealed that a low level of HDL-C was the variable more closely related to being admitted for an ACS than a non-ACS [5]. In this new study, we were able to demonstrate that HDL particles were clearly dysfunctional, especially in patients admitted for STEMI, expanding the knowledge in this controversial field. The results of our study showed that the HDL anti-atherogenic functional capacities were impaired in ACS patients, especially in the STEMI group with higher cardiac damage (elevated troponins). Interestingly, the degree of HDL-C oxidation was inversely correlated with the CEC in the reference and non-ACS patients. Oxidative stress and inflammation may occur in ACS patients and is capable of inducing pro-atherogenic modifications in lipoproteins, switching them into a dysfunctional state [24]. The degree of HDL-C oxidation was inversely correlated with the CEC in the reference healthy volunteers and non-ACS patients but not in the ACS patients because HDLs were already modified in these patients at baseline. This fact is especially evident in the STEMI patients. Hence, these observations suggest that HDL particles from subjects at the highest risk of an ACS may already have modifications in the circulation altering their functionality that are not modifiable by inducing in vitro oxidation. Nevertheless, whereas LDL-C levels were lower in patients than in the reference group, all patients had LDL particles with increased susceptibility to oxidation and impaired HDL antioxidant capacity. Moreover, cholesterol efflux capacity was significantly diminished only in ACS patients. In fact, HDL particles in ACS patients (especially the STEMI) were enlarged, probably depicting a shift into a dysfunctional state given that they are the small HDL-P ones linked to an increased cholesterol efflux and antioxidant capacities [25,26,27]. Therefore, not only the levels but also the functions of lipoproteins have a clear high impact on their contribution to ACS onset and presentation. Some studies have suggested, based on the observation that individuals with higher levels of large HDL particles have a lower risk of CVD, that larger HDL particles are more protective against CVD than smaller HDL particles [28,29]. On the contrary, other studies showed that small, dense HDL particles may actually be more protective against CVD than larger particles [30,31,32]. This controversy suggests that the relationship between HDL particle size and CVD risk is complex and may depend on other factors such as the presence of other lipid abnormalities or genetic factors [33]. Another issue is the difficulty in accurately measuring HDL particle size. Different methods can yield different results, and there are currently no standardized methods for measuring HDL particle size [34]. Hence, though we observed that ACS patients with dysfunctional lipoproteins have larger HDL-P, more research is needed to fully understand this relationship. Clinical registries are concordant in the findings that patients with HDL-C > 40 mg/dL have a lower incidence of cardiovascular events [5,8]. Nonetheless, the therapies that were designed to increase HDL-C levels, such as Cholesteryl Ester Transfer Protein (CETP) inhibitors [35] or nicotinic acid [36], did not reduce the incidence of major cardiovascular events. Thereafter, HDL functionality is probably impaired by some pharmacologic strategies in what reflects one of the many gaps in the knowledge of HDL particles. Our results, show significant differences in HDL particle functionality that might warrant future investigations to improve HDL functionality in subjects with high cardiovascular risk. Our study has the limitations of being cross-sectional, performed in a single centre and having a small sample size and a low number of women. Moreover, due to logistic factors that cannot be controlled in the clinical practice, samples were obtained after admission at different times. However, despite the described limitations and the heterogeneity of groups, the study is based on a well-characterized real-world cohort of patients admitted for first-onset chest pain with on-target LDL-C levels. In conclusion, patients treated as per guidelines in their primary care management with intermediate CVD risk that suffered a first chest pain episode had an impaired lipoprotein function, which might lead to a higher oxidative status, and an altered number/size of lipoprotein particles irrespective of the LDL-C level and optimal treatment. Interestingly, triglycerides transported by all lipoproteins were within the normal range as well as non-HDL-C levels. This study shows the relevance of changes in lipoprotein functionality and in particle number/size on first onset ACS presentation. The on-going follow-up of this cohort might add more information about recurrent events and long-term mortality according to the determinations obtained at baseline. ## 4.1. Clinical Diagnosis of Chest Pain Categories Ninety-seven patients admitted from January 2018 to April 2018 into “Hospital San Juan de Alicante” with chest pain were clinically diagnosed as ACS (ACS; $$n = 70$$) or non-ACS patients (non-ACS; $$n = 27$$) with high cardiovascular risk. Patients were classified as ACS or non-ACS after all diagnostic tests were performed, including an exercise test, echocardiogram or angiography. In addition, ACS patients were further categorized by electrocardiogram pattern on admission into NSTEMI ($$n = 31$$) and STEMI ($$n = 39$$) (Table 1). Non-ACS was diagnosed by the exclusion of acute ischemia (no troponin elevation and no dynamic or electrocardiographic changes suggestive of myocardial ischemia), inducible ischemia (conclusive stress test) or unstable or severe coronary lesions in the angiography, as previously published [5]. Demographic characteristics of the patients, risk factors for coronary artery disease (smoking, hypertension, dyslipidaemia and diabetes mellitus), medical history, laboratory data during the hospitalization, vital signs on admission, treatment and diagnosis at discharge were collected from all patients. A history of heart failure was codified if patients had at least one hospitalization with such diagnosis at discharge or the typical signs and symptoms of heart failure and a compatible echocardiogram. Patients underwent an echocardiography within 48 h of admission, and the left ventricular ejection fraction (LVEF) was calculated using Simpson’s method [37]. Patients were excluded from the study if they had age > 85, previous history of ischemic heart disease or heart failure, diagnosis of hypo- or hyperthyroidism, presence of previous valve disease, initial haemoglobin < 10 g/dL, initial presentation of ACS as cardiogenic shock, treatment with anti-retrovirals, pregnancy or died in the first <24 h or before the first blood test after a fasting night could be obtained (see Flowchart in Figure 3). All patients had moderate cardiovascular disease (CVD) risk following the European Heart Score (below $5\%$ and higher than $1\%$) [38] and the Framingham Risk Score (10–$19\%$) [39] (Table 1). Additionally, a reference group of 31 healthy, non-treated, overweight or obese volunteers without additional risk factors or clinical symptoms of disease was included for baseline comparative purposes of the novel techniques investigated in this study. Patient at admission and volunteer characteristics are shown in Table S1. The study complies with the Declaration of Helsinki and was approved by the Ethics Committee of Clinical Research of “Hospital San Juan de Alicante”, Spain (Ref $\frac{17}{314}$; 7 June 2017); informed consent was obtained from all subjects. ## 4.2. Biochemical and Laboratory Parameters Blood samples were obtained within a mean ± SEM of 2.60 ± 2.02 days. Briefly, blood samples were collected without anticoagulant or in EDTA-containing vacutainer tubes for serum and plasma preparation. Routine standard biochemical determinations including troponins and haemogram were performed for the on-going ACS registry of our institution [5]. Aliquots of both serum and plasma were kept at −80 °C for the specific assays involved in this study. ## 4.3. LDL and HDL Sample Preparation and Purity Control LDL (density 1.019 to 1.063 g/mL) and HDL (density range 1.063–1.210 g/mL) were obtained from 1 mL plasma-EDTA from individual samples by sequential ultracentrifugation according to the method originally described by Havel et al. [ 40] and modified by De Juan-Franco et al. [ 41]. To avoid lipoperoxidation, all solutions contained 1 mmol/L EDTA and 2 μmol/L butylated hydroxytoluene (BHT) and centrifugations were performed at 4 °C using rotors stored in a cold room. Briefly, plasma was adjusted to a density of 1.019 g/mL with a concentrated salt solution (potassium bromide) and centrifuged at 225,000× g for 18 h in a Beckman L-60 preparative ultracentrifuge with a fixed-angle type 50.4 Ti rotor (Beckman, Brea, CA, USA). After removal of the top layer containing very low and intermediate density lipoproteins (VLDL and IDL), the density of the infranatant was adjusted to 1.063 g/mL, followed by centrifugation for 20 h at 225,000× g before LDL was collected from the top of the tube. Lastly, the process was repeated adjusting the plasma density to 1.210 g/mL and samples were ultracentrifuged at 225,000× g for 24 h at 4 °C to allow HDL to float and separate from lipoprotein-deficient serum. In addition, LDL to be used in the TRAP assay was isolated from a pool of plasma (180 mL) obtained from normolipidemic subjects and obtained as described above in a Beckman Optima L-100 XP with a fixed-angle type 50.2 Ti (Beckman, Brea, CA, USA). LDL and HDL fractions were dialyzed against phosphate-buffered saline (PBS) for 24 h. After dialysis, LDL and HDL protein content was determined by the colorimetric BCA assay (Pierce) and adjusted to 100 µg/mL with PBS. Samples were left protected from light at 4 °C until analysis. LDL and HDL purity was routinely analysed by electrophoresis (2 µL sample) in agarose gels using a commercial assay (SAS-MX Lipo 10 kit, Helena Biosciences, London, UK) as described by the providers. ## 4.4. Conjugated Diene Assay Susceptibility of LDL to copper-induced oxidation was assessed by determining the formation of conjugated dienes. Briefly, freshly prepared LDL samples adjusted to 100 µg/mL with PBS were analysed in 96 well plates by incubation with a copper (II) sulphate (CuSO4•5H2O) solution at a final concentration of 5 µM. The change of absorbance was monitored for 2 h 30 min at 37 °C using a SpectraMax 190 Microplate reader (Molecular Devices, San José, CA, USA) by continuously following the formation of conjugated diene, a product of lipid peroxidation with absorbance peak at 234 nm. The total amount of conjugated diene was calculated using the molar extinction coefficient of 29,500 M−1cm−1 [42]. ## 4.5. HDL Antioxidant Potential The antioxidant potential of HDL was assessed by performing the total-radical-trapping antioxidative potential (TRAP) test [43]. This method is based on the capability of HDL to prevent LDL (control LDL) oxidation. Briefly, HDL and LDL lipoproteins were diluted in PBS 1× to a final concentration of 100 µg protein/mL. HDL from each individual subject was incubated for 4 h at 37 °C with copper (II) sulphate (CuSO4•5H2O) at final concentration of 20 µM either alone or in the presence of LDL control (plasma pool). As a baseline value, the LDL sample was incubated alone with and without CuSO4 during the same time period. Oxidation was stopped by adding 50 µL of EDTA 1 mM. Thereupon, 100 µL of each sample was transferred to a fluorescence 96-well plate (Corning®, TC Black plate with clear bottom, New York, NY, USA). Eventually, samples were incubated with 50 µL of freshly prepared DCFH-DA (2′,7′-dichlorodihydrofluorescein diacetate, Molecular Probes, Eugene, OR, USA) at a final concentration of 10 µM for 1 h 30 min at 37 °C and 100 rpm. Dichlorofluorescin-diacetate was employed as the marker of the oxidative reaction [44]. Lipid oxidation products convert DCFH to DCF, which produces intense fluorescence. The intensity of fluorescence was determined with a Typhoon FLA9500 set at λex = 500 nm and λem = 520 nm. Final fluorescence measurements were expressed as the percentage of inhibition of oxidized LDL in the presence of HDL relative to the oxidation level when LDL was incubated in absence of HDL. ## 4.6. HDL Cholesterol Efflux Capacity Assay The cholesterol efflux capacity (CEC) of HDL was determined in cholesterol-loaded murine macrophages as previously reported [45]. To this end, J774A.1 mouse macrophages (at passage seven) were cultured in RPMI 1640 (Roswell Park Memorial Institute medium) containing $10\%$ of heat-inactivated FBS (Foetal bovine serum), 2 mM glutamine, 100 U/mL penicillin, 100 U/mL streptomycin and 10 µg/mL gentamicin at 37 °C in a humidified atmosphere of $5\%$ CO2. For the experiments, macrophages (1.5 × 105 cells/well) were seeded in 6-well culture plates (Falcon 6-well Clear Flat Bottom TC-treated culture plate, Corning®, New York, NY, USA) and labelled for 48 h with [1α, 2α (n)-3H]-cholesterol] (GE Healthcare, Chicago, IL, USA) at 1 µCi per well. Cells were equilibrated overnight in $0.2\%$ bovine serum albumin and thereafter incubated with RPMI media containing $5\%$ ApoB-depleted serum (4 h, 37 °C) to promote cholesterol efflux from the [3H] cholesterol-labelled cells. ApoB-depleted serum was obtained by precipitation of ApoB particles with a solution containing phosphotungstic acid (0.484 mM) and MgCl2 (22 mM). ApoA-I and ApoB measurements in ApoB-depleted serum samples were determined by immunoturbidimetric assays using commercial kits adapted to a COBAS 501c autoanalyzer (Roche Diagnostics, Basilea, Switzerland). ApoB reported values in ApoB-depleted serum were below 0.06 mg/mL. The radioactivity signal was quantified in both media and cells and the percentages of cholesterol efflux calculated by expressing the radioactive cholesterol released to the medium as the fraction (%) of the total radioactive cholesterol present in the well (radioactivity in the cell + radioactivity in medium). ## 4.7. Lipoprotein Particle Number and Size Measurements Lipoprotein size was directly measured in serum (500 µL) by nuclear magnetic resonance (NMR) as described by Mallol et al. [ 46] using the two-dimensional diffusion-ordered 1H-NMR spectroscopy (2D DOSY) Liposcale® (Biosfer Teslab, Reus, Spain). Briefly, particle concentration was obtained from the measured amplitudes and attenuation of their spectroscopically distinct lipid methyl group NMR signals using the 2D diffusion-ordered 1H NMR spectroscopy (DSTE) pulse. The methyl signal was surface fitted with nine Lorentzian functions associated with each lipoprotein subtype of the LDL: large, medium and small. The area of each Lorentzian function was related to the lipid concentration of each lipoprotein subtype, and the size of each subtype was calculated from their diffusion coefficient. The particle numbers for each lipoprotein subtype were calculated by dividing the lipid volume by the particle volume of a given class. The lipid volumes were determined by using common conversion factors to convert concentration units into volume units [47]. ## 4.8. Statistical Analysis Statistical analyses were conducted using StatView 5.0.1 software (SAS Institute, Cary, NC, USA) and SPSS software (IBM SPSS Statistics 25.0.0, New York, NY, USA) except when indicated. Data are expressed by the number of cases (qualitative variable) and as mean ± standard error of the mean (SEM) or median [IQR] for the quantitative variable. The normal distribution of variables was analysed by the Kolmogorov–Smirnov test. Differences between characteristics of the groups were analysed by unpaired Student’s t-test or an analysis of variance (ANOVA) for parametric variables. A Bonferroni post hoc test was run for two group comparisons after ANOVA. Slope differences between groups in regression analysis were assessed by analysis of covariance (ANCOVA). When normality failed, Mann–Whitney or Wilcoxon tests was performed for non-parametric variables. When needed, chi-squared analysis was performed as indicated in the Results section. 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--- title: '“Working Together”: Perspectives of Healthcare Professionals in Providing Virtual Care to Youth with Chronic Pain during the COVID-19 Pandemic' authors: - Danielle Ruskin - Julia Borsatto - Klaudia Szczech - Monique Tremblay - Lisa N. D’Alessandro - Giulia Mesaroli - Naiyi Sun - Catherine Munns - Jennifer Stinson journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048812 doi: 10.3390/ijerph20064757 license: CC BY 4.0 --- # “Working Together”: Perspectives of Healthcare Professionals in Providing Virtual Care to Youth with Chronic Pain during the COVID-19 Pandemic ## Abstract Background: The onset of the coronavirus disease in 2019 necessitated a rapid transition to virtual care for chronic pain treatment. Methods: A mixed methods design was implemented using qualitative interviews and quantitative satisfaction surveys. Interviews were conducted in February 2021 with a sample of healthcare professionals (HCPs; $$n = 6$$) who had provided multidisciplinary treatment (MDT) through an outpatient hospital pediatric chronic pain program. Satisfaction surveys were distributed to all MDT professionals employed by the clinic in April 2021 ($$n = 13$$ of 20 eligible; $65\%$ response rate). Participants represented medicine, rehabilitation, and mental health professionals. Results: Analysis of interviews generated five themes: [1] adaptation to virtual care, [2] benefits of virtual care, [3] limitations of virtual care, [4] shifting stance on virtual care over time, and [5] considerations for implementing virtual care. The satisfaction survey data revealed that respondents were able to effectively provide appropriate diagnoses, recommendations, and/or care plans for pediatric chronic pain via virtual care ($$n = 12$$, $92.3\%$). Detailed survey responses are presented by discipline. Conclusions: This study provides a rich exploration of HCPs’ experiences in providing MDT for pediatric chronic pain within a virtual care model. The current results may contribute to the future development of guidelines for virtual care delivery with pediatric chronic pain populations. ## 1. Introduction Chronic pain affects 20–$35\%$ of school-age children [1,2]. Those who present to chronic pain programs in tertiary care hospitals have pain that profoundly disrupts both the child’s life and their family life [3]. Common functional impairments in children with chronic pain include (a) school absenteeism (50–$75\%$ of children missing 1 day/month [4], $20\%$ missing more than half of school days [5], and $17\%$ of cases with complete withdrawal [6]), (b) physical limitations (e.g., reduced physical activities, poor endurance, and generalized deconditioning), [7] and (c) mental health sequelae and comorbidities, particularly anxiety and depression, which co-occur with pain in $21\%$ to $26\%$ of children [8]. The impacts on family life are profound, including financial strife due to parental days off work and limitations on family outings and activities [9]. Given that a latency of 6 months from the time of referral to treatment for chronic pain is associated with deterioration in health-related quality of life and psychological wellbeing, the importance of efficient and timely care for chronic pain cannot be overstated [10]. Therefore, in response to the pandemic from the coronavirus disease of 2019 (COVID-19), the delivery of care for youth living with chronic pain drastically shifted in an attempt to provide timely care. Beginning in March 2020, the world saw pediatric pain management clinics abruptly changing their delivery of care owing, in part, to social-distancing requirements. Depending on the location, some healthcare professionals (HCPs) were unable to see patients for varying periods of time because of clinic shutdowns or redeployment, whereas others were able to rapidly pivot to a virtual model of care delivery [11,12,13]. Fortunately, across Canada, all 13 pediatric pain clinic sites were able to successfully transition to virtual care, where the largest program in Canada (The Hospital for Sick Children) transitioned over the course of 2 weeks at the onset of the pandemic [11,14]. Further, given that the optimal paradigm for chronic pain management involves psychological, physical, and pharmacological approaches, the transition from in-person care to a full-scale virtual model was no easy feat [15,16]. Multidisciplinary treatment (MDT) teams consisting of physicians (e.g., anesthesiologist, pain doctor, or pediatrician), physical therapists, nurse practitioners, psychologists and/or psychiatrists, occupational therapists, and/or social workers began to rapidly navigate virtual technology to ensure that patients received safe and quality care [13,17]. While some HCPs had prior knowledge of and practice delivering occasional telemedicine appointments, very few had experience delivering full-time remote services (e.g., videoconference, telephone consultation) [12,18,19]. HCPs faced many administrative, logistical (i.e., infrastructure, setting up virtual care), and technological (i.e., internet connection, technological literacy) challenges, but they demonstrated resilience in meeting the demands of the unprecedented circumstances [13,17]. Now, nearly 3 years since the initial shift in modality of care, it is apparent that virtual healthcare has endured beyond needing to meet social-distancing requirements [20,21,22]. Virtual care has proven to be an advantageous method of delivery across populations and disciplines, with geographical and financial accessibility frequently touted as benefits [18,20]. However, its delivery also possesses a unique set of challenges. When considering specifically the delivery of virtual pediatric pain care, HCPs described the inability to perform in-person physical exams, the blunting of nonverbal and physical cues, and inequitable access to the internet and technological devices as major limitations. These findings come from an investigation by Killackey et al. [ 17], who conducted a qualitative analysis of various HCPs’ experiences in shifting to virtual pediatric pain care during the pandemic. The present study aims to build on these findings and enhance the understanding of providing this care in the context of an MDT team. Using mixed methodologies (semistructured interviews and self-reported satisfaction surveys), the primary aim of the current study was to elicit feedback from an MDT team of HCPs on their experiences assessing and treating pediatric chronic pain patients by utilizing virtual care during the second wave of the pandemic in Canada. A secondary study aim was to explore differences in experiences across disciplines in the multidisciplinary team (i.e., mental health, rehabilitation, and medicine). ## 2.1. Study Design Collecting stakeholder feedback is essential to program evaluation and is best obtained by using combined quantitative and qualitative data to capture the richness and complexities of program implementation [23]; therefore, a mixed methods design was employed in the current study. Both qualitative (semistructured interview) and quantitative (self-report satisfaction survey) methods were used to identify and evaluate the experiences of HCPs delivering virtual MDT throughout the second wave of the COVID-19 pandemic in Canada (e.g., February to April 2021). Qualitative interview questions mirrored areas queried in the satisfaction survey but provided additional opportunity for richer feedback (see Section 2.6. for additional details). ## 2.2. Setting The current study was conducted at a pediatric chronic pain clinic providing outpatient MDT pain assessment and management within a large urban tertiary care hospital. Ethics approval for this research study was obtained from the institution’s research ethics board (REB #1000071310). ## 2.3. Virtual Technology Platform In this paper, “virtual MDT care” refers to psychology and physiotherapy assessment and treatment delivered using PHIPA (Personal Health Information Protection Act)-compliant Zoom for healthcare. ## 2.4. Implementation of Virtual Care Model The chronic pain clinic at which this study took place successfully transitioned all clinic appointments (MDT intakes, assessments, and treatments) over a 2-week period to virtual care [11]. Patients referred to the clinic engaged in a 2-hour MDT intake visit (with a physician, psychologist, physiotherapist, and advanced practice nurse) composed of [1] reviewing medical and pain histories and current and prior functioning, [2] a physical exam, and [3] the disclosure of pain diagnoses and feedback as well as recommendations for treatment. This process, which previously occurred in person, was readily transferred to the virtual modality and was conducted in the same manner, albeit virtually. Following the MDT intake visit, separate psychology and physiotherapy assessments were completed. After the initial psychology and physiotherapy assessments, patients deemed appropriate for further treatment were then engaged in a block of treatment with the MDT. Each treatment block consisted of four sessions of weekly psychology and physiotherapy care (one hour each) and a 1-hour pain education session delivered to patients and families, with adjunctive pharmacology and occupational therapy support as needed (see Figure 1). Additional complementary therapies were offered, including a virtually delivered group for youth on mindfulness-based pain management. Prior to the pandemic, the above treatments were delivered in person, aside from infrequent virtual care treatments for those patients living in rural and remote areas of Ontario, Canada. The majority of psychology and physiotherapy assessments and treatment sessions had been virtually delivered over Zoom since the onset of the COVID-19 pandemic ($88.8\%$ of physiotherapy appointments and $96.4\%$ of psychology appointments over the study period). For selected cases, our team provided in-person assessments where virtual care would not have been feasible—e.g., very young children (<5 years old), those with developmental considerations, children who required an in-person physical exam (e.g., those with neuropathic pain or complex regional pain syndrome and those who have not had a physical exam), or individuals who faced barriers to accessing virtual care (e.g., low computer literacy, appropriate bandwidth, and lacking access to technology). ## 2.5. Recruitment and Participants Utilizing purposive sampling to ensure that each discipline that provides MDT in the institutions’ pediatric chronic pain team was represented, a portion of the clinic’s HCPs were invited to participate in semistructured interviews (SSIs) regarding their experience in delivering virtual care. It was determined a priori that a sample of six participants was adequate for producing cross-case generalities while still considering each participant as an individual identity [24]. The six participants for qualitative interviews were selected by using the following procedure: HCPs with full-time equivalent (FTE) positions were approached first because these individuals would have the maximum hours spent providing virtual care. If an HCP approached for the interview declined, then the next HCP in their discipline was invited to participate on the basis of FTE. Information on the purpose of the study, a consent statement, and invitations to participate were sent to HCPs by a remote member of the institution’s research team (K.S.) in February 2021. To assess the extent to which the experiences described by the subset of HCPs generalized to the entire MDT team, a questionnaire examining HCP satisfaction with provision-of-pain assessments and treatments via the virtual modality was disseminated to all MDT professionals employed by the clinic. Information on the purpose of the surveys, as well as the access links, were emailed by K.S. in April 2021. Surveys were hosted in Redcap (Research Electronic Data Capture), a secure, web-based application designed to support data capture for research studies [25]. An implicit consent statement was included with the study purpose, and HCPs were assured that participation in the study was voluntary and would not impact their employment. Primary investigators were excluded from recruitment. ## 2.6. Procedure and Measures SSIs were virtually conducted by using PHIPA-compliant Zoom throughout February 2021. The interviewer was a predoctoral psychology resident (M.T.) with training in qualitative interviewing. SSI guides (see Supplementary Section S1) were created by the authors to generate rich, in-depth discussion on HCPs’ experiences in providing virtual care. Informed by Turner [26], questions were open ended and single faceted, with prompts such as “tell me more about that” used to elicit detail and clarity, when necessary. Interviews were audio recorded and transcribed verbatim. Staff satisfaction survey content was informed by a complementary patient/caregiver satisfaction study [27] and was refined by using themes generated from the SSIs through an exploratory design procedure [28]. Questions probed various aspects of virtual care, including equipment and technical issues, communication and rapport, clinical modifications, and perceived self-efficacy of assessment. Respondents could also provide recommendations on the future of pediatric pain care delivery. Response options varied across questions and included text entry, true/false, and a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). See Supplementary Section S2 for the complete satisfaction survey. ## 2.7.1. Qualitative Analysis SSIs were transcribed verbatim and reviewed for accuracy. Following transcription, the data were coded by K.S. and J.B. in Dedoose [29], a SaaS coding software program that can be used to organize and assist with coding qualitative data. Taking a reflexive thematic analysis approach, coders met biweekly to refine themes and subthemes in an iterative and rigorous manner [30]. Disagreements were resolved by consultation with another author (D.R.) as needed. Following these meetings, a coding manual was generated in Dedoose, and an interrater reliability coding test was created. Specifically, one of the coders (K.S.) selected 92 excerpts from the coding project file and applied themes and subthemes to these excerpts. The unlabeled excerpts were then entered into a test template for the second coder (J.B.) to apply themes and subthemes to assess the degree of agreement between coders. An interrater reliability analysis using the Kappa statistic was performed to determine consistency among raters [31]. ## 2.7.2. Quantitative Analysis Descriptive data were analyzed from the satisfaction surveys. For ease of interpretation, responses to items one to three and five to seven were merged into agree and disagree categories, respectively. Lastly, “neither agree nor disagree” responses remained as one response category. ## 3.1. Semistructured Interviews In total, six HCPs from a group of six eligible participated in the SSI ($100\%$ response rate). Five overarching themes, namely [1] adaptation to virtual care, [2] benefits of virtual care, [3] limitations of virtual care, [4] shifting stance on virtual care over time, and [5] considerations for implementing virtual care, as well as corresponding subthemes, were generated. They are described below. Disciplines have been attached to the quotes below in the following manner: medical professional includes physicians and nurses, rehabilitation professional includes physiotherapists and occupational therapists, and mental health professional includes psychologists and nurse practitioners (psychiatry). Please see Table 1 for a full list of the themes and subthemes generated from the interviews. ## 3.1.1. Theme 1: Adaptation to Virtual Care The first major theme examines participants’ observations of the shift from the in-person delivery to the virtual delivery of pain assessment and treatment. Three factors were echoed by multiple HCPs across disciplines, comprising three subthemes: (a) teamwork/togetherness, (b) virtual modality necessitating innovations in care delivery, and (c) ease of transfer to virtual care given the service model. Group cohesion as well as an openness to sharing knowledge with each other was essential in the shift to virtual delivery: Further, clinicians in the mental health discipline also highlighted the use of the virtual modality as a form of treatment: This sentiment was expanded on by other HCPs, who emphasized that many aspects of pain treatment boded well in the virtual modality: ## 3.1.2. Theme 2: Benefits of Virtual Care This theme was generated to reflect the common benefits of virtual care delivery described by HCPs. Many participants described virtual appointments as being more geographically and financially accessible (e.g., no parking fees, families not taking time off work). The related two subthemes are (a) convenience and comfort and (b) continuity of care. HCPs also described that seeing the patients in the comfort of the family’s home environment boded well for rapport and for treatment planning: HCPs were able to successfully navigate new technology to continue to meet treatment goals: ## 3.1.3. Theme 3: Limitations of Virtual Care Participants identified several limitations when providing virtual multidisciplinary treatment for pediatric chronic pain. The noted challenges included (a) limitations in observing nonverbal cues, (b) barriers to physical examination, (c) privacy concerns, (d) technology glitches, (e) virtual fatigue and engagement challenges, and (f) inequities in access. Further, restricted nonverbal cues in the virtual modality impacted clinical observation, as one participant described: Additionally, patients were at times brought to the clinic for in-person care in response to such barriers: At other times, privacy concerns completely prohibited aspects of virtual care: Participants also outlined that technological disruptions occurred on both HCP and patient ends: Participants also described limited engagement and rapport with caregivers during virtual appointments: Another participant described erroneously assuming that all patients have access to the required resources: ## 3.1.4. Theme 4: Shifting Stance on Virtual Care A theme was constructed to describe how the evaluations and perceptions of virtual care from the perspective of HCPs, patients, and caregivers change over time. Participants’ statements were organized into two subthemes: (a) the change in HCPs opinions on virtual care and (b) the change in patient and caregiver perception of virtual care. It was also noted that although physical-distancing restrictions facilitated the mandatory shift to virtual care, this transition encouraged HCPs to view the potential of providing virtual care beyond the COVID-19 pandemic: Another echoed this sentiment: ## 3.1.5. Theme 5: Considerations for Implementing Virtual Care The final theme addressed considerations and recommendations for implementing virtual multidisciplinary treatment for pediatric chronic pain. This theme included (a) the preference for a hybrid model and (b) recommendations for implementing virtual care. Participants also specified a preference that initial appointments for new patients be in person at the clinic, in order to build rapport and effectively obtain confidential information from patients in a secure space: One participant suggested providing scheduling instructions for families to mitigate the abovementioned scheduling challenges when booking virtual appointments: Participants also commented on the pilot triage system that was developed by the clinic’s HCPs to schedule patients for virtual or in-person appointments: ## 3.2. Satisfaction Surveys In total, 13 HCPs from a group of 20 eligible HCPs completed the satisfaction survey ($65\%$ response rate). Overall, $92.3\%$ ($$n = 12$$) of respondents described virtual care as an effective treatment for pediatric chronic pain and agreed that virtually gathered information pertaining to a patient’s symptoms was sufficient to inform appropriate diagnoses, recommendations, and/or care plans. Rapport did not appear to be impacted by virtual care, all participants indicating an ability to develop a therapeutic relationship with the patient and caregiver. Of the 13 participants, 5 HCPs were in medicine, 5 were in rehabilitation, and 3 were in mental health. When asked to consider ways that virtual care differed from in-person care, $40\%$ ($$n = 4$$) of HCPs from the medicine and rehabilitation disciplines rated that the discussion of sensitive topics (e.g., self-injury) was more, or equally, challenging between modalities. HCPs in the mental health discipline did not endorse a difference. Similarly, when probed about the absence of physical contact in a virtual assessment, all HCPs in mental health ($$n = 3$$, $100\%$) agreed it was not a problem. However, $50\%$ ($$n = 5$$) of professionals in the medicine and rehabilitation disciplines reported that the lack of in-person physical examination made virtual assessment more challenging. Notably, they shared sentiments that assessments of skin color, temperature, texture, range of motion, and muscle tension had to be modified (e.g., self-assessment, separate video submissions). Regarding technological logistics, HCPs across disciplines ($$n = 10$$, $76.9\%$) described an overall positive experience, whereby they encountered few to no technical difficulties. When asked about their preferences for virtual use in the future, all HCPs participants agreed they will continue to use virtual care in some capacity. Participants expressed a preference for a hybrid model of delivery ($$n = 11$$, $84.6\%$), whereby some sessions are delivered in person and some virtually. Mental health professionals ($$n = 3$$, $100\%$) were more apt to remain virtual, even if in-person appointments were offered. Please see Table 2 for respondent characteristics. Table 3 outlines the survey responses where all HCPs ($$n = 13$$) were in agreement, and Table 4 provides differences observed across disciplines. ## 4. Discussion The primary aim of the present study was to examine the experiences of health care professionals who delivered multidisciplinary pediatric chronic pain care throughout the second wave of the COVID-19 pandemic in Canada. A secondary aim was to explore differences in experiences across the disciplines in the multidisciplinary team (i.e., mental health, rehabilitation, and medicine). The current study’s qualitative and quantitative analysis supports the growing body of literature describing the overall benefits and challenges of providing virtual care [12,13,32,33,34], and it corroborates Killackey et al. ’s [14] similar investigation into HCPs’ experiences in providing pediatric pain care during the COVID-19 pandemic. Further, this study provides a richer understanding of providing virtual care in the context of multidisciplinary pain management for pediatric chronic pain patients. Specifically, the results from the descriptive quantitative analysis support virtual care as an effective modality to inform chronic pain diagnoses and recommendations in care for children living with chronic pain. This is in line with many studies across both adult and pediatric populations, suggesting that virtual care enabled HCPs to provide essential treatment and intervention throughout the pandemic [13,18]. Notwithstanding the benefits related to this continuity of care, the rapid uptake of virtual treatment and assessment highlighted the barriers of providing virtual care. Particularly, medicine and rehabilitation professionals indicated that the lack of hands-on physical examination made virtual assessment more challenging. These findings align with other studies, which described the challenges of performing physical exams and the absence of nonverbal cues as being HCP-reported limitations of virtual care [17,35]. Further examinations of HCPs’ experiences highlighted that professionals in the mental health field experienced increased comfort discussing sensitive topics (e.g., self-injury) virtually than those in medicine or rehabilitation. It is possible that because mental health professionals elicit sensitive information as a standard component of their work, this is an area of comfort. In addition, the institution where this study was conducted disseminated a clear, stepwise plan to hospital staff, with a tailored protocol for mental health professionals to address safety concerns divulged during a virtual appointment (E. Romanchych, personal communication, 20 April 2022). This may also have contributed to the increased comfort levels of mental health professionals in discussing sensitive topics. Our qualitative analysis complements these findings and highlight five main themes related to HCPs’ experiences: [1] adaptation to virtual care [2] benefits of virtual care, [3] limitations of virtual care, [4] shifting stance on virtual care over time, [5] considerations for implementing virtual care. A key finding in the first theme was the perception that a high level of cohesion and togetherness between the HCPs across disciplines aided in the shift to virtual care. This finding offers an opportunity to better understand characteristics described as common features found in high-functioning healthcare teams [36,37]. Moving forward, it may be relevant for leadership/management personnel to encourage team-bonding experiences to facilitate, maintain, and improve teamwork within a multidisciplinary team for the benefit of their patients [36]. In addition, our data highlighted the flexibility and creativity that HCPs demonstrated throughout the transition from in-person care to virtual care. Providers described requiring to flexibly adapt and/or modify their assessments to adequately deliver service and address treatment goals. For example, mental health professionals described using the virtual whiteboard function in lieu of in-person sketching that would occur during in-person sessions of cognitive behavioral frameworks, as well as incorporating breakout rooms in larger treatment groups to allow for smaller group work. Richardson and Kandu’s [13] paper outlined “harnessing of creativity and innovation” as a common theme in the pediatric pain world, noting HCPs’ uptake of tools such as mobile applications, social media, blogs, podcasts, and online support groups to improve patient outreach and treatment. The second and third themes were developed to describe the benefits that HCPs believed virtual care offered (e.g., continuity of care, convenience/comfort) and its limitations to the provision of pediatric chronic pain (e.g., barriers to nonverbal observation and physical examination, privacy concerns, technology glitches, virtual fatigue/engagement challenges, and inequities in access). As described, these outcomes are consistent with many other studies exploring the delivery of pain care in the virtual space [12,13,17,33,34]. Notably, the virtual fatigue/engagement challenges identified by our participants appear to be all too common across healthcare professionals, patients, and families [19,38]. To reduce screen time throughout the traditional work/school week, HCPs in the present study noted an increase in patient/caregiver requests for afterhours appointments (e.g., weekend times). Moreover, they identified patient “Zoom fatigue” to be a precipitating factor to our fourth theme—shifting stances on virtual care. Indeed, HCPs described observing two shifts in the perceptions of virtual care. First, they observed patients’ and caregivers’ initial enthusiasm for virtual care dissipate over time, citing virtual fatigue and scheduling challenges as the main factors driving this change. As previously noted, HCPs observed an increase in weekend or afterhours appointment requests and experienced perceived frustration from patients and families when these requests could not be met. Research has suggested that matching patients’ booking requests with providers’ availability is a major barrier when creating outpatient appointments and may contribute to a reduction in patients’ perceived value of care [39,40]. Recommendations to mitigate this perception include using clear communication while reviewing patient expectations and preferences and ensuring all staff are adequately trained in effectively scheduling clinical activities [39]. The second shift observed by HCPs describes a common theme in the literature, wherein providers felt initial hesitation with the delivery virtual care, but their perspectives favorably changed over time [19,41]. HCPs report improved satisfaction with virtual care when they receive appropriate informatics training and support and when the technology itself is user-friendly [41,42]. This information is important for future virtual care implementation and is captured in our fifth theme: considerations for implementing virtual care. The last theme highlights that across disciplines, HCPs noted a preference for a future hybrid model of care delivery that incorporates both in-person care and virtual care. More specifically, and like other studies, providers described that assessing a patient in person for their initial appointment and then transitioning to virtual care would be the ideal model of care [19,22,43]. The importance of meeting family needs, such as patient and caregiver preferences and addressing travel and financial considerations while providing evidence-based health care, was also emphasized in this study. The current study provides important information on HCPs’ experiences in delivering virtual pediatric MDT chronic pain care. However, there are several limitations to this study that need to be considered. The first limitation relates to the generalizability of our findings. When considering the dynamic nature of the COVID-19 pandemic, it is important to highlight that data were collected during a specific point of time during the pandemic (e.g., February–April 2021). HCPs’ perspectives on virtual care may differ in later waves of the pandemic and recovery. Future studies may consider using longitudinal methods to investigate the changing perspectives of HCPs as the pandemic has evolved. Relatedly, all participants in this study worked within an urban Canadian tertiary care setting. Our results, therefore, may not be generalizable to providers working in either rural and remote locations or alternative ambulatory settings. Furthermore, because of the nature of the methodology (questionnaire) and the small sample size, our capacity to conduct formal statistical analyses was limited, albeit still informative. Future work may consider expanding recruitment to examine experiences associated with MDT teams on a larger scale, and they could also investigate HCPs’ experiences by using other virtual care applications, such as mobile phone applications in the treatment of pediatric chronic pain [44,45]. ## 5. Conclusions Virtual care for pediatric chronic pain has become an essential tool during the COVID-19 pandemic, and its benefits will continue to be relevant even after the pandemic has passed. In a post-COVID-19 scenario, virtual care provides accessibility, flexibility, and convenience to patients, families, and providers. Future virtual care models may look to incorporate tools such as video consultations, mobile phone applications, online educational resources, and remote monitoring devices [44,45,46]. These tools can facilitate communication between healthcare professionals and patients, allowing providers to monitor treatment progress and adjust treatment plans as needed. Virtual care also provides opportunities for caregivers to receive support and education, improving their ability to manage their child’s pain and overall wellbeing. Another consideration is the integration of virtual care into existing pediatric pain management programs, including multidisciplinary pain clinics. A recent study by the authors examined experiences of patients and caregivers who received virtual MDT for pediatric chronic pain between March 2020 and August 2021 at the pediatric chronic pain clinic where the present study was conducted. Patients and caregivers were overall satisfied with virtual care, and the most reported preference was for a hybrid model of care incorporating at least some in-person contact with providers. In light of these findings, the chronic pain clinic at which the current study was undertaken has implemented a pilot triage system by which patients are scheduled for virtual or in-person appointments on the basis of personal and provider preferences, access to technology, and specific presentations that require in-person physical exam (e.g., Complex Regional Pain Syndrome); see [27] for further details. Finally, recommendations and guidelines must be established for the safe and effective use of virtual care for the treatment of pediatric chronic pain. Consensus guidelines are needed to identify whether certain patient presentations are best seen in person and whether specific methodologies for assessment and treatment should be considered for provision of multidisciplinary pediatric pain care. Additionally, recommendations are required for optimizing the use of virtual care for both patients and providers (e.g., an appropriate confidential space, sufficient lighting), as well as ensuring virtual care resource accessibility for patients and providers (e.g., stable internet connection, suitable devices). Finally, specialized training should be considered for safety situations that may arise when providing virtual care (e.g., disclosures of abuse or suicidal ideation). Overall, this study adds depth to the emerging literature capturing HCPs experiences of delivering virtual care for pediatric chronic pain throughout the pandemic. Our mixed methods study allowed for a unique understanding of these experiences within a multidisciplinary team and underscores the value of virtual care to assess and treat pediatric chronic pain. Moving forward, it will be important to incorporate these findings with other studies that conduct similar investigations throughout the pandemic to inform the creation of evidence-based educational resources and training tools for facilitation of optimal virtual care in a post-COVID world. ## References 1. Groenewald C.B., Tham S.W., Palermo T.M.. Impaired school functioning in children with chronic pain: A national perspective. Clin. J. Pain (2020.0) 36 693-699. DOI: 10.1097/AJP.0000000000000850 2. Reid K., Simmonds M., Verrier M., Dick B.. 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--- title: Beneficial Effect of Cuban Policosanol on Blood Pressure and Serum Lipoproteins Accompanied with Lowered Glycated Hemoglobin and Enhanced High-Density Lipoprotein Functionalities in a Randomized, Placebo-Controlled, and Double-Blinded Trial with Healthy Japanese authors: - Kyung-Hyun Cho - Hyo-Seon Nam - Seung-Hee Baek - Dae-Jin Kang - Hyejee Na - Tomohiro Komatsu - Yoshinari Uehara journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048825 doi: 10.3390/ijms24065185 license: CC BY 4.0 --- # Beneficial Effect of Cuban Policosanol on Blood Pressure and Serum Lipoproteins Accompanied with Lowered Glycated Hemoglobin and Enhanced High-Density Lipoprotein Functionalities in a Randomized, Placebo-Controlled, and Double-Blinded Trial with Healthy Japanese ## Abstract This study evaluated the efficacy and safety of 20 mg of Cuban policosanol in blood pressure (BP) and lipid/lipoprotein parameters of healthy Japanese subjects via a placebo-controlled, randomized, and double-blinded human trial. After 12 weeks of consumption, the policosanol group showed significantly lower BP, glycated hemoglobin (HbA1c), and blood urea nitrogen (BUN) levels. The policosanol group also showed lower aspartate aminotransferase (AST), alanine aminotransferase (ALT), and γ-glutamyl transferase (γ-GTP) levels at week 12 than those at week 0: A decrease of up to $9\%$ ($p \leq 0.05$), $17\%$ ($p \leq 0.05$), and $15\%$ ($p \leq 0.05$) was observed, respectively. The policosanol group showed significantly higher HDL-C level and HDL-C/TC (%), approximately $9.5\%$ ($p \leq 0.001$) and $7.2\%$ ($$p \leq 0.003$$), respectively, than the placebo group and a difference in the point of time and group interaction ($p \leq 0.001$). In lipoprotein analysis, the policosanol group showed a decrease in oxidation and glycation extent in VLDL and LDL with an improvement of particle shape and morphology after 12 weeks. HDL from the policosanol group showed in vitro stronger antioxidant and in vivo anti-inflammatory abilities. In conclusion, 12 weeks of Cuban policosanolconsumption in Japanese subjects showed significant improvement in blood pressure, lipid profiles, hepatic functions, and HbA1c with enhancement of HDL functionalities. ## 1. Introduction Policosanol is a mixture of aliphatic alcohols ranging from 24–34 carbon atoms [1,2], such as octacosanol, triacontanol, dotriacontanol, hexacosanol, and tetratriacontanol, which are purified from sugar cane (Saccharum officinarum L.) wax [1,2,3] or various plants, such as oats [4], barley [5], insects [6,7], and bees wax [8]. Many policosanols from different sources have been claimed to treat blood dyslipidemia [9,10], diabetes [11], hypertension [12,13], and dementia [14,15] by raising the HDL-C level and lowering the LDL-C level. However, with the exception of Cuban policosanol [12,16,17], there is insufficient information on policosanol regarding its compositions and physiological effects on the lipoprotein metabolism, particularly in HDL functionality. In addition to the increase in HDL-C quantity, the improvement of HDL functionality should be accompanied to maximize the efficacy of policosanol. The HDL functionality in blood, such as antioxidant and anti-inflammatory properties, should be improved by policosanol consumption [18,19] since dysfunctional HDL is more atherogenic and exacerbates the proinflammatory cascade [20]. Many studies examining the molecular mechanisms for the efficacy of Cuban policosanol reported that the encapsulation of policosanol into HDL enhances the HDL functions and has anti-senescence and tissue regeneration effects by improving anti-glycation, anti-apoptosis, and cholesteryl transfer protein (CETP) inhibition [16]. CETP is an HDL-associated protein that contributes to HDL remodeling: Lowers the HDL-C and raises the triglyceride contents in HDL, which is associated with the production of dysfunctional HDL [21]. Cuban policosanol supplementation for 9 weeks in zebrafish had serum lipid-lowering and HDL-C-elevating effects via CETP inhibition [22]. Policosanol supplementation for 8 weeks also ameliorated the fatty liver changes with less production of reactive oxygen species (ROS) in zebrafish and rats [22,23]. Cuban policosanol supplementation in Korean participants increased the serum HDL-C and enhanced the HDL functionality to inhibit the oxidation and glycation of LDL and HDL [17,18]. The consumption of policosanol for 8 weeks by healthy female subjects with pre-hypertension resulted in lower blood pressure (BP) and CETP ability by elevating the HDL/apoA-I contents and enhancing the HDL functionalities, including cholesterol efflux and insulin secretion [18]. Eight weeks of policosanol supplementation in spontaneously hypertensive rats (SHR) resulted in remarkable dose-dependent decreases in BP [23]. In addition to increasing the HDL-C level after 12 weeks of consumption [12], long-term (24 weeks) policosanol consumption lowered the BP while enhancing the advantageous functions of HDL, including its antioxidant, anti-glycation, and anti-atherosclerotic activities [18]. Although many studies reported the efficacy of policosanol in humans, particularly in a Korean population, there has been no study on the effects of Cuban policosanol on the lipid parameters of a Japanese population. The Japanese population shows the unique features of a higher HDL-C level than western populations, with a higher portion of those with a genetic deficiency of CETP [24]. In a similar period between 2000 and 2002, Japanese populations showed higher HDL-C levels (~55 mg/dL) than American (United States) populations (46 mg/dL) [25,26], while Korean populations showed the lowest level of HDL-C (~43 mg/dL) [27,28]. These differences in serum HDL-C levels depend on the country. Moreover, ethnicities might influence the efficacy of policosanol in improving the lipid, lipoprotein profile, and HDL functionality. The current study examined the effect of policosanol consumption on the blood lipid parameters of a Japanese population; healthy Japanese subjects who had a normal blood pressure (BP) and normolipidemic (120 mg/dL < LDL < 160 mg/dL and >40 mg/dL of HDL) were recruited. The participants were randomized to consume 20 mg of policosanol or a placebo to compare the changes in BP, blood parameters, and lipid and lipoprotein parameters with a randomized and double-blinded test. After 12 weeks of consumption of policosanol or a placebo, blood was analyzed to assess the putative efficacy to improve the metabolic parameters of the heart, kidney, liver, or hidden toxicity. From the participants, HDL and LDL were purified individually, and the HDL functionality and LDL qualities, such as oxidized and glycated extent with antioxidant abilities, were analyzed. The antioxidant and anti-inflammatory properties of HDL were compared using zebrafish embryos by testing their developmental speed and survivability after injecting HDL in the presence of N-ε-carboxymethyllysine (CML), which is a proinflammatory and neurotoxin [29]. An elevated serum CML level is also associated with the exacerbation of atherosclerosis via lipoprotein modifications and increased susceptibility to low-density lipoproteins (LDL) oxidation [30]. Higher CML serum levels were associated with high-sensitivity C-reactive protein (CRP) via an increase in toll-like receptor 4 (TLR-4) expression in monocytes [31,32]. Zebrafish (Danio rerio) is a widely used vertebrate model to test the putative anti-inflammatory effects of drug candidates since zebrafish embryos have well-developed innate and acquired immune systems similar to the mammalian immune system [33]. An additional advantage of working with zebrafish embryos is that they develop externally and are optically transparent during development. With these characteristics, zebrafish and their embryos are a valuable and popular animal model for various studies, including inflammation [34]. The improvements of the quantity and quality of lipoproteins in blood are very important for evaluating the efficacy of policosanol or any nutraceutical to treat dyslipidemia and hypertension. In the current study, changes in the BP, lipid/lipoprotein parameters, and HDL functionalities were assessed after consuming Cuban policosanol for 12 weeks, using a randomized, double-blinded, placebo-controlled study. ## 2.1. Anthropometric and Blood Profiles As shown in Table 1, the two groups showed no difference in body mass index (BMI) and heart rate between weeks 0 and 12. The policosanol group ($$n = 30$$) showed a $7.1\%$ ($p \leq 0.001$) and 4.0 % ($$p \leq 0.034$$) decrease in systolic blood pressure (SBP) and diastolic blood pressure (DBP) at week 12, compared with week 0. On the other hand, the placebo group showed no changes in the SBP and DBP between weeks 0 and 12, despite all participants showing a normotensive range at week 0. Interestingly, the glycated hemoglobin (HbA1c) level was $4\%$ lower at week 12 than week 0 in the policosanol group ($$p \leq 0.009$$), while the placebo group showed a similar HbA1c level between weeks 0 and 12. In a group comparison during 12 weeks, the glycated hemoglobin level was $2\%$ lower in the policosanol group than in the placebo group ($$p \leq 0.024$$). These results suggest that policosanol consumption for 12 weeks can help in lowering the BP and HbA1c simultaneously without changing the BMI and heart rate. The other blood parameters (total protein, albumin, albumin/globulin (A/G) ratio, uric acid, and glucose) were relatively unaffected between weeks 0 and 12 in the policosanol and placebo groups. These results suggest that policosanol consumption did not affect the nutrient metabolism in protein, purine, and carbohydrate homeostasis. ## 2.2. Liver, Kidney, and Inflammatory Parameters At week 12, the policosanol group showed $8.7\%$ lower ($$p \leq 0.022$$) aspartate aminotransferase (AST) levels and $17.0\%$ lower ($$p \leq 0.013$$) alanine aminotransferase (ALT) levels than those of week 0. In contrast, the placebo group did not show notable changes in the enzyme levels (Table 1). Interestingly, at week 12, the policosanol group showed a $15.4\%$ ($$p \leq 0.016$$) and $6.0\%$ ($$p \leq 0.052$$) larger decrease in γ-GTP and blood urea nitrogen (BUN) than at week 0, while the placebo group did not show a notable change. The total bilirubin level of the placebo group at week 12 was $11.8\%$ higher than at week 0 ($$p \leq 0.030$$), while the policosanol group showed no change in the total bilirubin between weeks 0 and 12. These results suggest that policosanol consumption helps in the protection from liver damage and does not have liver toxicity. At week 12, the placebo group had even higher γ-GTP and BUN levels than at week 0, even though no significance was detected. The placebo group showed a $16\%$ higher BUN level than the policosanol group at week 12 ($$p \leq 0.001$$), even though they showed a similar level at week 0. In the policosanol and placebo groups, the creatinine, high sensitivity C-reactive protein (hsCRP), and lactate dehydrogenase (LDH) levels were similar at weeks 0 and 12, indicating that policosanol consumption did not affect acute inflammation and endocrinological damage in the liver and kidney. As listed in Table 1, the policosanol group at week 12 showed a $10\%$ increase in the serum apoA-I level compared with week 0, from 165 ± 2 mg/dL to 182 ± 3 mg/dL according to a paired t-test ($$p \leq 0.045$$), whereas the placebo group showed no change from 164 ± 5 mg/dL (week 0) to 160 ± 2 mg/dL (week 12). Analysis of covariance (ANCOVA) during 12 weeks revealed that the policosanol group showed $14\%$ higher apoA-I levels than the placebo group ($$p \leq 0.028$$). On the other hand, the apo-B level in both groups was unchanged during the 12 weeks, around 98–101 mg/dL. ## 2.3. Lipid and Lipoprotein Profiles After excluding the participants showing low compliance, who consumed a significantly more fat diet, heavy drinking, and smoking, during the 12-week consumption, the policosanol group ($$n = 15$$) showed $6.3\%$ higher HDL-C ($$p \leq 0.006$$) at week 12 than week 0 (Table 2). In contrast, the placebo group ($$n = 17$$) showed a $6.6\%$ decrease in the HDL-C level at week 12 from the baseline, week 0. At week 12, the policosanol group showed a $9.5\%$ higher HDL-C level ($p \leq 0.001$) than the placebo group ($$n = 17$$). Repeated measures ANOVA of HDL-C showed that the policosanol group ($$n = 15$$) showed a significant difference from the placebo group ($$n = 17$$) in the point of time and group interaction ($p \leq 0.001$) during the 12 weeks. Interestingly, the policosanol group showed an $11\%$ lower LDL-C level at week 8 ($$p \leq 0.013$$) than the placebo group. The LDL-C/HDL-C ratio was significantly lower in the policosanol group at weeks 8 ($$p \leq 0.018$$) and 12 than in the placebo group. On the other hand, the TC and TG levels in the policosanol group were relatively unaffected at weeks 0, 4, 8, and 12. Repeated measures ANOVA of TC, TG, LDL-C, and RC levels during the 12 weeks showed no difference between the policosanol group ($$n = 15$$) and the placebo group ($$n = 17$$) in terms of time and group interaction. On the other hand, at week 12, the policosanol group showed a $3\%$ lower TG/HDL-C ratio than the placebo group ($$p \leq 0.018$$) according to an analysis of covariance (ANCOVA) from baseline. The HDL-C/TC (%) ratio was increased in the policosanol group from 29.2 ± $1.1\%$ to 30.9 ± $1.4\%$ ($$p \leq 0.003$$) between weeks 0 and 12, while the placebo group showed a slight decrease from 29.7 ± $0.8\%$ to 28.8 ± $1.0\%$. At week 12, the policosanol group showed $7.2\%$ higher HDL-C/TC (%) than the placebo group ($$p \leq 0.003$$). Repeated measures ANOVA of HDL-C/TC (%) revealed a significant difference in the point of time and group interaction between the policosanol group ($$n = 15$$) and the placebo group ($$n = 17$$) ($p \leq 0.033$) during the 12 weeks, as shown in Table 2. These results suggest that HDL-C (mg/dL) and HDL-C/TC (%) were increased significantly by policosanol consumption at different times and group interactions according to repeated measures ANOVA. ## 2.4. VLDL Particle Observation and Composition Analysis As shown in Figure 1, transmitted electron microscopy (TEM) showed that the particle number of VLDL in the policosanol group decreased at week 12 compared with week 0, while the placebo group showed a larger increase in particle number (Figure 1A). After 12 weeks of consumption, the VLDL particle size decreased $27\%$ more in the policosanol group ($$p \leq 0.013$$) than at week 0, while the placebo group showed a $10\%$ increase in particle size compared with week 0 (Figure 1B and Table 3). As shown in Table 3, after 12 weeks of consumption, the policosanol group showed a $7\%$ and $63\%$ decrease in the extent of glycation (fluorescence intensity, FI) and oxidation (malondialdehyde, MDA) in VLDL, respectively, while the placebo group showed similar levels during the same period. The particle diameter of VLDL was significantly lower in the policosanol group at week 12 (~$14\%$ smaller) than at week 0, while the placebo group showed a $6\%$ increase in diameter. In the policosanol group, the TC and TG contents increased by $45\%$ and decreased by $15\%$, respectively, during the 12 weeks of consumption, whereas the placebo group showed around a $7\%$ decrease and a $2\%$ increase in the TC and TG content, respectively, in VLDL during the same period. These results suggest that policosanol consumption induced anti-atherogenic changes in the VLDL properties to exhibit lower glycation, oxidation, and TG content with a smaller particle size and number. As shown in Figure 2A, native electrophoresis ($0.5\%$ agarose) under the nondenatured state of VLDL showed that native VLDL (lane 1), which was purified from young and healthy volunteers, showed more distinct band intensity than the oxVLDL band (lane 2), which was oxidized by a cupric ion treatment (final 10 μM). The oxVLDL band almost disappeared with the fastest electromobility due to the degradation of the apo-B band and the increase in the negative charges of VLDL (lane 2, Figure 2A). VLDL under a nondenatured state revealed that the policosanol group at week 12 (lane 4) had more distinct band intensity and slower electromobility than at week 0 (lane 3), which showed a larger smear and weaker band intensity. On the other hand, the placebo group showed a similar band intensity and electromobility between weeks 0 (lane 5) and 12 (lane 6), with almost no change in the TC and TG content in VLDL. The oxVLDL (lane 2) and VLDL at week 0 (lane 3) showed faster smear band intensities (Figure 2A) since the larger TG and MDA content in VLDL caused a larger smear band intensity with faster electromobility. As shown in Figure 2B, quantification of oxidized species in VLDL showed that oxVLDL had the highest MDA level of 26 ± 3 μM MDA, while the native VLDL showed 7 ± 3 μM MDA. At week 0, both groups showed a similar level of MDA in VLDL around 12–15 μM MDA, as shown in Figure 2B. On the other hand, at week 12, the policosanol group (5.5 ± 1.5 μM MDA) showed $63\%$ lower MDA levels than at week 0 ($$p \leq 0.041$$) and $49\%$ lower MDA levels than the placebo group (10.6 ± 4.2 μM MDA). These results suggest that policosanol consumption caused a decrease in the TG and MDA content in VLDL with a smaller particle size (Figure 1 and Table 3). ## 2.5. LDL Particle Observation and Composition Analysis TEM showed a $5\%$ increase in LDL particle size (531 ± 8 nm2, $$p \leq 0.030$$) in the policosanol group at week 12, with a more distinct particle shape and morphology (Figure 3). On the other hand, the placebo group showed a $4\%$ decrease in particle size (492 ± 10 nm2, $$p \leq 0.272$$) with a similar particle morphology at week 12 compared with week 0. The LDL particle diameter at week 12 in the policosanol group was $6\%$ higher than at week 0, while the placebo group did not show a notable change (Table 3). As shown in Table 3, the extent of LDL glycation was $12\%$ lower in the policosanol group at 12 weeks, but the change was not significant ($$p \leq 0.082$$), while the placebo group showed an even $3\%$ higher extent of glycation at week 12 than week 0. The policosanol group showed a $12\%$ higher TC content and $9\%$ lower TG content than at week 0, but the change was not significant. In contrast, the placebo group at week 12 showed a $20\%$ lower TC level and a $2\%$ higher TG level than at week 0. These results suggest that policosanol consumption caused an increase in the LDL particle size and cholesterol content with a decrease in the TG content, glycation extent, and oxidation extent. ## 2.6. Electromobility of LDL and Oxidation Extent A comparison of the LDL electromobility under the nondenatured state (Figure 4A) showed that the LDL from the policosanol group at week 12 (lane 1) showed slower electromobility than week 0 (lane 2) with a stronger band intensity. In contrast, the control group at week 12 (lane 4) showed a faster electromobility and larger smear band intensity than at week 0. Native LDL (lane 5) showed the strongest band intensity with the slowest electromobility, whereas oxidized LDL (lane 6) showed the weakest band intensity with the fastest electromobility to the bottom of the gel. The more oxidized LDL exhibited faster electromobility to the bottom of the gel due to apo-B fragmentation and an increased negative charge in LDL. Native LDL, which was purified from a young and healthy control, showed 0.3 μM MDA. In contrast, the oxidized LDL by the cupric ion treatment (final 1 μM) showed the highest MDA level, around 1.3 μM, as shown in Figure 4B. The policosanol group at week 12 showed a $38\%$ decrease in oxidation extent than at week 0 ($$p \leq 0.004$$), while the placebo group showed no significant change in the oxidation extent. ## 2.7. Change in apoA-I Contents in HDL2 and HDL3 SDS-PAGE analysis of HDL2 (2 mg/mL) and HDL3 (2 mg/mL) showed that the policosanol group at week 12 exhibited higher apoA-I expression, which increased in an intensity-dependent manner: 1.22-fold and 1.15-fold higher band intensities of apoA-I in HDL2 (lane 2) and HDL3 (lane 6), respectively, than at week 0, as shown in Figure 5A. The placebo group at week 12 showed a decrease in the apoA-I band intensities (lanes 4 and 8): $18\%$ and $55\%$ lower than at week 0. The increase in apoA-I in the policosanol group showed a good agreement with the increase in serum HDL-C (mg/dL) and HDL-C/TC (%), as shown in Table 2. Repeated measures ANOVA with serum HDL-C ($p \leq 0.001$, Figure 5B) and HDL-C/TC (%) ($$p \leq 0.033$$, Figure 5C) revealed a significant difference in the point of time and group interaction between the policosanol and the placebo groups. During the 12 weeks, the policosanol group showed a gradual increase in HDL-C ($p \leq 0.001$) and HDL-C/TC (%) ($$p \leq 0.003$$) with significance according to ANCOVA from the baseline, as shown in Figure 5B and Figure 5C, respectively. ## 2.8. Paraoxonase Activities in HDL2 and HDL3 As shown in Figure 6, the HDL-associated paraoxonase (PON-1) assay at the same protein concentration (2 mg/mL) showed that the policosanol group at week 12 showed the highest PON-1 activity in both HDL2 and HDL3, approximately 59 and 49 μU/L/min, while the HDL2 and HDL3 at week 0 was approximately 38–42 μU/L/min. The policosanol group showed a 1.4-fold higher HDL2 level at week 12 than at week 0 ($$p \leq 0.003$$), whereas the placebo group showed no change between weeks 0 and 12 (Figure 6A). The policosanol group showed a 1.3-fold higher PON-1 level of HDL3 at week 12 than at week 0 ($$p \leq 0.008$$), while the placebo group showed no change between weeks 0 and 12 (Figure 6B). These results suggest that policosanol consumption is linked with the specific elevation of the paraoxonase activity in both HDL2 and HDL3. ## 2.9. Ferric Ion Reduction Ability of HDL2 and HDL3 At the same protein concentration (2 mg/mL), there was no difference in the ferric ion reduction ability (FRA) for HDL2 between weeks 0 and 12 in the policosanol and placebo group: ~63–70 μM of ferrous ion equivalents (Figure 7A). On the other hand, the policosanol group at week 12 showed a 1.6-fold higher HDL3 level than at week 0 ($$p \leq 0.043$$), whereas the placebo group showed no change between weeks 0 and 12: 41–43 μM of ferrous ion equivalents, as shown in Figure 7B. These results suggest that policosanol consumption is linked with the specific elevation of HDL3 associated with the ferric ion reduction ability. ## 2.10. Embryo Survivability As shown in Figure 8A, microinjection of CML (final 500 ng) into zebrafish embryos resulted in acute embryo death with up to 29 ± $3\%$ survivability, while the PBS-alone injected embryo showed 81 ± $3\%$ survivability. In the presence of CML, an injection of HDL2 from the policosanol group at week 12 resulted in the highest survivability of the injected embryo, ~75 ± $4\%$ ($p \leq 0.005$ versus CML alone), whereas HDL2 from the policosanol group at week 0 showed 55 ± $4\%$ survivability. On the other hand, the HDL2 from the placebo-injected embryo group showed similar survivability at week 0 or 12, 50–$54\%$ after 24 h post-injection. The HDL2 from the placebo group (weeks 0 and 12) and the policosanol group at week 0 showed protective activity against the CML-induced inflammatory death ($p \leq 0.05$ vs. CML alone), indicating that all the HDL showed a good anti-inflammatory activity. After 12 weeks of consumption, the HDL2 in the policosanol group showed a 1.5-fold higher survivability than the placebo group. As shown in Figure 8B, the CML alone injected embryo (photo b) showed acute death, as indicated by the red arrowhead, and severe defect of development morphology, as indicated by the blue arrowhead with the slowest eye pigmentation and tail elongation. On the other hand, a co-injection of HDL2 ameliorated the acute death. Particularly. in HDL2 from the policosanol group at week 12, the most normal developmental speed and morphology was shown. Acridine orange staining showed that CML-alone injected embryos with 3.2-fold higher green fluorescence (Figure 8C), indicating that the CML injection caused apoptosis in embryos. In contrast, co-injection of HDL2 from the policosanol group at week 12 decreased the apoptosis by up to $52\%$ reduction than the CML alone group. The CML-alone injected embryo also showed 3.4-fold higher red fluorescence than the PBS-alone group from the DHE staining, indicating that the apoptosis was accompanied by ROS production. A co-injection of HDL2 from the policosanol group at week 12 decreased the ROS amount by $63\%$ compared with the CML-alone group. These results suggest that policosanol consumption enhanced the antioxidant and anti-inflammatory activity in HDL to result in an elevation of embryo survivability in the presence of CML. ## 3. Discussion Cuban policosanol supplementation in Korean participants increased the serum HDL-C level and enhanced the HDL functionality to inhibit the oxidation and glycation of LDL and HDL [18]. The consumption of policosanol for 8 weeks by healthy female subjects with pre-hypertension resulted in a lower blood pressure and CETP ability by elevating the HDL/apoA-I contents and enhancing the HDL functionalities, including cholesterol efflux and insulin secretion [19]. Twelve weeks of Cuban policosanol consumption (10 and 20 mg) was associated with improved blood pressure and lipid/lipoprotein profile, such as lowering TC and LDL-C with increasing HDL-C [12,18,19]. The HDL particle quality and functionality were also enhanced by encapsulation into reconstituted HDL [16] by reducing hepatic inflammation in hyperlipidemic zebrafish [22] and spontaneously hypertensive rats [23]. In human studies, the increases in HDL-C quantity, HDL quality, and functionality were enhanced by policosanol consumption for 8 [17,19], 12 [12], and 24 weeks [18]. Many studies were carried out with different policosanol doses in different countries and ethnic populations, such as Caucasian [35,36], Cuban [37,38], and Chinese [39,40], to result in a variety of efficacies [13,41]. Interestingly, contradictory reports on the efficacy of policosanol depend on the policosanol origin and study design with healthy subjects or patients. Berthold’s group showed that policosanol is a promising phytochemical alternative for lipid reduction [35]. On the other hand, the same group later reported that policosanol consumption had no lipid-lowering effect during a 12-week study in 143 hyperlipidemic patients [36]. At the baseline of the study, however, all the participants (mean age: 56 ± 12 years old, body mass index = 27.2 ± 3.6) were patients with hypercholesterolemia who quit statin 6 weeks ago. They showed uncontrollably high serum LDL-C and TC of approximately 187 ± 36 mg/dL and 282 ± 42 mg/dL, respectively, with an additional one or two more risk factors, such as hypertension, dyslipidemia, obesity, and cigarette smoking. The patients showed lower HDL-C levels, less than 35 mg/dL, with current cigarette smoking, more than 10 cigarettes per day. In contrast, all participants in the current study were healthy subjects (52.8 ± 1.2 years old, BMI = 22.1 ± 0.4) with normotension and a normal serum lipid level around 220 ± 3 mg/dL of TC, 138 ± 4 mg/dL of LDL-C, and 64 ± 2 mg/dL of HDL-C at the baseline. On the other hand, with the exception of Cuban policosanol [12,16,17,18], there is insufficient information on lipoprotein metabolism regarding its physiological effects, particularly in HDL functionality. Moreover, no clinical study of Cuban policosanol with Japanese populations has been conducted to test the improved blood pressure and lipid profile in healthy subjects and adverse effects. To the best of the authors’ knowledge, this study is the first to show that Cuban policosanol has efficacy in middle-aged healthy Japanese participants, who had borderline TC and LDL-C levels but high HDL-C levels, to improve dyslipidemia and blood pressure by increasing the HDL-C quantity and enhancing the HDL quality without adverse effects. After 12 weeks of Cuban policosanol consumption, the systolic and diastolic BP were reduced significantly but still in the normal range of up to $7\%$ ($p \leq 0.001$ vs. week 0) and $4\%$ ($$p \leq 0.034$$) with a significant decrease in glycated hemoglobin from the initial level. Elevated glycated hemoglobin (HbA1c) levels were associated with a high risk of hypertension with lowered serum HDL-C [42]. The glycation of blood proteins, such as hemoglobin, albumin, LDL, and HDL, is linked to the exacerbation of hypertension via oxidative stress and an inflammatory process of advanced glycation end products to form atherosclerotic plaque and cause aortic stiffness. An reconstituted HDL (rHDL) containing policosanol exhibited inhibitory activity on in vitro fructose-mediated glycation [16,43]. Short-term (8 weeks) or long-term (24 weeks) policosanol consumption resulted in a decrease in the in vivo glycation in HDL and LDL [17,18]. In the same context, a meta-analysis also showed that policosanol supplementation improved hypertension and dyslipidemia [13,44]. The policosanol group at week 12 showed a remarkably lower extent of glycation and oxidation in VLDL and LDL, while the placebo group showed an increase in glycation extent after 12 weeks of consumption (Table 3). These decreases in VLDL/LDL glycation are linked with the decrease in HbA1c in the policosanol group at week 12 (Table 1) and the smear band intensity of LDL in the policosanol group at week 0 and placebo group at week 12, as shown in Figure 4 (lanes 1 and 4). The hepatic functions were also improved remarkably by the policosanol consumption with lower serum AST, ALT, γ-GTP, and BUN levels (Table 1), but the mechanism is still unclear. The accumulation of the CML and CML-related inflammatory response in steatotic livers may play an essential role in hepatic steatosis and the pathogenesis of non-alcoholic fatty liver disease [45,46]. The nonenzymatic glycation of proteins is associated with the production of advanced glycation end products (AGE), such as CML, which is proinflammatory and toxic to hepatocytes. Therefore, inhibiting glycation by policosanol consumption may help in improving the hepatic functions. Adding CML to HDL for 48 h increased the production of yellowish glycated fluorescence with proteolytic degradation of apoA-I and the loss of paraoxonase activity [47]. In the current study, however, consumption of 20 mg of policosanol in Japanese participants caused an elevation of apoA-I expression (Table 2), which concurs with a previous report that apoA-I expression increased and multimerization decreased in Korean participants [18]. In the current study, the significantly reduced extent of glycation in hemoglobin and VLDL/LDL agreed with previous reports [16,43] showing that Cuban policosanol-encapsulated rHDL exhibited potent anti-glycation activity against fructose-mediated glycation of HDL and apoA-I. Recently, an rHDL containing Cuban policosanol exerted anti-glycation activity to prevent proteolytic degradation of HDL and apoA-I, while rHDL containing Chinese policosanol did not show anti-glycation activity [48]. The CML also exhibited neurotoxicity and embryotoxicity in adult zebrafish and its embryo [47,48]. A microinjection of CML (final 500 ng) into zebrafish embryos caused acute embryo death, severe developmental defects, and the slowest developmental speed at 24 h post-injection [47,48]. In the current study, a co-injection of HDL2 from the policosanol group at week 12 showed the highest protection ability against embryonic death by neutralizing CML toxicity (Figure 8). In contrast, the HDL2 from the placebo group had adequate protection ability. To the best of the authors’ knowledge, these findings are the first to show that a co-injection of higher-quality HDL can neutralize CML toxicity. The enhanced HDL quality from policosanol consumption induced the highest survivability (~75 ± $4\%$) by neutralizing CML toxicity. Nevertheless, the other HDL showed sufficient protection ability with 50–$54\%$ survivability compared with 29 ± $3\%$ with CML alone. Regarding the mechanism of action, policosanol consumption resulted in an increase in HDL-C and apoA-I content in HDL via CETP inhibition, as reported elsewhere [16,17]. Cuban policosanol consumption (10–20 mg/day) resulted in a remarkable decrease in LDL oxidation and HDL glycation in healthy subjects with pre-hypertension [18]. In vitro studies showed that rHDL containing Cuban policosanol exerted potent antioxidant, anti-glycation, and anti-inflammatory activities [19,43,48] with cholesterol efflux ability [19]. The binding ability of policosanol with apoA-I for discoidal rHDL formation is crucial for exerting physiological activities by maximizing the pluripotent functionality of HDL to prevent atherosclerosis, dyslipidemia, hypertension, and dementia [49]. Since policosanol consists of long-chain aliphatic alcohols, which are extremely hydrophobic, each chain of the long-chain alcohols should be incorporated with a vesicle, such as a lipoprotein, after intake. An rHDL containing Cuban policosanol (Raydel®) showed a larger particle size and more particle numbers than other rHDLs containing Chinese policosanol. The rHDL-containing Cuban policosanol displayed potent anti-glycation activity to protect apoA-I and antioxidant activity to protect LDL, as reported recently [48]. The in vitro potentials of policosanol to enhance HDL functionality are linked with the in vivo efficacy in a human clinical study to improve blood pressure and dyslipidemia [12]. As summarized in Figure 9, policosanol (Raydel®) consumption 20 mg/day caused the simultaneous inhibition of glycation in hemoglobin (Table 1), improved the hepatic functions (Table 1), and improved the lipid profiles by raising the HDL-C levels and lowering the LDL-C/HDL-C ratio (Table 2). Consequently, improved HDL antioxidant ability (Figure 6 and Figure 7) and anti-inflammatory ability (Figure 8) were associated with a decrease in oxidation, glycation, and slower electromobility of VLDL and LDL (Table 3, Figure 2 and Figure 4). A limitation of this study was that the data on lifestyle, such as diet consumption, smoking, and alcohol drinking frequency, exercise intensity, and time were obtained from self-reported questionnaires. The validation of these data for diet, smoking, drinking, and exercise might be intricate for distinguishing the consumption of policosanol or placebo effect. In addition, methodological and practical difficulties due to space limitation in rotor for ultracentrifugation were related with the selection of participants. To achieve more reliable and accurate results, the four steps of ultracentrifugation for a total of 96 h and subsequent dialysis for each 24 h, from VLDL, LDL, HDL2, to HDL3 should be carried out in the same batch, simultaneously. Antioxidant abilities, PON-1 and FRA, and anti-inflammatory activities are very sensitive to change during the purification step. However, since the rotor contains a maximum of 44 holes, we had to select blood samples of 15 samples from the policosanol group, 17 samples from the placebo group, 10 samples from the young control group for the simultaneous ultracentrifugation. These low sample sizes ($$n = 32$$) for lipoprotein analysis could be a limitation of the current study. Another concern was the unequal distribution of menopausal women between the groups; the policosanol (Female $$n = 7$$) and placebo group (Female $$n = 8$$) contained four and seven post-menopausal women, respectively. These unequal distributions of menopausal status between groups might interfere with the interpretation of the current results since menopausal women displayed more atherogenic lipid and lipoprotein profiles with increased dysfunctional HDL [35]. In a future study, the detailed properties of HDL, particle size, and compositions from each participant should be investigated to observe trends in different parameters between the groups. Therefore, it will be important to know which parameters are more influential to the HDL quality and functionality in vivo across the participants by the policosanol consumption. In conclusion, 12 weeks of Cuban policosanol (Raydel®) consumption resulted in a significant decrease in BP and glycated hemoglobin with improvements of the hepatic parameters via lowered oxidation and glycation of VLDL and LDL through enhanced HDL functionalities with a higher apoA-I content. Policosanol consumption enhanced the HDL functionalities on the in vitro antioxidant abilities (PON and FRA) and anti-inflammatory activities in the zebrafish embryos to protect against acute death in the presence of CML, a proinflammatory neurotoxin. ## 4.1. Policosanol Raydel® policosanol tablet (two tablets of 10 mg, total 20 mg per day) was obtained from Raydel Japan (Tokyo, Japan), which was manufactured with Cuban policosanol at Raydel Australia (Thornleigh, Sydney). Cuban policosanol was defined as genuine policosanol with a specific ratio of each ingredient [50]: 1-Tetracosanol (C24H49OH, 0.1–20 mg/g); 1-hexacosanol (C26H53OH, 30.0–100.0 mg/g); 1-heptacosanol (C27H55OH, 1.0–30.0 mg/g); 1-octacosanol (C28H57OH, 600.0–700.0 mg/g); 1-nonacosanol (C29H59OH, 1.0–20.0 mg/g); 1-triacontanol (C30H61OH, 100.0–150.0 mg/g); 1-dotriacontanol (C32H65OH, 50.0–100.0 mg/g); 1-tetratriacontanol (C34H69OH, 1.0–50.0 mg/g). ## 4.2. Participants Healthy male and female volunteers with normal lipid levels and normal blood pressure were recruited nationwide in Japan via newspaper and internet advertisements between September 2021 and May 2022. The inclusion criteria were LDL-C levels in the normal range (120–160 mg/dL) and age between 20 and 65 years old. The exclusion criteria were as follows: [1] Maintenance treatment for metabolic disorder, including dyslipidemia, hypertension, and diabetes; [2] severe hepatic, renal, cardiac, respiratory, endocrinological, and metabolic disorder disease; [3] allergies; [4] heavy drinkers, more than 30 g of alcohol per day; [5] taking medicine or functional food products that may affect the lipid metabolism, including raising HDL-C or lowering LDL-C concentration, and lowering triglyceride concentration; [6] current and past smoker; [7] women in pregnancy, lactation, or planning to become pregnant during the study period; [8] person who had more than 200 mL of blood donation within 1 month or 400 mL of blood within 3 months before starting this clinical trial; [9] a person who participated in other clinical trials within the last 3 months or currently is participating in another clinical trial; [10] those who consumed more 2000 kcal per day; [11] others considered unsuitable for the study at the discretion of the principal investigator. The study was approved by the Koseikai Fukuda Internal Medicine Clinic (Osaka, Japan, IRB approval number 15000074, approval date on 18 September 2021). ## 4.3. Study Design This study was a double-blinded, randomized, and placebo-controlled trial with a 12-week treatment period. After an initial screening, 72 subjects (Male 36, Female 36) with 120 mg/dL ≤ LDL <160 mg/dL were selected as shown in Figure 10. After allocating the participants into two groups, they were directed to take two tablets per day containing policosanol 10 mg (Raydel®) or a placebo. The tablet for the policosanol group included policosanol (10 mg), hydroxypropyl cellulose, carboxymethyl cellulose, maltodextrin, lactose, and crystalline cellulose. The tablet for the placebo group contained maltodextrin (10 mg) rather than policosanol. All participants received advice to avoid excess food (<1800 and 1500 kcal for men and women, respectively, per day), cholesterol (<600 mg per day), and alcohol drinking (<30 and <15 g of ethanol for men and women, respectively, per day), and no smoking both direct and indirect, which can interfere with the lipoprotein metabolism. They were also instructed to avoid intense exercise (<30 min daily at 60–$80\%$ maximum capacity). After 12 weeks of consumption, the blood parameters of all participants who completed the program were analyzed. Then, the lipid and lipoprotein parameters were analyzed after excluding those who violated dietary and exercise guidelines, such as omitting daily consumption, overeating, a significantly more fat diet, smoking, and heavy drinking, and failed the other exclusion criteria after stratified analysis based on the self-reported questionnaire. ## 4.4. Anthropometric Analysis The blood pressure was measured using an Omron HEM-907 (Kyoto, Japan) with a total of three times of measurements, and the average was recorded. The height, body weight, and body mass index (BMI) were measured individually using a DST-210N (Muratec KDS Co., Ltd., Kyoto, Japan). ## 4.5. Blood Analysis After fasting overnight, blood samples were collected in ethylenediaminetetraacetic acid (EDTA)-coated tubes and centrifuged at 3000× g for 15 min at 4 °C for the plasma assays. The samples were subjected to 19 blood biochemical assays by BML Inc. (Tokyo, Japan): Total protein, albumin, albumin and globulin ratio, aspartate transferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (γ-GTP), creatinine, glucose, uric acid, blood urea nitrogen (BUN), lactate dehydrogenase (LDH), total bilirubin, glycated hemoglobin (hemoglobin A1c, HbA1c), high sensitivity C-reactive protein (hsCRP), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (apo-B), and apolipoprotein A-I (apoA-I). ## 4.6. Isolation of Lipoproteins and Quantification Very low-density lipoproteins (VLDL, d < 1.019 g/mL), LDL (1.019 < d < 1.063), HDL2 (1.063 < d < 1.125), and HDL3 (1.125 < d < 1.225) were isolated from individual subject sera via sequential ultracentrifugation for 96 h [51], with the density adjusted by adding NaCl and NaBr according to standard protocols [52]. As a control serum for native VLDL and LDL, the blood from young and healthy human males ($$n = 10$$, mean age, 23 ± 2 years old) was donated voluntarily after fasting overnight. The samples were centrifuged for 24 h for each lipoprotein fraction at 10 °C at 100,000× g using an Himac NX (Hitachi, Tokyo, Japan) equipped with a fixed angle rotor P50AT4-0124 at the Raydel Research Institute (Daegu, Korea). After centrifugation, each lipoprotein sample was dialyzed extensively against Tris-buffered saline (TBS; 10 mM Tris-HCl, 140 mM NaCl, and 5 mM ethylene-diamine-tetraacetic acid (EDTA) [pH 8.0]) for 24 h to remove the NaBr. For each lipoprotein purified individually, the total cholesterol (TC) and TG levels were measured using commercially available kits (cholesterol, T-CHO, and TG, Cleantech TS-S; Wako Pure Chemical, Osaka, Japan). The protein concentrations of the lipoproteins were determined using a Lowry protein assay, as modified by Markwell et al. [ 53], using the Bradford assay reagent (Bio-Rad, Seoul, South Korea) with bovine serum albumin (BSA) as the standard. ## 4.7. Quantification of Oxidation Extent in VLDL and LDL The degree of oxidation of the individual VLDL (0.5 mg/mL of protein) and LDL (1.0 mg/mL of protein) was assessed by measuring the concentration of oxidized species in the lipoproteins using the thiobarbituric acid reactive substances (TBARS) method with malondialdehyde (MDA) as a standard [54]. The relative electrophoretic mobility depends on the intact charge and three-dimensional structure of VLDL and LDL. ## 4.8. Oxidation of VLDL and LDL Oxidized VLDL (oxVLDL) and LDL (oxLDL) were produced by incubating the native VLDL (0.5 mg/mL of protein) or LDL fraction (1.0 mg/mL of protein), which was purified from young and healthy males, with CuSO4 (Sigma # 451657) at a final concentration of 10 and 1 μM for VLDL and LDL, respectively, for 4 h at 37 °C. The oxVLDL and oxLDL were then filtered (0.22 μm filter) and analyzed using a thiobarbituric acid reactive substance (TBARS) assay to determine the extent of oxidation with malondialdehyde (MDA, Sigma # 63287) standard, as described elsewhere [54]. ## 4.9. Agarose Electrophoresis The relative electromobility of the VLDL and LDL (5 μg of protein) was compared under a non-natured state on $0.5\%$ agarose gel (120 mm length × 60 mm width × 5 mm thickness). The electrophoresis was carried out with 50 V for 1 h in Tris-acetate-EDTA buffer (pH 8.0), as described previously [55]. The apo-B in VLDL and LDL were visualized by Coomassie brilliant blue staining (final $1.25\%$). More oxidized VLDL and LDL were moved faster to the bottom of the gel due to apo-B fragmentation and the increase in negative charge. ## 4.10. Electron Microscopy Transmission electron microscopy (TEM, Hitachi H-7800; Ibaraki, Japan) at the Raydel Research Institute (Daegu, Korea) was performed at an acceleration voltage of 80 kV. VLDL and LDL were stained negatively with $1\%$ sodium phosphotungstate (PTA; pH 7.4) with a final apolipoprotein concentration of 0.3 mg/mL in TBS. Five μL of the lipoprotein suspension was blotted with filter paper and replaced immediately with a 5 μL droplet of $1\%$ PTA. After a few seconds, the stained lipoprotein fraction was blotted onto a Formvar carbon-coated 300 mesh copper grid and air-dried. The shape and size of the LDL were determined by TEM at 40,000× magnification, according to a previous report [56]. ## 4.11. Paraoxonase Assay The paraoxonase-1 (PON-1) activity in HDL2 and HDL3 toward paraoxon was determined by evaluating the hydrolysis of paraoxon into p-nitrophenol and diethylphosphate, which was catalyzed by the enzyme [57]. The PON-1 activity was then determined by measuring the initial velocity of p-nitrophenol production at 37 °C, as determined by measuring the absorbance at 415 nm (microplate reader, Bio-Rad model 680; Bio-Rad, Hercules, CA, USA). ## 4.12. Ferric Ion Reduction Ability Assay The ferric ion reduction ability (FRA) was determined using the method reported by Benzie and Strain [58]. Briefly, the FRA reagents were freshly prepared by mixing 20 mL of 0.2 M acetate buffer (pH 3.6), 2.5 mL of 10 mM 2,4,6-tripyridyl-S-triazine (Fluka Chemicals, Buchs, Switzerland), and 2.5 mL of 20 mM FeCl3∙6H2O. The antioxidant activities of HDL (2 mg/mL) were estimated by measuring the increase in absorbance induced by the ferrous ions generated. Freshly prepared FRA reagent (300 μL) was mixed with HDL2 (2 mg/mL) and HDL3 (2 mg/mL) as an antioxidant source. The FRA was determined by measuring the absorbance at 593 nm every 2 min over a 60 min period at 25 °C using a UV-2600i spectrophotometer. ## 4.13. Electrophoretic Patterns of HDL The relative compositions of the apolipoproteins and band intensity of apoA-I in HDL2 and HDL3 were compared using $12\%$ SDS-PAGE in the denatured state. The gels were then stained with $0.125\%$ Coomassie Brilliant Blue, after which the relative band intensities were compared by band scanning using Gel Doc® XR (Bio-Rad) with Quantity One software (version 4.5.2) and Image J software (http://rsb.info.nih.gov/ij/, accessed on 15 December 2022). ## 4.14. Data Analysis All analyses in Table 1, Table 2 and Table 3 were normalized using a homogeneity test of the variances through Levene’s statistics. Nonparametric statistics were performed using the Kruskal–Wallis test if not normalized. For Table 1, comparisons between the policosanol and placebo with respect to all assessments were analyzed using an analysis of covariance (ANCOVA) with the independent variable as the baseline and treatment. For Table 2, repeated measure ANOVA was used for comparison of the score changes between the two groups during the same period. The differences in the placebo or policosanol group over the follow-up time were analyzed to compare the point of time and group interaction. For Table 3, significant changes between the baseline and follow-up values within groups were assessed using a paired t-test. Statistical power was estimated using the program GPower 3.1.9.7 (GPower from University of Düsseldorf, Düsseldorf, Germany). All tests were two-tailed, and the statistical significance was $p \leq 0.05.$ Data were analyzed using the SPSS software version 29.0 (IBM, Chicago, IL, USA). ## References 1. Arruzazabala M.d.L., Carbajal D., Mas R., Garcia M., Fraga V.. Effects of policosanol on platelet aggregation in rats. Thromb. Res. (1993) 69 321-327. DOI: 10.1016/0049-3848(93)90030-R 2. Batista J., Stüsser R., Saez F., Perez B.. 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--- title: Brazilian Immigrant Parents’ Preferences for Content and Intervention Modalities for the Design of a Family-Based Intervention to Promote Their Preschool-Age Children’s Healthful Energy Balance-Related Behaviors authors: - Thaís Vilasboas - Qun Le - Mary L. Greaney - Ana Cristina Lindsay journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048827 doi: 10.3390/ijerph20064817 license: CC BY 4.0 --- # Brazilian Immigrant Parents’ Preferences for Content and Intervention Modalities for the Design of a Family-Based Intervention to Promote Their Preschool-Age Children’s Healthful Energy Balance-Related Behaviors ## Abstract Brazilians are a rapidly growing ethnic immigrant population in the United States (U.S.), and there is a lack of childhood obesity prevention interventions addressing the needs of Brazilian preschool-age children. Using the family ecological model (FEM) as a guide, this developmental cross-sectional study assessed the preferences (content, intervention modality, and language) of 52 individual Brazilian immigrant parents (27 mothers, 25 fathers) for a family-based intervention to promote healthful energy balance-related behaviors (EBRB). Overall, $85\%$ or more of parents reported being interested or very interested in content related to five of the seven assessed EBRBs (increasing fruits and vegetables, reducing unhealthy foods and sugar-sweetened beverages, increasing physical activity, and reducing screen time). Parent-preferred intervention modalities were group sessions delivered by community health workers (CHWs, $86.5\%$), email ($84.6\%$), and messaging ($78.8\%$), with most parents ($71.2\%$) indicating a preference for content in Portuguese. Interventions integrating multiple components, such as group sessions offered by CHWs and text messaging using SMS and WhatsApp, should be considered. Future steps for intervention development should include investigating different communication channels and their integration into a culturally and linguistically tailored family-based intervention designed to promote healthful EBRBs of preschool-age children in Brazilian families living in the U.S. ## 1. Introduction Childhood obesity is a complex public health problem disproportionally affecting ethnic minority children in immigrant families [1,2]. Promoting healthful energy balance-related behaviors (EBRBs) in young children, a fundamental obesity preventative strategy, is critical for addressing health disparities in childhood obesity among ethnic minority and immigrant populations [3,4,5]. Accumulating evidence indicates that unhealthful EBRBs, including unhealthy eating behaviors, such as high consumption of unhealthy foods and beverages high in sugar, also known as sugar-sweetened beverages [SSB], inadequate physical activity (PA), excessive sedentary behavior/screen time, and short sleep duration increase the risk of child obesity [4,5,6,7]. Early childhood is a critical period for developing health habits and presents a unique opportunity for early interventions to support children in developing healthful EBRBs [8,9]. Evidence suggests parents’ critical and unique role in promoting and maintaining health and preventing diseases [10,11]. Parents play a central role in the family as primary caregivers, primarily responsible for their children’s nutrition and PA patterns, particularly in early childhood [10,12,13,14]. Consequently, parents should be considered important forces for change in their children’s behaviors [9,15]. Parents are influential in their children developing and maintaining life-long healthful behaviors through their modeling, parenting practices, and routines they establish in the home environment [9]. Therefore, supporting parents in developing the skills to engage in healthful parenting practices and creating a family environment that promotes healthful EBRBs is critical to preventing childhood obesity [5,8,16]. Research indicates that parents, including racial/ethnic minority parents, want to learn how to support their young children in developing healthful EBRBs [8,17,18]. Hence, identifying parents’ preferences (content and modalities) for interventions to promote healthful EBRBs is essential for designing effective family-based interventions that address parents’ specific needs. Most available family-based interventions designed to promote healthful EBRBs and prevent childhood obesity have focused on mothers [19,20]. Although mothers are often the primary parent providing care for their children, fathers also play an influential role in parenting practices and household routines that influence young children’s EBRBs [8,20]. Previous research suggests that Latino fathers in the United States (U.S.), including immigrant fathers, believe it is essential for their children to develop healthful behaviors during childhood [21,22]. Therefore, they try to model and help their children develop healthful behaviors [22,23,24,25,26]. However, research also suggests that Latino fathers may contribute to the children participating in unhealthful EBRBs, such as screen time and consuming excessive unhealthy snacks [21,22,27,28]. For example, recent research found that Latina mothers perceive fathers as negatively influencing children’s EBRBs by bringing high-calorie foods, such as pizza and SSB, home and using sweets and savory foods to reward children for positive behaviors [21,22]. Brazilians are a rapidly growing ethnic minority immigrant population in the U.S., now home to the largest population of Brazilians outside of Brazil [29]. According to the American Community Survey (ACS) data, which provides the best estimate of demographic, economic, and social characteristics of Brazilians in the U.S., about 710,000 Brazilians, including those born in Brazil and their U.S.-born descendants, resided in the United States in 2019 [29]. Nonetheless, this count may underestimate the number of Brazilians in the U.S., due to the large number of Brazilians who are undocumented [29]. Massachusetts is the state with the second largest Brazilian population in the U.S., behind only Florida. Brazilians are categorized as Latinos in national datasets (e.g., U.S. Census); hence, data specific to this population in the U.S. is limited [29]. Although Brazilians share many cultural characteristics with other Latino populations (e.g., personalism, familism, machismo, Catholicism official religion, etc.), Brazilians have distinctive characteristics, including speaking Portuguese, a very important cultural difference between Brazilians and Spanish-speaking Hispanic groups [30,31]. As a result, many Brazilians living in the U.S. do not view themselves as Hispanic, as they speak a different language (Portuguese). They also have different cultural origins (Portuguese, African, and Indigenous) that make them distinct from other Hispanic groups [30,31]. Hence, there is an urgent need to develop interventions to promote EBRBs that meet the specific needs of this unique and growing immigrant population in the U.S. It also is important to assess if mothers and fathers have the same preferences for interventions. This will ensure the design of salient interventions for both Brazilian immigrant mothers and fathers. To date, no interventions promoting healthful EBRBs to prevent childhood obesity have addressed the specific needs of Brazilian immigrants in the U.S. Therefore, the present descriptive study was conducted as formative research to assess Brazilian immigrant parents’ preferences for informational content, delivery modality, and access and use of communication technology for the development of a family-based intervention to promote healthful EBRBs and prevent child obesity [32,33,34,35,36]. ## 2.1. Theoretical Model Using the family ecological model (FEM) [37], this developmental [32,33,34,35,36] cross-sectional pilot study [31] was conducted to inform the design of a family-based early childhood obesity prevention intervention. The FEM builds on the ecological system theory (EST), which posits that human behavior cannot be understood without considering the contexts in which it occurs [38]. The EST emphasizes the individual as a focal intervention point while considering the broader social context of influences [38]. The FEM was developed to emphasize the importance of focusing on the family—rather than the individual—as the focal point of the intervention target. The inner circle summarizes how parents influence children’s diet, activity, and screen-based behaviors [37]. These processes include parents’ knowledge and beliefs about obesity, the modeling of healthy behaviors., and the opportunities they create for healthy eating and physical activity. Research documents the important role of these factors in predicting children’s lifestyle behaviors [37]. The outer domains of the FEM represent theoretically justifiable contextual factors, including demographic factors and child characteristics. ## 2.2. Participants Eligibility criteria to participate in this study included: (a) self-identify as Brazilian; (b) with at least one child between the ages of 2 and 5 years; (c) ≥21 years of age; (d) living in Massachusetts for at least 12 months; (e) speaking Portuguese or English; and (f) willing to provide informed consent [31]. ## 2.3. Data Collection Data were collected between January and August 2020 in selected Massachusetts communities with sizeable Brazilian immigrant populations [31,39]. We used both direct (e.g., in-person outreach at faith- and community-based events, such as meeting participants after church services, at faith-based community events, and outreach to personal contacts of research staff and recruiting partners) and indirect (e.g., posting flyers at local Brazilian businesses and community-based social and health services agencies, attending events and making announcements at predominantly Brazilian churches, and also using social media, such as Facebook postings) recruitment strategies to enroll study participants [31,39]. All in-person recruitment was conducted by bilingual, bicultural, and trained research assistants who were native Brazilians, undergraduate students in health-related disciplines [39]. In addition, we recruited participants through a “word of mouth” or snowball sampling approach by asking participants enrolled in the study to ask their Brazilian friends if they would be interested in participating [31,39]. Interested individuals called the phone number included on the flyer or spoke with study staff at church events [31,39]. Three authors developed a survey instrument in English based on a review of the literature and the “5 − 2 − 1 − 0 + 10” public health recommended lifestyle and obesity prevention goals, as well as our previous qualitative research with multi-ethnic Latinos [22,40,41,42,43] and Brazilian parents living in the U.S. by the research team [4,6,44,45,46,47,48,49]. The core survey included 44 items divided into three sections [22,31,40]. The original survey instrument developed in English was translated (Spanish and Portuguese) and pilot-tested for use with Hispanics [50,51] and Brazilians [31]. The first section had six items assessing parents’ perception of the importance of EBRBs for their preschool-aged children framed in terms of current public health recommendations (3 on healthy eating behaviors [promoting consumption of five or more fruits and vegetables per day, reducing consumption of unhealthy/junk foods, and reducing consumption of SSB, and 3 items on healthy 24 h movement behaviors [promoting at least 90 min of daily PA, limiting screen time to less than two h/day, and promoting 10 h sleep/night]) [31,50,51]. The second section included items assessing sources used by parents to obtain information to support EBRBs of their preschool-aged children (12 items). The third section included 26 items assessing parents’ preferences for a family-based intervention to promote healthful EBRBs, including content (7 items), delivery channels (11 items), access and use of communication technology (7 items), and language preference (1 item) [31,50,51]. Additionally, the survey included a section with two items assessing parents’ self-identified weight and their perceptions of their preschool-age child’s weight status (underweight, normal weight, overweight, obese), a section on sociodemographic characteristics (age, marital status, country of birth, years of residency in the U.S., primary language spoken, educational attainment, and annual household income) used in several of our previous studies with Brazilian immigrants in the U.S. [31,46,47,48,49,50,51], access and type of health insurance, and participation in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) [31,46,47,48,49,50,51]. Finally, the survey included the Short Acculturation Scale for Hispanics (SASH), a 12-item scale assessing participants’ acculturation level [52] that was used in our previous studies with Brazilian parents [31,46,47,48,49]. As recommended by the SASH developers, acculturation scores were computed by averaging across the 12 items, measured on a scale of 1 to 5, and scores were then dichotomized (high vs. low) [31,52,53]. We used the recommended cutoff point scores to categorize respondents as having a low acculturation level (SASH < 2.99) or a high acculturation level (SASH ≥ 2.99) [31,52]. The final survey was translated into Portuguese by two trained public health professionals and native Portuguese (Brazilian) speakers [31]. The survey was pilot tested with three Brazilian parents prior to use in the current study, and these results are not included in this study [31]. Parents with more than one preschool-age child were asked to think of the oldest age-eligible child when answering the survey questions [31]. The average time for completing the survey was 15 min [31]. Participants received a USD 25 gift card at the end of the interview for their participation [31]. ## 2.4. The Current Study This paper presents data derived from participants’ responses to questions from the third section of the survey (see above) [31]. Analysis of data from Section 1 and Section 2 have been published elsewhere [31]. In addition, data derived from responses about participants’ socio-demographics, access and type of health care insurance, acculturation, and parents reported perceived self and child’s weight status were used in the analysis [31]. Preferences for informational content were assessed by the following seven questions: “The next questions are about how much you would be interested in learning more about the following topics related to your child’s preschool-age health. Help your child: [1] eat more healthy foods such as fruits and vegetables, etc., [ 2] eat less unhealthy or “junk” (e.g., chocolates, candy, etc.) and fast food (e.g., hamburgers, fries, etc.), [ 3] drink less sugar-sweetened beverages (e.g., soft drinks, artificial juices, etc.), [ 4] drink more water, [5] be more physically active, [6] reduce the use of electronics (e.g., television, iPad, iPhone, computer, video game, etc.) or have less screen-time, [7] have adequate sleep every night (i.e., >10 h/night)” (5-point Likert scale: 1 = not interested to 5 = very interested) [31,50,51]. Preference for delivery channels or intervention modalities (11 items) was measured by the following questions: “If you were to enroll in a health promotion program for parents of preschool-age children, what would be your preference to receive such information?” ( 5-point Likert scale: 1 = completely disagree—5 = completely agree): [1] email, [2] text or SMS, [3] WhatsApp, [4] social media (e.g., Facebook, Instagram, Pinterest, etc.), [ 5] English language website, [6] Portuguese language website, [7] telephone calls by trained community health workers (CHWs), [8] individual sessions delivered by trained CHWs, [9] short duration courses offered by trained CHWs, [10] short-duration courses offered by trained parents like me or peer parents, and 11) printed materials [31,50,51]. Access and use of technology (7 items) was measured by the following questions: “Do you have a computer at home?” ( yes, no); “Do you have access to Internet at home?” ( yes, no); “How often do you check your email?” ( daily, more than once a week, less than once a week, once or less a month); “Do you have a mobile telephone where you can receive SMS/Text?” ( yes, no); “Do you use WhatsApp?” ( yes, no); “Do you use social media (e.g., Facebook, Instagram, Pinterest)” (yes, no); “How often do you check messages on your WhatsApp, SMS/text, or social media?” ( daily, more than once a week, less than once a week, once or less a month) [31,50,51]. Participants access and use of technology was assessed by two survey items. The first assessed whether the respondent had a home computer (yes, no), while the second assessed if respondents had Internet access at home (yes, no) at home [31,50,51]. Participants also reported how often they checked email (daily, more than once a week, less than once a week, once or less a month) and if they had a cell phone where they could receive SMS/Text messages (yes, no). Respondents also reported if they used WhatsApp (yes, no) and social media (e.g., Facebook, Instagram, Pinterest) (yes, no). Lastly, respondents reported how often they check messages on WhatsApp, SMS/text, or social media” (daily, more than once a week, less than once a week, once or less a month) [31,50,51]. Finally, language preference was measured by the following question: “If you were to enroll in a health promotion program for parents of preschool-age children, what would be your preferred language(s) to receive such information?” ( 1 = Portuguese, 2 = Either Portuguese or English, 3 = English) [31]. ## 2.5. Analysis All analyses were performed using SAS 7.1 (SAS Institute). We calculated means and standard deviations for all continuous variables and frequencies and percent for categorical variables [31,50,51]. Responses for preference for informational content, intervention modality, and preferred language were dichotomized for analysis based on the distribution of the data [31,50,51]. We used Wilcoxon rank sum, Chi-square test, and Fisher’s exact test appropriately to examine if there were differences in mothers’ and fathers’ preference for informational content related to EBRBs (interested/very interested vs. not interested), intervention modality (completely agree vs. neutral/disagree), and language (Portuguese vs. Portuguese or English/English) [31,50,51]. ## 3. Results A total of 52 Brazilian immigrant parents participated in the study, 27 mothers and 25 fathers. All parents were born in Brazil and reported having lived in the U.S. for an average of 7.6 years (SD = 6.6 years) [31]. Approximately $93\%$ ($$n = 48$$) were classified as having a low level of acculturation, and all reported Portuguese as their primary language. Moreover, while approximately $41\%$ ($$n = 11$$) of mothers and $28\%$ ($$n = 7$$) of fathers reported being overweight, the majority ($92\%$, $$n = 48$$) perceived their child as having normal weight status [31]. Additional information on the study’s participants is presented in Table S1. ## 3.1. Parents’ Preferences for Informational Content As shown in Table S2, most parents reported being interested/very interested in informational content focused on most of the assessed EBRBs. Regarding healthy eating behaviors, approximately $94\%$ ($$n = 49$$) reported being interested/very interested in content related to reducing the consumption of unhealthy or junk foods (Table S2). Furthermore, $90.4\%$ ($$n = 47$$) were interested/very interested in content related to increasing consumption of fruits and vegetables to 5 or more per day, $86.5\%$ ($$n = 45$$) in content related to reducing SSB intake, and $69.2\%$ ($$n = 36$$) in content related to promoting water consumption. Although more mothers than fathers reported being interested/very interested in content related to three of the four assessed healthy eating behaviors, these differences were significant only for reducing consumption of SSBs ($88.9\%$ vs. $56\%$; $$p \leq 0.008$$) and increasing intake of water ($81.5\%$ vs. $56\%$; $$p \leq 0.04$$). Furthermore, about $91\%$ ($$n = 47$$) of parents reported being interested/very interested in content related to increasing PA, $88.5\%$ ($$n = 46$$) in content focused on limiting screen time, and $67.3\%$ ($$n = 35$$) in content related to promoting healthy sleep. In contrast to healthy eating behaviors, as shown in Table S2, a greater proportion of fathers than mothers stated being interested/very interested in content related to increasing PA and limiting screen time to 2+ h per day. Nonetheless, these differences were not significant (Table S2). ## 3.2. Preferences for Intervention Modalities Most parents ($86.5\%$, $$n = 45$$) reported that they were interested/very interested in group sessions delivered by CHWs, with $96.2\%$ ($$n = 25$$) of mothers and $80\%$ ($$n = 20$$) of fathers endorsing this intervention modality (Table S3). Parents were also receptive to content being delivered via email ($84.6\%$, $$n = 44$$), SMS/text ($80.4\%$, $$n = 41$$), printed materials ($69.2\%$, $$n = 36$$), group sessions led by peer parents ($61.5\%$, $$n = 32$$), WhatsApp ($51.9\%$, $$n = 27$$), and individual home visits by CHWs ($50\%$, $$n = 26$$), with all these modalities being endorsed by half or more of the participating parents. A greater proportion of mothers than fathers endorsed information content being delivered via group sessions led by CHWs ($96.2\%$ vs. $80\%$; $$p \leq 0.18$$), email ($88.9\%$ vs. $80\%$; $$p \leq 0.37$$), group sessions by peer parents ($74.1\%$ vs. $48\%$; $$p \leq 0.05$$), WhatsApp ($59.3\%$ vs. $44\%$; $$p \leq 0.27$$), and individual visits by CHWs ($63\%$ vs. $36\%$; $$p \leq 0.05$$). However, these differences were significant only for group sessions by peer parents and individual visits by CHWs (Table S3). In contrast, a greater proportion of fathers than mothers endorsed content being delivered via text/SMS ($80\%$ vs. $77.8\%$; $$p \leq 0.85$$) and printed materials ($80\%$ vs. $59.3\%$; $$p \leq 0.11$$). However, these differences were not significant. Some intervention modalities were endorsed by less than half of the parents. These modalities included content delivered via Portuguese language websites ($32.7\%$, $$n = 17$$), English language websites ($23.1\%$, $$n = 12$$), social media ($21.2\%$, $$n = 11$$), and telephone calls ($19.2\%$, $$n = 10$$). Although a greater proportion of mothers than fathers endorsed information content delivered by these modalities, this difference was significant only for social media ($33.3\%$ vs. $8\%$, $$p \leq 0.03$$) (Table S3). ## 3.3. Communication Technology Access and Use Nearly all parents ($96.2\%$, $$n = 50$$) reported having access to a mobile phone where they could receive text messages, about $90\%$ ($90.4\%$, $$n = 47$$) reported using WhatsApp, and $85.2\%$ ($$n = 39$$) reported checking WhatsApp messages daily. A significantly higher percentage of mothers than fathers ($100\%$ vs. $80\%$, $$p \leq 0.02$$) reported using WhatsApp and checking for text messages daily ($85.2\%$ vs. $56\%$, $$p \leq 0.003$$). In contrast, only $25\%$ of respondents ($$n = 13$$) reported checking their email daily, with a higher proportion of mothers than fathers reporting they checked their email daily ($37\%$ vs. $12\%$, $$p \leq 0.04$$). Additionally, although $92.3\%$ ($$n = 48$$) of parents reported having Internet access at home, only $63.5\%$ ($$n = 33$$) reported having access to a computer at home. A higher proportion of mothers than fathers ($77.8\%$ vs. $48\%$, $$p \leq 0.03$$) reported having access to a home computer [31]. ## 3.4. Language Preference Finally, most parents ($71.2\%$, $$n = 37$$) reported a preference for delivered information to be in Portuguese, while $23\%$ ($$n = 12$$) indicated no preference, and $5.7\%$ ($$n = 3$$) preferred information be delivered in English [31]. There were no significant differences in language preference between mothers ($70.4\%$, $$n = 19$$) and fathers ($72\%$, $$n = 18$$) (Table S4). ## 4. Discussion The present study found important and significant differences between mothers’ and fathers’ preferences for intervention content and delivery modalities. As with previous studies with other Latino populations, a higher proportion of mothers than fathers in our study reported being interested/very interested in learning about most of the assessed EBRBs [26,54,55,56]. Interestingly, a higher percentage of fathers than mothers reported being interested/very interested in content related to children reducing consumption of junk food, promoting PA, and limiting screen time [31]. Although these differences were only significant for reducing the consumption of SSBs and increasing water consumption, they warrant further investigation. Parents were least interested in receiving information about promoting water consumption and adequate night sleep (>10 h/night). This finding suggests that there is a need for increased education regarding the importance of these two EBRBs, which are essential for preventing obesity and promoting children’s overall health and development [4,5,7,16]. In the current study, most parents preferred interventions involving direct interaction with CHWs (“promotores” or community health agents) with content delivered in Portuguese. CHWs are peer health educators who are trusted individuals from the community and share common characteristics with the priority population [57,58,59,60]. Previous research with Latino populations has successfully employed CHWs to deliver interventions to promote healthful EBRBs and prevent obesity [23,61,62]. Recent culturally tailored interventions delivered by CHWs designed to promote healthful EBRBs and prevent obesity in Latino children in the U.S., such as ANDALE Pittsburgh [23,24,62], Aventuras Para Niños [63], and La Vida Buena [56] have shown high acceptability and feasibility. Nonetheless, it should be noted that mothers accounted for most participants in these interventions, and these were conducted with Hispanic parents, not Brazilians [23,24,63]. In Brazil, CHWs (also known as “Agentes Comunitários de Saúde”) are an integral part of the government-sponsored child and family health programs that include home visits, and evidence suggests the effectiveness and acceptability of CHW-led child and adult health programs in Brazil [64,65]. This may partially explain why mothers and fathers participating in this study supported group sessions delivered by CHWs. In addition, most mothers favored group sessions delivered by peer parents. Combined with existing research findings, these findings suggest that interventions should consider offering group sessions and fostering social support by individuals of similar sociocultural backgrounds. Email and SMS/text messages were the second and third most preferred delivery channels for receiving informational content. Although a higher proportion of parents favored email over SMS as a delivery channel, only a small percentage reported checking their email daily. In contrast, nearly all parents checked SMS/text messages daily. These findings suggest that SMS/text may be a feasible communication channel for intervention delivery for this population [50,51]. Previous studies indicate the acceptability and feasibility of using SMS/text as a practical and low-cost way to deliver health information to parents at home [66,67,68,69]. Therefore, future child health promotion and obesity prevention interventions should consider SMS/text messages as a potential channel for reaching Brazilian immigrant parents to communicate health messages to support healthful EBRBs [31]. Moreover, nearly all parents in the current study reported using WhatsApp, and more than $90\%$ reported checking WhatsApp messages daily. WhatsApp is a popular cross-platform instant messaging application that facilitates communication, information sharing, and human interaction among Latin Americans, including Brazilians [36,69,70,71]. Notably, a greater proportion of mothers than fathers reported using and checking WhatsApp messages daily, which suggests that WhatsApp might be a good communication tool for prompting healthful EBRBs. Given that a high proportion of parents in this study reported using WhatsApp, it is likely a feasible and acceptable communication channel for the delivery of intervention content given that it is free of cost for exchanging text, image, video, and audio messages and should be considered in the development of future interventions for Brazilian immigrant families [69,70,71]. Our findings also revealed that most parents preferred intervention content be in their native language, Portuguese. This finding aligns with the acculturation levels of Brazilian immigrants participating in this study and previous studies [25,31,72] that document that Portuguese is the preferred and most used language by foreign-born Brazilians in the U.S. [31]. Previous studies with other minority immigrant populations suggest that language and communication can be barriers to parents understanding and engaging in interventions to promote healthful EBRBs, indicating the importance of interventions being linguistically tailored to meet the needs of specific ethnic minority immigrant populations, such as Brazilians [31,39,46,47,48,49,50,51,73,74]. Some study limitations include a small, convenience sample of Brazilian-born immigrant parents classified as having a low acculturation level living in a few cities in Massachusetts, which limits generalizability and should be considered when interpreting the study findings. In addition, participants may have had a heightened interest and awareness regarding the study topics. In addition, the present study did not include an assessment of the influence of some popular social media, such as TikTok, on parents perceptions of the importance of their children’s EBRBs [31]. Finally, as is often the case with formative research [32,33], the convenience sample in the present study is small, consequently limiting our ability to conduct additional analyses [31,50,51]. To address these limitations, future studies should include a larger sample size and Brazilian immigrant parents from other communities in the U.S. [31]. Nonetheless, the present study provides new information about the preference of Brazilian immigrant parents related to healthy eating and 24 h movement behaviors that may be used to design interventions to promote healthful EBRBs and prevent childhood obesity in preschool-age children in Brazilian immigrant families in the U.S. [31]. ## 5. Conclusions This formative research is the first to examine Brazilian immigrant parents’ preferences for informational content, delivery modality, and access and use of communication technology to develop a family-based intervention to promote healthful EBRBs and prevent child obesity [31]. Findings provide important information that can help guide the development of future family-based interventions tailored to meet the specific needs of this growing immigrant population in the U.S. [31]. Future steps by the research team include using findings to inform the design of a linguistic and cultural-sensitive family-based intervention to promote healthful EBRBs of preschool-age children in Brazilian immigrant families [31]. The intervention will integrate different communication strategies and be pilot tested to assess its acceptability and effectiveness. Intervention modalities, including direct contact, interactive interventions delivered by CHWs combined with mHealth, including SMS/text messages and WhatsApp messages, and printed materials, will be considered as methods to enable the sharing of information to promote Brazilian immigrant parents’ increased knowledge, skills, and practices needed to create a home environment supportive of their preschool-age children’s healthful EBRBs [50,51,66,67,68,69]. As behavior change and promoting healthful behaviors are complex, future interventions will likely need multiple components as one single delivery channel may not be sufficient to effectively promote EBRBs among children of Brazilian immigrant families in the U.S. [31,50,51]. ## References 1. Skinner A.C., Ravanbakht S.N., Skelton J.A., Perrin E.M., Armstrong S.C.. Prevalence of Obesity and Severe Obesity in US Children, 1999–2016. Pediatrics (2018) 141 e20173459. DOI: 10.1542/peds.2017-3459 2. Singh G.K., Yu S.M.. 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--- title: Consumption and Breakfast Patterns in Children and Adolescents with Congenital Heart Disease authors: - Joanna Maraschim - Michele Honicky - Yara Maria Franco Moreno - Patricia de Fragas Hinnig - Silvia Meyer Cardoso - Isabela de Carlos Back - Francilene Gracieli Kunradi Vieira journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048830 doi: 10.3390/ijerph20065146 license: CC BY 4.0 --- # Consumption and Breakfast Patterns in Children and Adolescents with Congenital Heart Disease ## Abstract Little is known about skipping breakfast and breakfast patterns (BP) and their evaluation according to sociodemographic, clinical, lifestyle, cardiometabolic and nutritional data in children and adolescents with congenital heart disease (CHD). This cross-sectional study with 232 children and adolescents with CHD identified the prevalence and patterns of the breakfast, described these according to sociodemographic, clinical and lifestyle characteristics, and assessed their association with cardiometabolic and nutritional markers. Breakfast patterns were identified by principal components, and bivariate and linear regression analysis were applied. Breakfast consumption was observed in $73\%$ of participants. Four BP were identified: pattern 1 “milk, ultra-processed bread, and chocolate milk”, pattern 2 “margarine and processed bread”, pattern 3 “cold meats/sausages, cheeses and butter/cream” and pattern 4 “fruits/fruit juices, breakfast cereals, yogurts, and homemade cakes/pies and sweet snacks”. Family history for obesity and acyanotic CHD were associated with breakfast skipping. Younger participants and greater maternal education were associated with greater adherence to pattern 1 and pattern 4. Older participants and longer post-operative time showed greater adherence to pattern 3. No association between skipping breakfast or BP and cardiometabolic and nutritional markers was observed. Nonetheless, the findings reinforce the need for nutritional guidance for healthy breakfast, aiming to reduce the consumption of ultra-processed foods and to prioritize fresh and minimally processed foods. ## 1. Introduction Children and adolescents with congenital heart disease (CHD) are more likely to develop overweight or obesity and changes in cardiometabolic markers, such as increased blood pressure [1]. This is possibly due to a sedentary lifestyle [2,3] and high caloric and ultra-processed foods consumption [4,5,6]. Feeding behavior during childhood plays an important role in preventing excess body weight and changes in metabolic markers related to developing acquired cardiovascular diseases in adulthood [7]. Evidence indicates that breakfast is often recognized as the most important meal of the day, even though there is no consensus in the literature about its definition and ideal composition of nutrients and foods [8,9]. Furthermore, studies describe the association between skipping breakfast and obesity [10,11] and changes in cardiometabolic markers or metabolic syndrome in children and adolescents [8,11,12,13]. Besides having breakfast, the composition of this meal has important implications for cardiometabolic health [14], considering that foods are consumed together and not in isolation [15]. Thus, different methods for assessing breakfast quality have been defined [8,16]. The identification of dietary patterns (DPs) emerged as an assessment alternative, making it possible to know the DPs and their associations with a particular disease [17]. The association between breakfast patterns with obesity and cardiometabolic risk factors has already been well studied in adults [18,19,20]. However, few studies have been carried out with healthy children and adolescents, which identified breakfast patterns and evaluated their association with overweight and obesity [16,21]. In children and adolescents with CHD, as far as we know, there has been only one study carried out in Brazil, which identified six DPs of the global diet and observed that an unhealthy DP (poultry, red meat, cold cuts, and processed meats, soft drinks and sweetened beverages) was associated with a higher risk of central adiposity, a healthy DP (fish, eggs, bread, beans, tubers and roots, fruits and fruit juice) was associated with a decreased risk of central adiposity, and a low dairy DP (milk and dairy products with low-fat content, mixed dishes, ultra-processed bread, sweets, and chocolate) was inversely associated with carotid intima-media thickness [22]. In this context, the primary objectives of this study were to identify the prevalence of skipping and breakfast consumption, to describe the breakfast patterns, and to evaluate the sociodemographic, clinical and lifestyle characteristics according to the skipping and breakfast consumption and breakfast patterns of children and adolescents with CHD. Second, we aimed to investigate the association of skipping breakfast and breakfast patterns with cardiometabolic and nutritional markers in this population. ## 2.1. Study Design and Population A cross-sectional study was carried out with children and adolescents with CHD previously undergoing a cardiac procedure, who were monitored in pediatric cardiology outpatient clinics in two reference hospitals in southern Brazil, from January to July 2017. Sample size calculation was performed using the OpenEpi® 3.01 software (Atlanta, GA, USA) based on the study outcome variables (cardiometabolic markers and nutritional status), assuming a type 1 error (α) of 0.05, type 2 error (β) of 0.20, and $95\%$ confidence interval. Considering that the highest prevalence among outcomes was $26.9\%$ obesity, as assessed by air displacement plethysmography (Bod Pod® Body Composition System, COSMED, Concord, CA, USA) in Brazilian children and adolescents with CHD [23], the study required a sample with 131 children and adolescents with CHD. Inclusion criteria were: (I) age between 5 and 18 years; (II) diagnosis of CHD; and (III) after therapeutic catheterization or cardiac surgery for CHD. Exclusion criteria were: (I) secondary diagnosis of malignant neoplasm; (II) chromosomal anomalies; (III) primary or secondary familial dyslipidemia; (IV) diabetes mellitus or hypothyroidism; and (V) presence of acute or chronic inflammatory diseases in the last 15 days. The data are from the Floripa CHild study (Congenital Heart dIsease and atheroscLerosis in chilDren and adolescents Study), a longitudinal study aiming to investigate risk factors for atherosclerosis in children and adolescents with CHD. This research was approved by the Research Ethics Committee at Joanna Gusmão Children’s Hospital, Brazil (no. $\frac{1.672.255}{2016}$) and was conducted following the Declaration of Helsinki. The children and adolescents obtained written authorization from their legal guardians and agreed to participate in the study. ## 2.2. Dietary Assessment The assessment of food consumption was based on three 24 h recalls on non-consecutive days (two on weekdays and one on weekend), using the multiple-pass technique [24]. The first 24 h recall was applied at the time of data collection, and the next two were obtained via telephone, with an interval between recalls of 7.3 weeks (SD 3.21). Details on the collection and processing of recalls are available in previous studies [22,23]. A photographic album to aid in reporting the portion sizes of food intake was used [25]. The recalls were entered into the Nutrition Data System for Research® (NDSR) software, 2017 version (University of Minnesota, Minneapolis, MN, USA), which includes the name and meal times to obtain specific food consumption data by meals. This software uses the United States Department of Agriculture (USDA) database as the main database. Initially, the nutritional equivalences of the foods available in the software were checked on Brazilian charts [26], and Brazilian typical recipes were entered manually into the software [27]. The data of the foods and preparations (g or mL) from the 24 h recall were entered into the NDSR software after using standardized methodology and Brazilian home measurement charts [28,29]. ## 2.3. Breakfast Consumption Definition This study adopted the concept of breakfast based on guardians of children and adolescents identification during the application of 24 h recalls [30]. Breakfast skipping was considered as not having breakfast on at least one of the three days evaluated based on the definition of skipping this meal at least once per week as used by others [31,32]. ## 2.4. Identification of Breakfast Patterns The breakfast patterns were generated only with data from participants who had this meal in the three 24 h recalls. One hundred twenty-seven breakfast food items were reported. Foods with consumption frequency lower than $5\%$ were excluded from the analysis. Food items were grouped based on the similarity of nutritional composition and their respective degrees of processing, according to the NOVA [33] classification (UP, ultra-processed; P, processed), resulting in 19 food groups (Table 1). Data in grams were adjusted for the total energy intake using the residual method [34]. The breakfast patterns were derived by principal component analysis. The Kaiser–Meyer–Olkin (KMO) statistical test was performed to verify the applicability of the factor analysis, and a KMO value of 0.55 was considered acceptable [35]. The eigenvalues of 1.30, the screeplot [35,36] graphical representation, and the interpretation of breakfast patterns by nutritionists were considered to retain the number of factors. Varimax rotation was used to simplify data interpretation, and the food groups with factor loadings \|≥0.25\| were considered representative of each breakfast pattern [36]. For each component retained, a score was generated for each participant. The score was calculated considering the amount in grams of each food group multiplied by the factor loading of this item in the pattern, with higher scores corresponding to greater adherence to a specific breakfast pattern. ## 2.5. Sociodemographic, Clinical and Lifestyle Data Sociodemographic data such as age (in years), sex (female/male), mother’s education (<10 years of schooling and ≥10 years of schooling), and family history for obesity (absent/present) were collected. Clinical information such as the classification of congenital heart disease (cyanotic/acyanotic) [37] and the mean post-operative time (in years) were collected from the medical records. Sedentary behavior was assessed by the number of hours spent leisurely in front of the television, computer/similar and/or electronic games and time spent sitting per day, categorized as no, <8 h/day, and yes, ≥8 h/day, and in hours/day [38]. ## 2.6. Nutritional Markers Waist circumference (cm) was measured with the participant in the standing position on the iliac crest and at the end of a normal expiration [39], using a non-elastic tape with 0.1 cm precision (TBW®, São Paulo, Brazil), by a trained nutritionist. The waist circumference percentiles for sex and age were calculated [40]. Body composition was performed using air displacement plethysmography (Bod Pod® Body Composition System, COSMED, Concord, CA, USA) following the calibration procedures described by the manufacturer [41]. Details of the test protocol have been described previously [23]. The software determined the percentage of body fat (%) and the percentage of lean mass (%) using the calculation proposed by Lohman [1989] [42] for children and adolescents. ## 2.7. Cardiometabolic Markers Fasting glucose concentration was assessed using the colorimetric enzymatic method and was expressed in mg/dL. Total cholesterol and triglycerides were determined by the enzymatic method (Dimension®, Siemens, Newark, NJ, USA). The HDL-c concentration was obtained by the direct method, in vitro. Non-HDL-c values were obtained by subtracting total cholesterol from HDL-c. LDL-c concentrations were calculated using the Friedewald formula [43]. Lipid parameter values were expressed in mg/dL. The plasma concentration of hs-CRP was determined by immunonephelometry (BN II®, Siemens Healthcare Diagnostics Inc., Newark, DL, USA), expressed in mg/L. The measurement of systolic and diastolic blood pressure was performed with a mercury sphygmomanometer and an appropriate cuff according to the arm circumference (Tycos, Welch Allyn® New York, NY, USA), following the protocol of the National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents (NHBPEP), expressed in percentiles and calculated according to sex, age, and height percentile [44]. ## 2.8. Statistical Analysis The Kolmogorov–Smirnov test, coefficient of variation, and histograms were performed to assess the normality of the data. Descriptive data were presented as mean and standard deviation (SD) or median and interquartile range (IQR) and relative and absolute frequencies. To assess the sociodemographic, clinical and lifestyle sample characteristics according to adherence to the breakfast pattern, the scores of each one of the four breakfast patterns were categorized as below or above the median, with children and adolescents with CHD above the median showing greater adherence to the breakfast pattern. In the bivariate analysis, between the breakfast consumption or the breakfast patterns and sociodemographic, clinical and lifestyle sample characteristics, the chi-square test and Student’s t test were used. Simple and multiple linear regression by the forward selection procedure was performed to investigate the association between breakfast skipping or factorial scores of each breakfast pattern (independent variables) and cardiometabolic and nutritional markers (dependent variables). Asymmetric variables were transformed into logarithmic and later into exponential numbers. The covariates considered in multiple analyses were based on the bivariate analysis ($p \leq 0.20$) and potential confounders for cardiometabolic and nutritional markers described in the literature [37,45]. The variance inflation factor (VIF) was used to analyze the collinearity between the variables, (VIF) > 10 were not included, avoiding multicollinearity. Multivariable-adjusted analysis was adjusted for age (in years), sex (female/male), maternal education (<10 years and ≥10 years) (Adjustment 1), adjustment 1 + sedentary behavior (in hours/day), postoperative period (in years), family history for obesity (absent/present), classification of congenital heart disease (cyanotic and acyanotic and according to the international code of diseases (ICD-10)), waist circumference (percentile) (Adjustment 2), and adjustment 2 + glucose (mg/dL) (Adjustment 3), only for HDL-c and non-HDL-c variables. Linear regression models between breakfast skipping and cardiometabolic and nutritional markers were also adjusted for total daily energy (kcal/day) [34]. Total daily energy was adjusted for intra-interpersonal variability [46]. The results were expressed in regression coefficients and respective $95\%$ confidence intervals ($95\%$ CI). All statistical analyses were performed using Stata® software version 13.0 (STATA Corporation, College Station, TX, USA). ## 3.1. Characteristics of Study Participants Three hundred nineteen children and adolescents were considered eligible for the study, of which the following were excluded: not contacted ($$n = 63$$), chromosomal syndrome ($$n = 7$$), under five years old ($$n = 4$$), over 18 years old ($$n = 12$$), and losses (nephrotic syndrome $$n = 1$$). Thus, 232 children and adolescents with CHD participated in the study. Supplemental Figure S1 shows the flowchart of participant selection. The mean age was 10.2 (SD: 3.7 years), $52.5\%$ were girls, $63.4\%$ had sedentary behavior, the mean postoperative time was 6.7 (SD: 3.8 years), $35.5\%$ of the participants had a family history for obesity, and $66\%$ with acyanotic CHD. Of the 232 children and adolescents with CHD, $73\%$ had breakfast consumption. The mean energy intake for breakfast among children and adolescents with this meal was 78.3 (SD: 53.8 kcal). Among participants who skipped breakfast ($27\%$), most were girls ($57.1\%$), had a family history for obesity ($52.4\%$), and $66.6\%$ had acyanotic CHD. Bivariate analysis showed that participants with a family history for obesity ($$p \leq 0.001$$) and those with acyanotic CHD ($$p \leq 0.01$$) were associated with breakfast skipping. Characteristics of the total study population according to skipping and breakfast consumption are shown in Table 2. ## 3.2. Breakfast Dietary Patterns Four breakfast patterns were identified, which explained $37.0\%$ of the total breakfast variability. Breakfast pattern 1 was characterized by high consumption of milk, UP bread, and chocolate milk, and low consumption of homemade cakes/pies and sweet snacks and coffee/tea. Breakfast pattern 2 included high consumption of margarine and P bread. Breakfast pattern 3 included high consumption of cold meats/sausages, cheeses, butter/cream, and low consumption of sugary drinks and soft drinks. Breakfast pattern 4 was characterized by high consumption of fruits/fruit juices, breakfast cereals, yogurts, and homemade cakes/pies and sweet snacks, and low sugar, coffee, and tea (Table 3). In the bivariate analysis, there was an association of younger participants with greater adherence to the breakfast pattern 1 of milk, UP bread, and chocolate milk ($$p \leq 0.001$$) and to the breakfast pattern 4 of fruits/fruit juice, breakfast cereals, yogurts, and homemade cakes/pies and sweet snacks ($$p \leq 0.001$$), and older participants showed greater adherence to the breakfast pattern 3 of cold meats/sausage, cheeses and butter/cream ($$p \leq 0.003$$). Maternal education was associated with the breakfast pattern 1 of milk, UP bread, and chocolate milk ($$p \leq 0.01$$) and the breakfast pattern 4 of fruits/fruit juices, breakfast cereals, yogurts, and homemade cakes/pies and sweet snacks ($$p \leq 0.002$$), with participants whose maternal education level was greater than 10 years showing greater adherence to these patterns compared to participants with lower maternal education. In addition, the longer post-operative time of the participants was associated with greater adherence to the breakfast pattern 3 of cold meats/sausage, cheese, and butter/cream ($$p \leq 0.04$$) (Table 4). ## 3.3. Association Analysis between Skipping Breakfast and Breakfast Patterns with Cardiometabolic and Nutritional Markers In the multiple linear regression analysis, after adjustments for confounding factors (Adjustments 1 and 2), no association was found between skipping breakfast and cardiometabolic markers and nutritional status markers (Table 5). The cardiometabolic and nutritional markers were also not associated with any of the four breakfast patterns in the multivariate analysis adjusted for potential confounding factors (Adjustments 1, 2, and 3) (Table 6). ## 4. Discussion To date, this is the first study that identified skipping, breakfast consumption and the breakfast patterns and assessed their association with sociodemographic, clinical and lifestyle characteristics as well as cardiometabolic and nutritional markers in children and adolescents with CHD. In this cross-sectional study carried out in southern Brazil with children and adolescents with CHD, $27\%$ of the participants skipped breakfast. Breakfast skipping was associated with a family history for obesity and participants with acyanotic CHD. Among those who consumed breakfast, four breakfast patterns were identified. The first pattern characterized by the high consumption of milk, UP bread, and chocolate milk showed greater adherence in younger patients and between those with higher maternal education. The second pattern, constituted by the high consumption of margarine and processed bread, was not associated with any analyzed variables. The third pattern characterized by the high consumption of cold meats/sausages, cheeses, and butter/cream showed greater adherence among older patients and with a longer mean postoperative time. The fourth pattern characterized by the high consumption of fruits/fruit juices, breakfast cereals, yogurts, and homemade cakes/pies and sweet snacks showed greater adherence in younger patients and those with higher maternal education. The frequency of breakfast consumption among the participants in this study was $73\%$, similar to that found in previous studies carried out in Brazil (79 to $91\%$) [47,48,49], Jordan ($80\%$) [50] and Mexico ($83\%$) [15] but higher than those identified in the United States (14–$18.7\%$) [16,51] and in Spain ($5.3\%$) [8]. In this study, $27\%$ of the participants skipped breakfast, lower than that found between adolescents in other Brazilian studies ($36.2\%$ to $38\%$) [52,53]. Furthermore, we found a higher prevalence of breakfast skipping among participants with a family history for obesity ($52.4\%$), similar to a study in Egypt [54], and despite that a higher prevalence of breakfast skipping among participants with acyanotic CHD ($66.6\%$) was observed, no similar study was found for comparison. In this study, no association was found between breakfast consumption and cardiometabolic and nutritional markers. This fact can be partially explained by our definition of breakfast, as we did not consider the occasional consumption of breakfast. In a study in Southern California [55] of overweight adolescents with a family history for type 2 diabetes, it was observed that breakfast consumption was associated with increased intra-abdominal adipose tissue. Breakfast was defined as any food or drink consumed between 5:00 and 10:00 a.m. with a total combined energy ≥100 kcal, and people who consumed breakfast occasionally were also included [55]. In another study in Taiwan [13] with elementary school students, breakfast was defined with the question “how often do you eat breakfast in the week?”. It was found that children who consumed breakfast daily had lower risks of high blood pressure and metabolic syndrome compared to children who consumed breakfast 0 to 4 times per week [13]. Similar to our results, a study in the Netherlands [56] did not observe an association between breakfast skipping and being overweight in children aged 2 to 5 years. The authors did not consider occasional breakfast consumption. The breakfast pattern 1 “milk, UP bread and chocolate milk” identified in this study confirms the results of the research by Marchioni et al. [ 2015] [53], demonstrating that milk, UP bread, and chocolate milk are among the most consumed foods for breakfast among healthy Brazilian adolescents. Similar results were shown in international studies, as observed in healthy Mexican children, in which the milk and sweetened bread pattern was the most consumed foods for breakfast [15]. Studies with healthy children in Greece [57] and Spain [58] found that milk and chocolate milk were the most frequently consumed foods for breakfast. In addition, greater adherence to the pattern “milk, UP bread and chocolate milk” by younger participants was observed in this study, in line with a previous study carried out in Brazil [59], which identified a higher prevalence of consumption of foods such as milk at breakfast ($63.3\%$), bread ($59.5\%$), dairy products ($3.3\%$), and chocolate milk ($29.1\%$) among children aged 7 to 9 years old. The breakfast pattern most consumed by children aged 9 to 11 years in France [60] included mainly flavored milk, bread, fat (butter), and juice, similar to the breakfast pattern 2 of “margarine and P bread” found in this study. The food groups found in this pattern are similar to those identified in the breakfast of Brazilian adolescents [61]. The breakfast pattern 3 of “cold meats/sausages, cheeses and butter/cream” in this study corroborates what was observed in a study carried out with schoolchildren aged 7 to 13 years in southern Brazil, in which the “Traditional Brazilian Pattern” was made up of bread, cheese, sausages, and coffee with milk, which was the most consumed among the three identified breakfast patterns [62]. In addition, a similar result was found in a population-based study carried out with adolescents aged 10 to 19 years in Brazil, in which the breakfast pattern was protein based, consisting of cold cut meat, milk and cheese [21]. In addition, greater adherence to the pattern “cold meats/sausages, cheeses and butter/cream” was identified in participants with a longer mean postoperative time. However, no similar study was found for comparison. A similar result to breakfast pattern 4, “fruits/fruit juices, breakfast cereals, yogurts, and homemade cakes/pies and sweet snacks”, was found in a study carried out in the United States [16] with healthy children and adolescents, which identified the breakfast pattern of ready-to-eat cereals and whole milk. In Mexico [15], of the six breakfast patterns identified in children aged 4 to 13 years, the “cereal and milk pattern” consisting of ready-to-eat breakfast cereal, milk, and yogurt was represented by $6\%$ of the children. In the present study, the older age of the participants was associated with greater adherence to the “cold meats/sausages, cheeses and butter/cream” pattern, which is in line with a study carried out in Salamanca that identified an increase in the consumption of dairy products and fruits in adolescents [63]. The younger age of the participants is associated with greater adherence to the pattern “fruits/fruit juices, breakfast cereals, yogurts, and homemade cakes/pies and sweet snacks” in this study; a similar result was found in a study carried out in Malaysia, which identified a higher consumption of ready-to-eat breakfast cereals in children aged 6 to 9 years [64]. Higher maternal education was associated with greater adherence to the patterns “milk, UP bread and chocolate milk” and “fruits/fruit juices, breakfast cereals, yogurts, and homemade cakes/pies and sweet snacks” in the present study, which is in line with what was observed in the study with Brazilian children aged 8 and 9 years that found a positive association between maternal education and the pattern called “egg–dairy”, consisting of sweetened milk drinks [65]. A study carried out in the Netherlands with children aged 8 to 12 years found a positive association between fruit consumption and increased maternal education [66]. The four breakfast patterns identified in this study can be considered mixed and were composed of UP and P products, which are formulations made mainly of substances derived from food and chemical additives, containing little or no whole food; they are more energy-dense, rich in saturated fats, trans fats and added sugar, and they are low in protein, dietary fiber, and micronutrients [33]. Furthermore, UP foods are highly palatable, promoting the physiological interruption of the signs of hunger and satiety, inducing their excessive consumption, and they are associated with increased lipogenesis [67] and the accumulation of fatty acids in tissues and blood [68]. This evidence was observed in a study carried out with the same CHD children and adolescents in the present study, which found a positive association between total daily consumption of added sugar and trans fatty acids and total and central body adiposity [23]. These previous findings, associated with the results found in this study, reinforce the importance of raising awareness about the promotion of a healthy lifestyle, for the prevention of obesity and cardiovascular diseases by children and adolescents with CHD, after undergoing corrective cardiac surgery, with the introduction of healthy foods for breakfast containing whole grains, fruits, and low-fat dairy products and avoiding the consumption of ultra-processed foods. Similar to breakfast skipping, cardiometabolic and nutritional markers were not associated with any of the four breakfast patterns in the present study. Only one study carried out in the United States [16] investigated the association between these outcomes with breakfast patterns in healthy children and adolescents through cluster analysis, which observed that children and adolescents who make up the patterns “ready-to-eat pre-sweetened cereal, low-fat milk” and “pre-sweetened cereal, whole milk” were $43\%$ and $46\%$, respectively, less likely to be overweight or obese than children and adolescents who skipped breakfast [16]. In addition, a population-based study carried out with adolescents in Brazil investigated the association between breakfast patterns with weight status through principal component factor analysis, which observed that the “cereal, protein, fruit beverages and northern/northeastern” pattern was inversely associated with weight status [21]. Furthermore, foods representative of the breakfast pattern 3 “cold meats/sausages, cheeses and butter/cream” are predominantly of animal source, have a high energy density, are rich in saturated and trans fatty acids and added sugars, and are low in fiber, dietary factors considered to be risk factors for the development of obesity [23,69,70]. Although the breakfast patterns were not associated with cardiometabolic and nutritional markers in our study, breakfast skipping or breakfast patterns characterized by unhealthy foods may impact the long-term cardiovascular health of this population since they already have cardiovascular risk factors in childhood [1]. The main strengths of this study include, first, the originality of the study; second, the collection of food consumption data was obtained by three 24 h recalls on non-consecutive days; third, the sample size was representative of the population studied; finally, the use of an a posteriori approach to identify the breakfast patterns, which is a new and representative approach of the food consumption profile [71], making it possible to know the foods that are positively or negatively associated with a DP [17]. However, the present study had limitations: first, the cross-sectional design, which made it impossible to establish a causal relationship between breakfast consumption or identified breakfast patterns and cardiometabolic and nutritional markers; second, there is limited consensus as to what defines the breakfast meal. How eating occasions are defined has been shown to affect how eating patterns are quantitatively characterized. Leech et al. [ 2021] [30] suggested that a definition that categorizes meals and snacks using the participant identification or time-of-day approach may be a suitable choice for use in children’s DPs and obesity research; third, the lack of information that can influence breakfast composition, such as sleeping time and school hours, and housing (rural or urban); fourth, the principal component analysis presents a subjectivity of choice in the criteria for retaining the number of DPs; Lastly, the multivariable analysis between breakfast patterns and cardiometabolic and nutritional markers should be interpreted with caution, considering that the possibility of random measurement error [72,73] and stepwise selection procedure can produce biased estimates and incorrect confidence intervals [74]. However, the decisions to derive breakfast patterns as well as the selection of the variables included as potential confounders in the multivariable analysis were made according to the methodologies described in the literature [36,37,45]. ## 5. Conclusions In the present cross-sectional study in children and adolescents with CHD, a high frequency of breakfast skippers and four breakfast patterns considered mixed and composed of ultra-processed foods were identified. These findings reinforce the need to promote intervention strategies to improve the lifestyle through food and nutrition education for a healthy breakfast, promoting the reduction in the consumption of ultra-processed foods and prioritizing fresh and minimally processed foods by this population. 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--- title: 'Exposure to Healthy Weight Information on Short-Form Video Applications to Acquire Healthy Weight-Control Behaviors: A Serial Mediation Model' authors: - Donghwa Chung - Yanfang Meng journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048840 doi: 10.3390/ijerph20064975 license: CC BY 4.0 --- # Exposure to Healthy Weight Information on Short-Form Video Applications to Acquire Healthy Weight-Control Behaviors: A Serial Mediation Model ## Abstract This study explored the effects of Chinese college students’ (20–34 years old) exposure to healthy weight information on short-form video applications on their intention to acquire healthy weight-control behaviors (reducing high-fat diet intake, accessing physical activity to control body weight, etc.). Specifically, this study investigated the direct and mediated effect on such a relationship via healthy weight awareness, the first-person effect, and perceived herd. The data were collected using a web-based survey and thoroughly tested questionnaire with a sample of 380 Chinese college students. Hierarchical regression, parallel mediation, and serial mediation analysis were applied to test the hypotheses. The results indicated that healthy weight awareness, first-person effect, and perceived herd all played mediator roles that induced the relationship between Chinese college students’ exposure to healthy weight information and their intention to acquire healthy weight-control behaviors. In addition, healthy weight awareness and the first-person effect sequentially mediated this relationship. ## 1. Introduction In November 2022, recurrent outbreaks of COVID-19 and increased confirmed daily new cases urged the Chinese Center for Disease Control (CDC) to issue a stay-at-home order to reduce the spread of COVID-19 [1]. The prolonged lockdown limited outdoor activities, which reinforced Chinese citizens’ lifestyle problems [2]. A number of studies stressed that the COVID-19 pandemic led to individuals adopting an unhealthy diet and poorer lifestyle; the remaining question was whether college students gained weight during this time [3,4]. Tavolacci et al. [ 5] found that college students’ overweight and obesity increased during the COVID-19 pandemic. Similarly, an investigation also demonstrated that almost $45.5\%$ of university students experienced weight gain during the one month of lockdown in Malaysia. Such healthy-weight-maintenance-related issues caught the attention of the China Health National Health Commission. In response to this circumstance, the departments cooperated with social media platforms, encouraging content creators to promote healthy weight lifestyles and preventive behaviors to the media audiences [6,7]. However, it remains unclear how exposure to healthy weight information on cutting-edge new media platforms (e.g., short-form video applications) may impact Chinese media users’ acquisition of healthy weight-control behaviors during the ongoing COVID-19 pandemic in China. Chinese short-form video applications have skyrocketed in popularity globally in recent years [8]. One of the latest investigations has indicated that short-form video applications have mostly been promoted to Chinese young media users, and are widely accepted by college students (20–34 years old) [9]. The applications are mainly recognized as entertainment platforms, where users create short-form videos or perceived content from other accounts [10]. Aside from their entertainment features, one of the most recent studies has indicated that short-form video applications offer great opportunities for disseminating health-related information [11]. Specifically, these applications’ users have demonstrated higher perceived credibility in regard to health-related short-form videos, which has further encouraged their intention to use these applications to obtain health information [12,13,14]. However, prior studies have failed to investigate such mechanisms in Chinese users’ acquisition of healthy weight-control behaviors. As discussed above, investigating such a phenomenon is critical for two reasons. First, Chinese health institutions are eager to know whether healthy weight promotion on short-form video applications is effective, and what the likelihood is of individuals being willing to acquire healthy weight-control behaviors [15]. Second, no empirical research has fully investigated the mechanisms that lead to Chinese short-form video users’ acquisition of healthy weight-control behaviors to date. To fill this gap, the current study applied the third-person effect theory as a guide to further investigate the direct effect between Chinese college students’ (20–34 years old) exposure to healthy weight information (EHWI) on short-form video applications, and their intention to acquire healthy weight-control behaviors (IAHWCB) (H1). This study also identified possible mediating mechanisms in this relationship, based on health awareness, first-person effect (FPE), and perceived herd (PH) perspective (H2–H5). ## 2.1. TPE TPE in communication arises when an individual exposed to a mass-media message perceives that message as being more impactful or persuasive to others than to him or herself [16]. In general, the TPE has been explored through two aspects in the field of communication and mass media. The first aspect is the so-called “perceptual hypothesis”: identifying a self–other discrepancy regarding exposure to persuasive media messages through mass media. In other words, the perceptual hypothesis explores whether people believe that the media content has the greatest influence on “them” rather than on “me”. The second aspect refers to the “behavioral hypothesis”. In most cases, individuals’ decisions are affected by their presumption of media impact (TPE) [17]. Therefore, this hypothesis refers to individuals’ presumption that the effect on behavioral intentions is greater for others compared to themselves. Previous studies have explored TPE in the context of news about bird flu outbreaks [18], voters’ attitudes toward polls in the 2008 U.S. presidential election [19], the impact of fake news online on voter decisions [20], individuals’ perception of fake news during the COVID-19 outbreak [21], and the media’s influence on individuals’ vaccination against COVID-19 [22]. Although existing studies have examined both perceptual and behavioral hypotheses, there are certain gaps that remain unsolved. For instance, most of the studies overlooked how socially undesirable information increases TPE (e.g., pornography- and violence-related messages), whereas only a few studies have identified how socially desirable information decreases the self–other discrepancy [23]. Moreover, numerous scholars have broadly investigated TPE in Chinese media (such as Chinese TV, print media, social networking websites, and social media) [24,25,26]. However, there are still very few studies discussing the media effect of short-form video applications. Lastly, only a handful of health communication studies have explored the link between TPE and health behaviors, namely, vaccine hesitancy [27], COVID-19 prevention [28], and safer-sex consciousness [29]. However, the academic literature has paid little attention to how TPE leads individuals to acquire healthy weight-control behaviors. Prior behavioral studies have argued that not all self–other perceptual discrepancies are considered TPE [23,30]. Scholars have identified an induced likelihood for individuals to presume that a media message is more impactful and persuasive to themselves compared to others [31]. Moreover, individuals show stronger effects for themselves than others when receiving a message that they presume to be socially desirable [23]. This effect is identified as reverse TPE, also called FPE. Among the social media platforms in China, users have deemed TikTok (Douyin) as an application with large amounts of socially desirable content. This is one of the reasons that individuals continue to use TikTok, as well as for the induced social rewards and self-presentation [32]. For instance, a search of the keywords “Slimbody” and “Fitness inspiration” in December 2022 on Douyin demonstrated that the recommended videos (determined by the number of likes and comments) were viewed between 17 million and 20 million times. Most of the short-form video content could be categorized as the flat tummy challenge, athletic women showcasing workout routines, and weight-loss transformations before and after. Chinese short-form video users tend to assume that these types of videos are socially desirable content. This may increase users’ evaluation of the message’s impact on themselves compared to other users. According to Bavikatty’s [33] study, when female TikTok users are exposed to short-form video content with thin body types, they are likely to perceive the impact of the thin body ideal, and tend to compare their body type with that of influencers. However, little is known about whether reversed TPE exists in the context of healthy weight promotion on Chinese short-form video applications. Therefore, based on the concept of FPE, the current study explores the effect of perceived exposure to healthy weight information on short-form video applications on Chinese college students’ FPE (perceptual hypothesis). This study also explores how this will further reinforce them to acquire healthy weight-control behaviors (behavioral hypothesis). ## 2.2. Effect of EHWI on IAHWCB Popular Chinese short-form video applications have a powerful algorithm feature that fully meets the needs of Chinese college students [8]. Moreover, a previous study has demonstrated that Chinese citizens’ use of Douyin (overseas version of TikTok) has greatly reinforced their interest in managing health-related content and disease-prevention knowledge [34]. Moreover, in an effort to spread healthy weight-control behavior, short-form video applications have collaborated with the Chinese health department to spread healthy weight information (e.g., weight-control and engagement in weight loss behaviors) [6]. In this study, healthy weight information (EHWI) refers to the frequency of individuals’ perceived coverage of healthy weight issues on short-form video applications. Maintaining a weight within the normal range is critical to overall health. This is beneficial for preventing range of major diseases (e.g., heart disease, high blood pressure, type 2 diabetes, and breathing difficulties) [35]. Prior studies have indicated that the prolonged COVID-19 pandemic has worsened individuals’ weight-management maintenance. For instance, poor dietary habits [36], binge eating behaviors [37], and unwillingness to engage in physical activities have increased [38]. Investigating this phenomenon is critical to understand why individuals developed such behaviors during the COVID-19 pandemic. It is also urgent to know whether healthy weight campaigns in the media have successfully changed perceptions or behaviors. Lampard [39] categorizes healthy weight-control behaviors into six categories. Based on the individual frequency of activities dedicated to losing weight or preventing weight gain undertaken in the last year, the six specific behaviors are engaging in exercise, consuming more fruits and vegetables, controlling the intake of high-fat food, reducing the intake of sweets, avoiding drinking soda pop, and controlling portion sizes for each serving. In this study, the intention to acquire healthy weight-control behaviors (IAHWCB) refers to the possibility of an individual actively engaging in weight-control behaviors (reducing high-fat diet intake, increasing physical activity to control body weight, etc.). A previous study has demonstrated that exposure to weight-stigmatizing media was positively associated with British female users’ weight loss intentions [40]. Similarly, individual exposures to weight-related messages have induced participation in weight-loss activities [41]. Moreover, a focus group study indicated that exposure to friends’ food-related posts on social media induced healthy diet behavior (healthy food choices) in young adults [42]. The following hypothesis is thus proposed. ## 2.3. The Mediating Role of Healthy Weight Awareness One of the key factors that predicts individuals’ engagement in health behaviors is health awareness [43]. In Mitchell and Prue’s [44] study, health awareness was conceptualized as general perception and knowledge of healthy behaviors. In the current study, healthy weight awareness (HWA) refers to Chinese college students’ healthy weight-related perception and knowledge. Previous studies have shown that mass-media exposure strongly predicted individuals’ maternal health awareness [45]. Furthermore, awareness of healthy food intake increases adults’ intentions to engage in weight management [46]. Similarly, a previous health communication study found that college students’ exposure to health news stories on TV is positively associated with their health consciousness (health awareness) [47], which further increased individuals’ healthy food-choice behavior [48]. In sum, it is logical that Chinese college students’ EHWI would increase their level of HWA. This would further affect IAHWCB. The following hypothesis is thus proposed. ## 2.4. The Mediating Role of FPE A handful of digital media and behavior-change studies explored the likelihood that media users evaluate a media message as being more impactful and persuasive to themselves compared to others [23,31]. Such an effect is identified as FPE. However, in this study, FPE refers to when individuals are EHWI and assume that healthy weight issues are much more important compared to other users. Previous studies have demonstrated that exposure to environmental videos increases individuals’ FPE [23]. Additionally, this impacts their intention to partake in COVID-19 preventive behavior [49]. Moreover, Day [50] found that exposure to public affairs advertisements amplified the individual degree of FPE. Furthermore, increased environmental risk (to self) induces the intention to promote pro-environmental news articles via social media [51]. ## 2.5. The Mediating Role of PH Sundar et al. [ 52] indicated that selecting information, liking, and sharing features on social media increase individuals’ perceived control and sense of agency. Moreover, such perceived psychological effects have a positive impact on their knowledge, attitudes, and behavior [53]. One health communication study indicated that frequent social media engagement improved individuals’ HIV awareness and attitude [54]. Parallel to this investigation, numerous studies have also investigated how passive exposure to social media amplifies individuals’ sense of health. For instance, Waddell and Sundar [55] suggested that the bandwagon effect occurs when individuals are exposed to the apparent opinion of the crowd via online comments. Specifically, observing social media metrics (such as likes, comments, and shares) is positively associated with changes in social media users’ attitudes and behaviors [56]. Similarly, scholars have identified the concept of PH as a lens by which media users’ high-risk behaviors can be investigated [57,58]. PH refers to an individuals’ observation of a great number of others performing a certain behavior. This increases the chance that individuals will perform the same behavior they have seen previously [57]. Huang [58] conceptualized this term as WeChat users’ willingness to share a piece of information after being exposed to the most-shared pieces of information. This study investigates how PH manifests in the context of Chinese college students’ IAHWCB; in other words, whether the possibility of imitating others increases if the students witness highly rated (likes and shares) short-form videos of others engaging in healthy weight-related behaviors. Therefore, in this study, PH refers to Chinese college students’ willingness to engage in healthy weight-loss behaviors that are shared and liked by a large number of others on short-form video applications. Previously, communication researchers have demonstrated that exposure to the number of “likes” embedded in online comments triggers individuals to process and evaluate the comments [59]. Ultimately, this increases individuals’ intention to acquire preventive skin behaviors [60]. Moreover, Lim et al. [ 61] found that exposure to nonprofit organizations’ advertisements increased the bandwagon effect (PH), which increased individuals’ intention to engage in non-profit donation. Thus, it is logical that Chinese college students’ EHWI would increase their level of PH. This will further affect their IAHWCB. The following hypothesis is thus proposed. ## 2.6. The Serial Mediating Role of HWA and FPE The above literature demonstrates the direct and mediated effects of EHWI on Chinese college students’ IAHWCB. However, the previous literature suggested a serial mediation mechanism in this relationship. Debatin et al. [ 62] indicated that Facebook users were more likely to participate in protective behaviors if they believed that they could experience negative consequences after using the social media platform. Similarly, the elders’ perception of COVID-19 reduced their degree of optimistic bias regarding COVID-19 infection [63]. In other words, individuals’ perceived awareness increased their presumption that they have a high risk of contracting COVID-19 compared to others (FPE). Considering the studies discussed above, this study aims to further examine the sequential mediation chain of HWA and FPE in the relationship between EHWI and IAHWCB. Hence, the following hypothesis is proposed (Figure 1). ## 3.1. Questionnaire Design The current study adopted 5 measurements (e.g., EHWI, HWA, FPE, PH, and IAHWCB) from previous studies. Nonetheless, the measurements have not yet been applied in the context of Chinese culture. To obtain higher precision and accuracy in the measurements, the current study carefully made modifications based on the following rules. First, each measurement was translated from the original English form into Chinese by two language experts. Once the translation was completed, all researchers compared the original to the translated measurements several times, and came to an agreement regarding the translation’s precision and accuracy [8]. Second, face validity methods were applied to adjust and remove inappropriate items [64]. Therefore, three experts in the field of journalism and media were invited and asked to participate in this evaluation [8]. Lastly, a pre-test was arranged with 10 volunteers, who were recruited from both Shanghai University and the Beijing Institute of Graphic Communication. All volunteers were requested to provide recommendations for further revision, for example, to state whether any items were unreadable or ambiguous [65]. We then uploaded the finalized survey on the online survey platform “Wenjuanxing”. This platform provides a sampling pool of nearly 260 million registered users in China (excluding Tibet and Qinghai province in China). The platform provides great data quality and options that best fit the researcher’s needs. This has been widely adopted in academic research related to China [66,67]. Permission to conduct the current study was reviewed and approved by the Beijing Institute of Graphic Communication Academic Committee [20221129]. The statement of permission was included at the beginning of the online questionnaire. The data were collected from 1 October to 1 December 2022 via Wenjuanxing. Among the 450 potential respondents who received the survey link, 380 filled out the questionnaire. ## 3.2. Measurement of Variables All the measurements were evaluated using a five-point Likert scale. Answers ranged from strongly disagree to strongly agree or from very rarely to very frequently. EHWI was adopted according to the definition of Shen et al. [ 68]. This was measured as an index of how often respondents watch the topics “fat and sugar reduction,” “Low-calorie recipes,” “Nutritionist recommendation for weight loss,” and “fat-burning workout,” in short-form videos on the following six applications: (a) Douyin, (b) WeChat short video, (c) Kuaishou, (d) Watermelon video, (e) Volcano video, and (f) Meipai. Those items were added to create an EHWI measurement on a five-point Likert scale ($M = 2.80$, SD = 1.00, α = 0.86). To observe HWA, the current study adapted the health-awareness scale of [44]. Four items were assessed by five-point Likert scale, with statements such as “Failure to control weight can lead to a number of diseases (e.g., cardiovascular disease, digestive system function),”and “Obesity causes irreversible diseases and increases the risk of getting cancers.” ( $M = 4.00$, SD = 0.90, α = 0.90). FPE measures how the importance of maintaining a healthy weight could affect participants, with four items derived from [23]. Participants were asked to respond to a five-point Likert scale. Items included statements such as “I understand that maintaining healthy weight is critically important,” and “I am aware of the consequences of not controlling my weight could lead seriously damage to my body” ($M = 3.50$, SD = 1.10, α = 0.90). PH was measured as an index of individuals’ willingness to engage in healthy weight-loss behaviors, which are shared and liked by many others on short-form video applications. The four items were revised from the scale of PH from a previous study [58]. Examples of these statements are “The more “likes” a short-form video gets, the more I will be willing to control my weight and stay healthy,” and “The more “shares” a short-form video gets, the more I will be willing to control my weight and stay healthy” ($M = 3.20$, SD = 1.12, α = 0.93). IAHWCB was operationalized with four items, which were adapted from Lampard [39]. Example items are “I intend to engage in aerobic or anaerobic exercise in the near future,” and “I have it in my mind that I would limit the consumption of high sugar foods and beverages” ($M = 3.11$, SD = 0.90, α = 0.80). ## 4.1. Descriptive Data Three-hundred-and-eighty valid responses were collected. The demographic characteristics of the survey participants are outlined in Table 1. Respondents were mostly Female ($$n = 197$$, $51.8\%$), single ($$n = 347$$, $91.3\%$), and were either undergraduates ($$n = 246$$, $64.7\%$) or master’s students ($$n = 59$$, $15.5\%$). The respondents’ age range varied from 18 to 21 years old ($$n = 304$$, $80.0\%$), and their monthly income ranged from RMB 1000 to RMB 6999 ($$n = 219$$, $57.6\%$). The bivariate association among the independent, dependent, and control variables (gender, education, and income) can be seen Table 2. ## 4.2. Direct and Mediated Effect Test To test Hypothesis 1, this study applied a hierarchical regression analysis with IAHWCB as a dependent variable. Gender, education, and income were entered into the first block as controlling confounders, and EHWI was entered in the second block. The effect of Chinese college students’ EHWI on IAHWCB was significant (β = 0.45, $p \leq 0.001$). Figure 2 indicates the standardized coefficients and significance for each path in the hypothesized model. Hayes’s PROCESS macro (Model 4) was applied to test the mediation analysis of HWA, FPE, and PH on the relationship between EHWI and Chinese college students’ IAHWCB. This study then applied bootstrapping to obtain bias-corrected $95\%$ confidence intervals to make statistical inferences about specific indirect effects [69]. In the first mediation model, HWA positively predicted IAHWCB (β = 0.10, $p \leq 0.01$). In addition, EHWI positively predicted IAHWCB (β = 0.43, $p \leq 0.001$). The mediation effect test showed an indirect effect of EHWI on IAHWCB, mediated by HWA (β = 0.02, $p \leq 0.001$, $95\%$ CI [0.01, 0.05]). Therefore, the indirect effect was significant, and the partial mediation effect of HWA was confirmed. The second mediation model’s result indicated that FPE positively predicted IAHWCB (β = 0.32, $p \leq 0.001$). Meanwhile, EHWI positively predicted IAHWCB (β = 0.27, $p \leq 0.001$). The mediation effect test showed an indirect effect of EHWI on IAHWCB, mediated by FPE (β = 0.19, $p \leq 0.001$, $95\%$ CI [0.13, 0.25]). Therefore, the indirect effect was significant and the partial mediation effect of FPE was confirmed. The third mediation model demonstrated that PH positively predicted IAHWCB (β = 0.29, $p \leq 0.001$). Furthermore, EHWI positively predicted IAHWCB (β = 0.30, $p \leq 0.001$). The mediation effect test showed an indirect effect of EHWI on IAHWCB, mediated by PH (β = 0.16, $p \leq 0.001$, $95\%$ CI [0.10, 0.22]). Thus, the indirect effect was significant, and the partial mediation effect of PH was confirmed. ## 4.3. Serial Mediating Effects Test The PROCESS macro (Model 6) was applied to test the serial mediating effects of HWA and FPE in the relationship between EHWI and Chinese college students’ IAHWCB. The results demonstrated that EHWI positively predicted HWA (β = 0.24, $p \leq 0.001$), FPE (β = 0.49, $p \leq 0.001$) and IAHWCB (β = 0.27, $p \leq 0.001$) (Table 3). HWA positively predicted FPE (β = 0.43, $p \leq 0.001$), but did not predict IAHWCB (β = 0.04, $$p \leq 0.40$$). Lastly, FPE positively predicted IAHWCB (β = 0.34, $p \leq 0.001$). A total of 5000 bootstrap estimates were applied to construct the $95\%$ confidence intervals for the indirect effects [70]. The serial mediating effect of HWA and FPE on the relationship between EHWI and IAHWCB was significant (β = 0.03, $p \leq 0.001$, $95\%$ CI [0.02, 0.06]). ## 5. Discussion By applying the third-person effect theory as a guideline, this study further investigated the effect of EHWI on short-form video applications on IAHWCB among Chinese college students (20–34 years old). The main objective of the current study is an in-depth exploration of the direct and indirect effects of critical factors (HWA, FPE and PH) on such a relationship. Moreover, this study aims to further examine the sequential mediation chain of HWA and FPE in the relationship between EHWI and IAHWCB. In terms of direct effects, EHWI was positively associated with Chinese college students’ IAHWCB. The finding is in line with previous studies that indicated that exposure to media information amplified individuals’ healthy weight-related behaviors [40,41,42]. For instance, Pan [41] suggested that the more individuals are exposed to weight-related messages, the higher their willingness is to participate in weight-loss activities. Regarding mediated effects, the finding of H2 indicated that HWA mediated the relationship between EHWI and Chinese college students’ IAHWCB. This finding is consistent with prior studies [45,47,48], which found that college students’ exposure to health-related news content on TV increased their degree of health awareness. Ultimately, this increased their adoption of healthy food-choice behaviors [47,48]. FPE explained that individuals are more likely to presume that a media message is more persuasive to themselves compared to others [31]. A prior study has indicated that exposure to public affairs-related information induced a higher level of FPE in individuals. Ultimately, increased FPE amplifies people’s intention to share pro-environmental-related news on social media [51]. It is logical that FPE could play the role of the mediator in the relationship between perceived information and individuals’ intention to acquire behaviors. This study also found that FPE mediated the relationship between EHWI and Chinese college students’ IAHWCB (H3). This finding is consistent with the previous literature, suggesting that individuals’ exposure to environmental-related videos increased their degree of FPE [23]. Moreover, Mesch et al. [ 49] found that FPE is positively associated with the intention to engage in COVID-19-preventive behavior. Lastly, the findings of H4 demonstrated that PH mediated the relationship between EHWI and Chinese college students’ IAHWCB. This finding is in line with earlier studies, which have shown that individuals who are exposed to highly liked online comments have a more positive judgment of the comments [59]. Additionally, individuals’ PH encouraged them to acquire preventive skin behaviors [60]. Individuals’ maintenance of weight-loss behavior is considered to be a complex interaction that involves multiple mechanisms. Specifically, cognitive factors are recognized as unique factors that influence such behaviors [71]. For instance, when individuals perceive a health threat, their cognitive processes tend to deal with the threat by themselves, as compared to other cognitive processes. Additionally, a recent study found that individuals who were exposed to fear-related messages had an increased health awareness of COVID-19, which amplified their degree of vigilance [72]. Vigilance is defined as being very careful to notice things or watchful for whatever may occur [73]. This cognitive factor is seemingly related to FPE, in which individuals tend to presume the degree to which healthy weight issues are much more important than others. Ultimately, this effect was positively associated with the intention to promote pro-environmental news articles via social media [51]. In sum, as the findings of the current study demonstrate, Chinese college students’ EHWI on short-form video applications increases their level of HWA, which reinforces their degree of FPE. Furthermore, increased FPE is correlated with IAHWCB. The recommended practical implications are as follows. Firstly, EHWI can drive HWA, which further reinforced Chinese college students’ IAHWCB. Therefore, this study suggests that Chinese short-form video applications motivate content creators to create various types of healthy weight-related short-form videos. This may further impact Chinese college students’ salience of healthy weight literacy and related knowledge. Secondly, the prior study suggested that social networking provided peer social support, which encouraged others to acquire weight-loss behaviors [74]. Moreover, Chung et al. [ 75] indicated that the fact that college students frequently engage in conversation via Facebook led to their intention to become involved in weight-loss behaviors. Thus, Chinese university teachers and family members should encourage college students to connect and actively engage with their peers to include healthy weight-related topics in short-form video applications (e.g., commenting and sharing educational content together). Lastly, a previous study demonstrated that during the COVID-19 pandemic, many misleading short-form videos were disseminated [76]. In most cases, these platforms contain both verified and unverified health information. In response to this issue, a previous internet and educational study has suggested improving college students’ information literacy, so that they have the ability to determine what is correct information, and what is misleading or fake information [77]. Therefore, Chinese college teachers should be encouraged to utilize their courses to discuss fake information on short-form video applications and how to recognize it. There are several limitations to this study that should also be mentioned. First, the current study was a cross-sectional study. This is limited to the identification of each factor’s causal relationship (both direct and mediated effects). Future studies should conduct deeper investigations, such as longitudinal quantitative studies. Second, the current study IAHWCB was adopted from Lampard [39], and there are only four items representing Chinese college students’ intention to acquire healthy weight-control behaviors. However, the limited number of items makes it difficult to measure their actual intention to engage in all types of healthy weight-related behaviors. Thus, more precise measurements should be operationalized in future studies. Despite this study expanding the knowledge of information avoidance behavior by empirically examining the effect of three key determinants on the relationship between EHWI and IAHWCB, it has not yet fully considered health as a “dialogic process” which triggers individuals’ pathologies or actions in health communication [78]. Therefore, it is important to further explore how individuals are persuaded by health information via linguistic features. For example, one unique study applied Proppy’s architecture and fully evaluated how political information features (such as N-gram, Lexicon, and vocabulary richness) could gain individuals’ attention and further change their opinions [79]. Therefore, examining the effect of persuasion of EHWI on individuals’ IAHWCB will extend the current knowledge of individuals’ IAHWCB. In addition, future research needs to further investigate the influence of normative and cognitive factors: for instance, how injunctive norms and descriptive norms directly, indirectly or sequentially mediate the relationship between EHWI and Chinese college students’ IAHWCB. Additionally, they should test whether an individual’s optimistic bias (optimistic bias is people’s tendency to overestimate their likelihood of experiencing positive events and underestimate their likelihood of experiencing negative events in the future) is positively or negatively associated with IAHWCB. These potential factors will fill the gap in understanding the effect of short-form video media on media users’ behavior changes. ## 6. Conclusions The current study took a critical step to explore direct and mediated mechanisms in the relationship between EHWI on short-form video applications IAHWCB among Chinese college students (20–34 years old), a critical and understudied phenomenon that has threatened this population’s motivation to maintain a healthy weight amid COVID-19 lockdowns in China. First, the current study confirms that EHWI has a direct impact on Chinese college students’ IAHWCB. Second, this study also found that a mediated effect exists on the relationship between EHWI and IAHWCB via HWA, FPE, and PH. That is to say, unlike other determinants, EHWI on short-form applications increases Chinese college students’ HWA, FPE, and PH. Ultimately, these factors amplify their IAHWCB. Lastly, the result also demonstrated that there is a serial mediation mechanism in HWA and FPE for linking EHWI and Chinese college students’ IAHWCB, which successfully supports the previous evidence [62,63]. ## References 1. 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--- title: 'Going Vegan for the Gain: A Cross-Sectional Study of Vegan Diets in Bodybuilders during Different Preparation Phases' authors: - Stefano Amatori - Chiara Callarelli - Erica Gobbi - Alexander Bertuccioli - Sabrina Donati Zeppa - Davide Sisti - Marco B. L. Rocchi - Fabrizio Perroni journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048841 doi: 10.3390/ijerph20065187 license: CC BY 4.0 --- # Going Vegan for the Gain: A Cross-Sectional Study of Vegan Diets in Bodybuilders during Different Preparation Phases ## Abstract Numerous athletes compete at a high level without consuming animal products; although a well-planned vegan diet might be appropriate for all stages of the life cycle, a few elements need to be addressed to build a balanced plant-based diet for an athlete, particularly in bodybuilding, in which muscle growth should be maximised, as athletes are judged on their aesthetics. In this observational study, nutritional intakes were compared in a cohort of natural omnivorous and vegan bodybuilders, during two different phases of preparation. To this end, 18 male and female bodybuilders (8 vegans and 10 omnivores) completed a food diary for 5 days during the bulking and cutting phases of their preparation. A mixed-model analysis was used to compare macro- and micronutrient intakes between the groups in the two phases. Both vegans and omnivores behaved similarly regarding energy, carbohydrate, and fat intakes, but vegans decreased their protein intake during the cutting phase. Our results suggest that vegan bodybuilders may find difficulties in reaching protein needs while undergoing a caloric deficit, and they might benefit from nutritional professionals’ assistance to bridge the gap between the assumed proteins and those needed to maintain muscle mass through better nutrition and supplementation planning. ## 1. Introduction Nowadays, numerous athletes compete at a high level without consuming animal products, guided by ethical and health reasons [1,2]. A well-planned vegan diet that considers the management of critical nutrients could be appropriate for all stages of the life cycle, as well as for athletes [3], as there seem to be no differences between plant-based and omnivorous diets when comparing physical performance [4]. However, a few elements need to be addressed to build a balanced plant-based diet for an athlete. Plant-based diets are known to promote early satiation and reduced appetite due to many plant foods’ high amounts of fibre and low-calorie density. While this could be positive while trying to lose weight, it can become an issue when the athlete’s energy balance needs to be managed [5]. Vegan athletes should pay attention to the quantity and quality of the protein ingested, given that plant-based proteins are often incomplete: Indeed, plant-based proteins typically contain fewer essential amino acids (EAAs) than their animal-based equivalents [6]. Nonetheless, combining different plant protein sources makes it possible to improve the overall quality of protein meals since the amino acid composition of the various plant proteins can complement each other [3]. In addition, the digestibility of plant-based proteins appears to be significantly lower than that of animal products due to anti-nutritional factors such as phytic acid and trypsin inhibitors, oxalates, phenolic compounds, and tannins, which limit the absorption of nutrients [7,8]. However, simple domestic preparation methods such as cooking, germination, fermentation, soaking, and dehulling can reduce the anti-nutritional factors improving protein bioavailability [9]. A properly designed vegan diet seems to be able to meet the protein needs of an endurance athlete via whole foods alone; however, this may not be ideal for maximising muscle growth, as the protein demands for these objectives are higher [5,7]. Indeed, plant protein quality has been reported to likely limit the stimulation of muscle protein synthesis and, subsequently, the gains in muscle mass [10,11]. In competitive bodybuilding, athletes are judged on their aesthetics: muscle size, muscular proportions, and conditioning (the absence of body fat). To achieve the required physique, athletes undertake intensive resistance training and dietary manipulations to increase muscle mass and reduce fat mass. The nutritional strategies vary according to an athlete’s competitive cycle phase. The two primary phases of a bodybuilder’s competitive cycle are the muscle-gaining phase, also called “bulking”, and the contest preparation phase, also known as “cutting”. During the bulking phase, the goal is to increase muscle mass without adding unnecessary body fat. To achieve that, athletes resort to resistance training and the maintenance of a positive energy balance, with a protein requirement ranging from 1.6 to 2.2 g/kg of body mass [12]. The cutting phase involves a reduction in body fat and the maintenance of the muscle mass gained through the bulking phase. During this phase, in addition to regular resistance training, most bodybuilders follow a high protein (2.3–3.1 g/kg of body mass), calorie-restricted diet, aerobic exercise, and isometric “posing practice” to prepare for the mandatory physique poses that judges use to place competitors [13,14]. To reach the high protein targets, “traditional” bodybuilding diets include a considerable amount of animal-source food and resort to high-quality protein supplements such as whey protein powder [15]. Whey proteins are considered to be high-quality proteins due to their digestibility and quantity of essential amino acids, providing the proteins in correct ratios for human consumption [16]. This presents a challenge for bodybuilders following a vegan diet since vegans avoid common protein sources, such as meat and dairy products. Foods high in plant-based proteins such as seitan, tofu, tempeh, and new meat alternatives can be beneficial while building a high-protein diet, and supplemental vegan protein powders can also help meet the needs by providing concentrated sources of protein surrounding workouts and throughout the day [7,17]. Emerging data support the efficacy of vegan options of protein powder in fostering muscle hypertrophy post-resistance exercise, improving indices of body composition and exercise performance [18,19,20]. With these premises, we investigated whether it was possible to follow a vegan diet and, at the same time, meet the nutritional needs of bodybuilders. Most of the scientific literature concerning vegan diets in athletes is limited to endurance sports. This observational study compared nutritional intakes in a cohort of natural omnivorous and vegan bodybuilders. The study aimed to highlight the differences between vegan and omnivorous bodybuilders in the intake of macronutrients and micronutrients at different stages of preparation for a bodybuilding competition. ## 2.1. Participants For this study, a convenient sample of competitive bodybuilders from northern Italy was invited to participate through word of mouth and social network announcements. Twenty-two subjects expressed their interest, twenty accepted to participate after receiving the procedures’ explanation, and eighteen correctly completed the data collection. All subjects had been practising bodybuilding for several years, and they were affiliated with the Italian World Natural Bodybuilding Federation (WNBF). The participants were divided according to their diet into a group of vegans and a control group of omnivores. All participants were informed of the study’s aims and signed a written informed consent form to participate. The study was approved by the Human Research Ethical Committee of Urbino University (approval no. 31_2020) and was conducted in accordance with the Declaration of Helsinki. ## 2.2. Data Sources and Measurements The participants completed an initial online questionnaire on anthropometric measurements, years of training and competition, training hours per week, and diet type. The participants were asked to record their food intake on an online food diary over a 5-day period at two points during the competition preparation, the muscle-gaining phase (bulking), and the contest preparation phase (cutting), in order to be able to highlight the differences in dietary composition between the two phases. The period for completing the diary differed for the athletes depending on each competitive calendar; the data collection period during the bulking and cutting phases was two months apart for all athletes. Food quantities were recorded in grams and/or portions, and dietary/sports supplement consumption was also requested; the missing data from the questionnaire and clarifications on food consumed/portions were followed up via email or phone call. The dietary analysis of the participants’ diets was performed using the WinFood (WinFood, San Giovanni, Teramo, Italy) nutritional analysis software. The daily intake of macronutrients was reported in grams per kilogram of body weight (g/kg/day). Micronutrients such as calcium, sodium, and iron were calculated in milligrams per kilogram of body weight per day (mg/kg/day), while vitamin D and vitamin B12 in micrograms per kilogram of body weight per day (mcg/kg/day). The investigated micronutrients were selected as being of critical importance for a plant-based diet and of greatest interest for testing differences between diets. The macronutrients of the supplements were included in the analysis based on the manufacturer’s specifications on the brands’ websites. Different sources were used to compare whether the dietary intakes (calories and macro- and micronutrients) were in line with the recommended dietary allowances (RDAs) present in the literature. In the case in which specific recommendations for bodybuilders were present, such as for protein, sport-specific recommendations were chosen [12,14]. Regarding the dietary intakes of critical micronutrients, as no specific recommendations for bodybuilders were present, the RDAs provided by the Italian Society of Human Nutrition for omnivores [21] and vegetarian/vegan diets [22] were used. ## 2.3. Statistical Analyses Data were reported as mean ± standard deviation or median (first and third quartile) when appropriate. A multivariate analysis of variance was conducted to compare the characteristics of the two groups at baseline. Comparisons between omnivorous and vegan athletes in the two preparation phases (bulking vs. cutting) were performed using a mixed-model analysis, with athletes as a random factor, diet (omnivore vs. vegan) and preparation phase (bulking vs. cutting) as fixed factors and days (1 to 5 days of dietary recording) as a repeated measure; macronutrients and micronutrients were the dependent variables. The effects of diet, phase, and diet × phase interaction were examined. Furthermore, a one-sample Hotelling T2 test was used to test if athletes respected the RDAs (expected values) according to their diet and the preparation phase; post hoc analyses were performed using one-sample t-tests with Bonferroni correction. Statistical significance was set at alpha = 0.05, and the analyses were performed using SPSS Statistical Package for Social Sciences v26 (IBM, Armonk, NY, USA) and RStudio 4.1.1 (Posit, Boston, MA, USA). ## 3.1. Participants’ Characteristics In total, 18 bodybuilders (11 males and 7 females; age = 34.8 ± 6.4 years old; height = 173.3 ± 8.9 cm; body mass = 72.3 ± 12.6 kg; fat mass percentage = 12.3 ± $3.4\%$) took part in this study. The participants had been practising bodybuilding for 6 years (Q1–Q3: 3.2–7) and trained for 8 h per week (Q1–Q3: 7–8). After the initial questionnaire, they were divided according to their dietary regimen into vegans (5 males and 3 females) and controls (omnivores; 6 males and 4 females). The vegans reported being on a plant-based diet for 2 to 11 years. No significant differences were reported at baseline between the two groups for none of the investigated characteristics. ## 3.2. Dietary Differences between Omnivore and Vegan Bodybuilders The mean macronutrient, micronutrient, and energy intake scaled for body mass are reported in Table 1 and were separately examined for the bulking phase and cutting phase of the competitive cycle. The results revealed a significant reduction in many dietary components as preparation progressed (phase effect). A reduction in calories (F[1,160] = 956.8, $p \leq 0.001$), carbohydrates (F[1,160] = 510.4, $p \leq 0.001$), fats (F[1,160] = 56.6, $p \leq 0.001$), and its saturated, monounsaturated, and polyunsaturated components (F[1,160] = 7.1, $$p \leq 0.009$$; F[1,160] = 21.1, $p \leq 0.001$; F[1,160] = 5.8, $$p \leq 0.017$$, respectively) was observed between the preparation phases of both groups. A significant reduction in calcium (F[1,160] = 9.1, $$p \leq 0.003$$), iron (F[1,160] = 16.7, $p \leq 0.001$), and vitamin D (F[1,160] = 5.4, $$p \leq 0.022$$) was also observed between the phases. Omnivores consumed significantly more protein than vegans (F[1,16] = 5.4, $$p \leq 0.033$$). The analysis indicated greater protein intake scaled to body mass among omnivores compared with vegans, and these groups changed the protein intake differently throughout the preparation phases (diet × phase: F[1,160] = 11.6, $$p \leq 0.001$$). Indeed, omnivores increased their protein intake in the cutting phase compared with the bulking phase (2.24 vs. 2.57 g/kg/day), while the vegans reduced it (2.23 vs. 1.78 g/kg/day). Although no difference was observed in the consumption of carbohydrates between the different diets, its reduction over preparation phases revealed significant differences between the omnivore and vegan groups (diet × phase: F[1,160] = 11.6, $$p \leq 0.001$$). No other significant differences were detected between the two groups. ## 3.3. Achievement of Recommended Dietary Allowances The dietary intakes were investigated on whether they were in line with the recommendations present in the literature. The thresholds provided by Iraki et al. [ 12] for the bulking phase and Helms et al. [ 14] for the cutting phase about protein intake in bodybuilders were used. Moreover, the dietary intakes of critical micronutrients were compared with the RDAs provided by the Italian Society of Human Nutrition (SINU) for omnivores [21] and vegetarian/vegan diets [22]. The Hotelling T2 tests were all statistically significant for all the groups tested (omnivore bulking, omnivore cutting, vegan bulking, and vegan cutting) ($p \leq 0.01$). The sample data are expressed with $95\%$ confidence intervals. ## 3.3.1. Protein During the bulking phase, the omnivore group consumed protein ranging from 2.2 to 2.7 g/kg/day, while vegans consumed 1.9–2.5 g/kg/day. The results revealed that both omnivores and vegans during the bulking phase reached the recommended protein range of 1.6–2.2 g/kg/day, and in some cases, these values were exceeded ($t = 3.77$, $$p \leq 0.04$$ for omnivores; $t = 1.59$, $$p \leq 0.16$$ for vegans). The recommended protein ranges for the cutting phase were 2.3–3.1 g/kg/day; vegans (1.6–2.2 g/kg/day) failed to reach the protein requirements (t = −5.42, $$p \leq 0.001$$), while the omnivores (2.2–2.7 g/kg/day) remained within this recommendation (t = −1.78, $$p \leq 0.11$$). A graphic representation of the protein intake in relation to guidance is shown in Figure 1. ## 3.3.2. Calcium The RDA for calcium is 1000 mg/day regardless of the type of diet and phase; neither omnivores nor vegans reached this value during bulking (384–694 mg/day vs. 336–567 mg/day) or cutting (313–573 mg/day vs. 310–505 mg/day) ($p \leq 0.001$ for all comparisons). ## 3.3.3. Vitamins The RDA for vitamin D is 15 mcg/day regardless of the type of diet and phase. Our results showed that the intake of vitamin D in the omnivore and vegan groups was highly variable and shifted over a wide range, including RDA cut-off in three of four observations, which were omnivore bulking (1.1–22.3 mcg/day), vegan bulking (5.9–31.9 mcg/day), and vegan cutting (5.7–31.8 mcg/day), while omnivore cutting failed to include the cut-off (0–12.7 mcg/day; $p \leq 0.05$). Our observations of vitamin B12 intake were considerably different, despite none of the two groups reaching the RDA cut-off for B12 (2.4 mcg/day) ($p \leq 0.001$). Notably, the results showed that while omnivores had little B12 intake in both bulking and cutting (0–0.92; 0.06–0.95 mcg/day), the vegan group had largely higher intake during both phases due to the daily supplementation of 50 mcg of vitamin B12 by all the athletes. ## 3.3.4. Iron For omnivore adults, iron intakes of 10 mg/day for males and 18 mg/day for females are suggested [21]. Recommendations for vegetarians indicate that iron intakes increased by $80\%$, achieving 18 mg/day for males and 33 mg/day for females [22]. Omnivore males’ intake exceeded RDA (13.9–17.9 mg/day) ($t = 3.35$, $$p \leq 0.006$$), while omnivore females failed to reach it (9.3–11.5 mg/day) (t = −7.84, $p \leq 0.001$). Vegan males reached the recommended iron intake (18.8–25.2 mg/day) ($t = 1.41$, $$p \leq 0.19$$), and in some cases, these values were exceeded; by contrast, vegan females did not reach RDA (11.7–17.9 mg/day) (t = −6.93, $$p \leq 0.001$$). ## 3.3.5. Zinc Both male and female omnivores’ zinc intake (10.4–13.8 mg/day; 7.7–10.8 mg/day) did not differ from the one recommended (12 mg/day for males; 9 mg/day for females). Vegan males and females failed to reach zinc RDA (18 mg/day for males; 13.5 mg/day for females) with an intake of 8.6–12.7 mg/day (t = −3.94, $$p \leq 0.003$$) and 3.2–6.4 mg/day (t = −6.40, $$p \leq 0.001$$). ## 4. Discussion This study aimed to determine if the intakes of macronutrients and micronutrients differ between omnivore and vegan bodybuilders at different stages of preparation for competition. As expected, the energy intake of all the athletes was higher at the start of the competitive cycle during the bulking phase than that at the end of the cutting phase, as reported in previous studies [23]. Both vegans and omnivores behaved similarly when looking at their energy intake. The average caloric reduction for the omnivore group was 8.5 kcal/kg/day, corresponding to a deficit of 700 kcal/day for males and 510 kcal/day for females. Likewise, the vegans, who had shown an average caloric reduction of 7.9 kcal/kg/day, reached a deficit of 665 kcal/day for males and 385 kcal/day for females. In contrast, two case studies of bodybuilders reported a higher reduction in energy intake between 882 and 1300 kcal [24,25], while others had more similar results, reporting 554 kcal [26]. Smaller reductions in caloric intake are preferred rather than higher deficits, as they are intended to counteract metabolic adaptations to dieting and better preserve muscle mass. The caloric deficit should result in a weight loss of approximately $0.5\%$ to $1\%$ weekly, and this dieting period should be tailored to the competitor based on the starting body fat percentage [14]. The weekly weight loss in the present study was estimated to be $0.83\%$ in omnivore males, $0.88\%$ in omnivore females, $0.86\%$ in vegan males, and $0.66\%$ in vegan females: These values were within the recommended range, meaning that both groups respected weight loss guidance for preserving lean body mass. A summary of dietary recommendations for bodybuilding in the two main phases of the competitive cycle is reported in Table 2. An adequate carbohydrate intake during the competitive cycle is needed to maintain muscle glycogen and enhance resistance training performance [27]. As expected, carbohydrate was the most abundant macronutrient consumed across all the preparation phases in both vegans and omnivores. Their intake during the bulking phase was in line with the recommendations indicated by Iraki et al. [ 12] (≥3–5 g/kg/day) and was reduced from the start to the end of contest preparation, reflecting the practice previously reported in bodybuilding case studies [24]. In our cohort, omnivores had reduced carbohydrates more than vegans while transitioning into the cutting phase, shifting from 5.1 to 3.5 g/kg /day compared with 4.8 to 3.4 g/kg/day for vegans. The protein intakes derived from whole foods and supplements were between 2.2 g/kg/day and 2.7 g/kg/day amongst bulking omnivores and between 1.9 g/kg/day and 2.5 g/kg/day amongst bulking vegans reaching, and for the majority exceeding, the recommended protein range (1.6–2.2 g/kg/day) suggested by Iraki et al. [ 12]; however, these values are similar to reports from other case studies (2.2 to 3.5 g/kg BW) [25,28]. Although we did not collect data to verify muscle growth, evidence suggests that while adequate amounts of protein are consumed, the dietary protein source does not affect resistance-training-induced adaptations [29]. Since the vegan group had no problem reaching the protein requirements while bulking, this will likely reflect a successful increase in muscle mass and support of muscle strength. However, regarding protein mixtures, the non-equivalence between plant-based protein isolates and whey protein has been demonstrated, highlighting that many plant products contain insufficient amounts of leucine, which is the pivotal element in the anabolic potential. Blends of various plant-based protein sources may provide protein characteristics that closely reflect the typical characteristics of animal-based protein sources and are preferable to protein isolates from a single vegetable source [30,31]. During energy restriction in the cutting phase, prioritising protein over other macronutrients is a smart practice, as high-protein diets are known to spare muscle mass during energy deficits. Indeed, a recent systematic review on bodybuilding contest preparation recommended keeping protein intakes between 2.3 and 3.1 g/kg while cutting calories [14]. In the present study, we observed that, as suggested, the omnivore group maintained their protein intake unchanged (2.2–2.7 g/kg/day) during the cutting phase. Conversely, a protein intake of 1.6–2.2 g/kg/day was observed in the vegan group, which is below the guidelines. Furthermore, considering the above-described limitations of plant proteins (e.g., bioavailability and amino acid profile), vegan athletes should aim for protein intakes toward the higher end of the recommendations [7]. It should be pointed out that a reduction in protein after switching to the cutting phase was detected in all vegan participants; this trend, which occurred during a caloric deficit, could likely put vegan bodybuilders at risk of losing the muscle mass they successfully built during the bulking phase. Most foods are not composed exclusively of one macronutrient, i.e., only proteins, carbohydrates, or fats, but from a combination of the three. The vegan diet does not rely on commonly known protein-rich foods, such as meat, fish, eggs, and dairy products. Vegans obtain proteins from a variety of foods whose principal macronutrients may not always be proteins, for instance, legumes and cereals [32]. While looking for a caloric deficit during the cutting phase, it is common practice to reduce the consumption of cereals, as also seen being reported in other case studies [24,33]; this may have contributed to an overall reduction in protein intake in the vegan bodybuilders because cereals would have added a non-negligible amount of protein to their diet. Noting this critical issue, a smart strategy for a vegan bodybuilder looking for a caloric deficit might be to not only reduce the consumption of typical cereals (such as rice and pasta) but to replace them with their richest protein equivalents (i.e., pseudocereals). Pseudocereals, such as quinoa, amaranth, and buckwheat, even though not frequently consumed foods, have a high protein quantity (16.3, 13.5, and 13.1 g/100 g, respectively) and a higher quality score, as they have leucine as the most abundant EAA [34]. Moreover, increasing plant-protein supplementation while in a caloric deficit might be helpful since consuming a significantly greater amount of protein from whole foods would increase energy intake. To be able to help meet the high demand for protein from plant sources, a technological approach is needed to formulate “new foods”, which might help in combining the different requirements that are difficult to achieve with conventional foods [35]. Therefore, our results align with those of Hevia-Larraín et al. [ 29] and suggest that a vegan diet with the supplementation of isolated plant-based proteins can achieve adequate total protein intake and be suitable for a bodybuilder aiming to increase muscle mass. However, our results also show that the dietary manipulations adopted by bodybuilders to lose fat and maintain lean mass may not be successful on a vegan diet, despite supplementation, since reaching protein intakes is challenging while cutting calories. In both groups, fat intake was the lowest amongst the three macronutrients, and like carbohydrates, it was reduced over time, shifting from bulking to cutting (0.74 to 0.62 g/kg/day for omnivores and from 0.83 to 0.69 g/kg/day for vegans). There was a tendency for the competitors in this cohort to favour low-fat diets; those values are consistent with other cross-sectional studies of bodybuilders [13,23,26] and reflect the low end of the total energy recommendations for the fat intake proposed for bodybuilding for the bulking phase (20–$35\%$) and the cutting phase (15–$30\%$) [12,14]. These results point out an insignificant difference between the two diets; however, regarding the quality of fats, the vegan diet was richer in polyunsaturated fats than the omnivorous one across the phases. This result was expected, as plant-based diets are known to be richer in polyunsaturated fats compared with omnivorous diets [36]. Given the monotonous nature of bodybuilders’ diets, it was of interest to investigate the adequacy of their diets in terms of some micronutrients, which have been reported as particularly critical in vegan subjects. Both omnivorous and vegan bodybuilders did not reach the RDAs for calcium with diet in any of the two stages of preparation for competition; this can be attributed to a lack of variety in the diet, as was already observed in another study on the food selection patterns of bodybuilders [15]. Regarding iron intakes, both omnivores and vegan males successfully reached the recommended intakes, despite the guidance for vegetarians being increased by $80\%$ since non-heme iron from vegetables is less easily assimilated by the body than the heme iron found in meat. Moreover, vegan males consumed more iron than their counterparts, confirming the tendency already observed in other observational studies comparing vegan and omnivore diets [36]. All the females participating in this study failed to reach the recommended intakes, increasing their risk of iron deficiency. The iron RDAs for omnivore and vegan females are higher than for males due to the menstrual cycle. Moreover, it is worth mentioning that the low energy availability during the cutting phase may place female athletes at risk of developing amenorrhea, leading to a multi-system dysregulation known as relative energy deficiency in sport (RED-S) [37]. On the adequacy of zinc intake, neither male nor female vegans reached the guidelines, and although both the mean intake of omnivore males and females were found to not significantly differ from the one recommended, only half of them successfully met RDAs. Vegan bodybuilders seemed aware of the necessity of supplementing vitamin B12 and cared to be explicit that they were taking appropriate daily oral supplementation. Vitamin B12 deficiency, also called cobalamin, is common among vegetarians because this essential vitamin is found only in animal foods. Vitamin B12, as a cofactor, is involved in the metabolism of amino acids, nucleic acids, and fatty acids. It plays a crucial role in producing red blood cells and bone marrow formation. Cobalamin deficiency can cause anaemia through alterations in erythropoiesis and nerve transmission via the inhibition of myelin formation [38]. The omnivore counterpart did not meet B12 RDA; however, we should point out that the nutritional analysis software used to examine the diets did not have sufficient nutritional information on all foods to estimate the content of this micronutrient. The results showed that the intake of vitamin D in the omnivore and vegan groups was highly variable due to few participants taking supplements. However, assessing vitamin D status only through diet is highly limiting, as other factors, such as the amount and intensity of sun exposure, have a greater effect on vitamin D status than a diet [39]. Due to the limitations mentioned above, the findings of this study require corroboration and should be interpreted with caution. Another limitation that should be acknowledged is that participants self-reported their anthropometric data (height, body mass, and fat percentage), so alterations in these parameters due to different instruments, operators, or techniques could not be evaluated. However, this paper provides a contemporary picture of the current bodybuilding landscape in which the vegan lifestyle is starting to make its way. ## 5. Conclusions The appearance of vegan bodybuilders is due to the need of some athletes to combine their ethical principles with the desire to achieve a competitive muscular physique, but whether a vegan diet could be suitable for an athlete competing in bodybuilding was not assessed before. Our results suggest that vegan bodybuilders may find difficulties in reaching protein needs while undergoing a caloric deficit, and they might benefit from nutritional professionals’ assistance to bridge the gap between the assumed proteins and those needed to maintain muscle mass through better nutrition and supplementation planning. However, bodybuilders’ diets are not considered health-promoting diets and—like other dietary regimens adopted by athletes—are designed to improve performance and, in this case, to increase the potential to build a competitive muscular physique. Given the lack of research into veganism in bodybuilding, our findings will likely interest bodybuilders seeking to follow a vegan diet. 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--- title: 'Realfood and Cancer: Analysis of the Reliability and Quality of YouTube Content' authors: - Sergio Segado-Fernández - Ivan Herrera-Peco - Beatriz Jiménez-Gómez - Carlos Ruiz Núñez - Pedro Jesús Jiménez-Hidalgo - Elvira Benítez de Gracia - Liliana G. González-Rodríguez - Cristina Torres-Ramírez - María del Carmen Lozano-Estevan journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048849 doi: 10.3390/ijerph20065046 license: CC BY 4.0 --- # Realfood and Cancer: Analysis of the Reliability and Quality of YouTube Content ## Abstract This study analyzes the quality and reliability of videos related to nutrition and cancer on YouTube. Study Design: An observational, retrospective, cross-sectional, time-limited study analyzing activity on the social network YouTube was proposed. Methods: The information from the videos was extracted through an API search tool, using the NodeXL software. The criteria to select the videos on YouTube were the keywords “real food”, “realfood”, and “cancer” and the hashtags #realfood and #cancer were present, videos in English and videos available on 1 December 2022. Results: The DISCERN value in the total number of videos viewed was 2.25 (±0.88) points, indicating low reliability. The videos uploaded by HRU represented only $20.8\%$. Videos suggesting that the use of foods defined as “real food” could cure cancer without the intervention of any other treatment accounted for $12.5\%$. Videos that provided external links to scientific/technical evidence verifying the information represented only $13.89\%$ of the total number of videos. Of these videos, $70\%$ corresponded to HRU. The DISCERN value for videos from HRU users was 3.05 (0.88), a value that reflects a good reliability of videos from these users. Conclusions: This study provides information on the content and quality of the videos that we can find on YouTube. We found videos of non-health users who do not base their content on any scientific evidence, with the danger that this entails for the population, but it also highlights that the videos published by HRU have greater reliability and quality, being better perceived by the population, so it is important to encourage healthcare professionals and health institutions to share verified information on YouTube. ## 1. Introduction Cancer is currently one of the primary causes of death worldwide [1], with lung cancer being the fourth leading cause of death. When differentiating between countries according to their income, we find that it is in middle- and high-income countries where several types of cancer (lung, colorectal, and stomach) appear among the ten most probable causes of death [2]. About the evolution and development of cancer as a probable cause of death, if in 2020 a total of 19 million people were expected to be diagnosed with cancer [3], the Global Cancer Observatory of the World Health Organization estimates that it could cause a total of 28.9 million deaths worldwide in 2040 [4]. This data highlights the importance of this disease and the need for adequate information for patients, family members, and healthcare professionals [5,6]. The need to obtain information about a cancer diagnosis is so necessary for patients to know something such as treatments, side effects, or even how to live with cancer [7] through cancer survivor testimonies [8]. But it is necessary not to forget the mental well-being associated with the access to information; this access reduces anxiety, depression, and even frustration, and is something essential to improve the emotional and mental well-being of patients who have received a diagnosis of cancer, as well as their families [8,9]. Although, in many cases, the main source of information is healthcare professionals [10,11], it is true that doubts and questions sometimes cannot wait, at which point patients or family members seek information in the most accessible way possible, which is using the Internet [12]. It is important to note that on the Internet it is increasingly common to address social media for information [11,12], and social networks have emerged as tools that allow health-related content to be shared quickly and directly [13,14,15]. In particular, those of an audiovisual nature such as Instagram, TikTok, or YouTube are becoming increasingly important, as written health information is sometimes phrased in a way that is not easily understandable for people without adequate health literacy [16,17]. Among these, the social network with the largest number of users is YouTube [18], with an estimated 2.1 billion active users [19], generating 5 billion visits and 1 billion hours viewed every day [20,21]. Although social media represents an advantage when it comes to obtaining information about a given pathology due to the existing high quantity of information about health on multiples platforms such as blogs, webs [22], messaging apps (Telegram or WhatsApp) [23], there is a lack of control over the veracity and reliability of the health content shared by the users [22], thus representing a potential source of health misinformation [6,24,25,26] that can affect adherence to medical treatments and patients’ health [27]. Concerns about diet and food intake have increased in recent years to the current situation where $60\%$ of the population admit to being worried about long-term risks of the food they eat [28]. This has led to a considerable increase in food-related searches on the various platforms available on the Internet [29,30,31]. One example is the concept of “real food”, a movement that promotes the consumption of fresh or minimally processed foods, or foods whose industrial or artisanal processing has not altered the quality of the natural properties of the ingredients [32]. One of the most prominent searches is related to the so-called superfoods. This type of food is erroneously associated with the cure of some diseases such as cancer [33]. In addition, information sources, which may include people who are not knowledgeable about nutrition, can be unreliable and contribute to the proliferation and dissemination of misinformation on nutrition-related topics [30,34]. In patients diagnosed with cancer, the role of nutrition and controlled diet is of great importance [35,36], and should be taken into account from the moment of diagnosis, representing an important part of the therapeutic process, and should therefore be applied in parallel with antineoplastic treatments [37,38]. Not forgetting those people who have survived cancer and need to maintain healthy dietary habits [39,40]. As patients’ need for nutritional information is so important [30], they even get to consume content on social media created by non-experts in nutrition [34]. These sources may even suggest the consumption of nutritional supplements as if they were foods and even proper treatments [28], always promoted as “anti-cancer,” “cancer-fighting,” or “cancer-busting” [30]. Although the intake of ultra-processed foods can be detrimental to health, and there is clear evidence of this [33,36,40], diet choices, nowadays, cannot be considered as an anti-cancer treatment. This type of misinformation can be particularly dangerous for patients diagnosed with cancer because it may be deemed as a real alternative [41,42], sometimes even leading to the abandonment of the prescribed medical treatment. To the best of our knowledge, there are no previous studies that focus on the role of the real food movement and its association with cancer patients on YouTube. In this context, the aim of this study is to analyze the quality and validity of the existing videos on YouTube that relate the consumption of “real food” and cancer. Secondary objectives are: (i) to determine the role of health-related users in the generation of content, (ii) to analyze whether there is a relationship between the validity and quality of the information found in the videos and the presence of scientific evidence in them, (iii) to assess the possible appearance of sources of misinformation for patients, and (iv) to determine the types of videos that exist in the analyzed network and their relationship with the perceived validity and quality. ## 2.1. Study Design and Ethics The research design is comprised of an observational, retrospective, cross-sectional, time-limited study analyzing activity on the social network YouTube. This study was considered exempt from ethical review because it was performed upon a social network, and it did not involve any patient or human data beyond measuring the Internet activity among YouTube users. In addition, this study only used data from users who consented to YouTube making their data publicly available (i.e., no privacy settings were selected by them). However, accounts of individual users have been anonymized to develop good research practices on social media [43]. ## 2.2. Data Collection The data extraction system used in the present study has been through an API (Application Programming Interface) search tool, using the professional version of the software NodeXL (Social Media Research Foundation). To achieve the objectives proposed in this study, the criteria to select the videos in YouTube were: (i) the keywords “real food”, “realfood” and “cancer” and the hashtags #realfood and #cancer were selected. ( ii) videos in English. ( iii) videos available on 1 December 2022. The exclusion criteria were: (i) non-English videos, (ii) advertisements, (iii) videos not related to real food and cancer in humans. ## 2.3. Data Analysis The analysis of the data obtained was performed in several steps. The first step was collecting a total of 3817 videos. Second, both the titles and descriptions of the videos obtained were analyzed to assess whether they addressed the subject matter of the proposed study. Subsequently, the ViewRatio [27] of the resulting videos was calculated and the first 100 clips were selected [40]. Finally, a detailed analysis was carried out by viewing the videos, and it turned out that 28 of them did not deal with anything related to the subject matter required in the study (Figure 1). This analysis was conducted by two researchers (S.S.-F. and M.d. C.L.-E.) and then corroborated by a third one (P.J.J.H.). The videos were reviewed by a group of experts including physicians, nurse, and a pharmaceutic-nutritionist, so that any differences in approach and focus were always discussed and resolved with full agreement. The following data was retrieved: upload date, number of views, number of likes. Moreover, two indexes were calculated to compare the videos with each other; (i) the View Ratio (number of views/days from the upload to the moment of the data collection), (ii) the Viewers interaction (number of likes + comments/number of views) [27]. Likewise, an analysis was carried out to determine whether the videos provided scientific evidence or not. To meet this objective, the videos were described to include references to scientific articles or reliable technical documents that support or confirm what was explained in the video. The videos were scored and sorted, using the modified DISCERN instrument, which allows the classification of the quality of health information related to treatments provided in videos [44,45]. It is an instrument consisting of five items rated on a Likert-type scale from 1 (poor quality) to 5 (high quality). Where, video scores > 3 points indicate “good reliability/quality”, a score of 3 points indicate “moderate”, and scores < 3 points indicate “poor reliability” and should not be used by patients [45]. The Global Quality Scale (GQS) score was used to assess the overall quality of the video. It is a five-point scale based on the quality and ease of use of online information [27,44]. The videos were categorized depending on their content and classified according to their health information quality, looking for the presence of scientific evidence in their messages, links to reliable health organizations, and identification of the authors as healthcare professionals or reliable organizations. Finally, an analysis of users’ account descriptions was performed. Regarding users’ uploaded videos, the description was analyzed looking for an identification as (i) Health-related users (HRU) (government/university channels/healthcare professionals) and (ii) Non-health-related users (NHRU) (communication media, news Internet channels, individual users). The videos were also classified according to: (i) the advocacy of real food as a treatment for cancer and the abandonment of medical treatment, (ii) whether the video had a focus on testimonials claiming a cancer cure linked to real food or rather, it had an informational approach; (iii) whether the video description had links to external sources that allowed verification of what was stated in the video. ## 2.4. Statistical Analysis Descriptive and inferential statistics for analysis were performed via the Statistical Package for the Social Sciences software (SPSS) version 23.0 (IBM, Armonk, NY, USA). Descriptive statistics are presented, medians were used for quantitative variables and proportions were used for qualitative variables. Spearman’s nonparametric correlation coefficient (Spearman’s Rho) was used for correlational analysis. Mann Whitney’s U was used to compare the numerical variables. Multivariate linear regression was used to characterize relationships between video characteristics, upload source, content category, reliability (DISCERN), and educational quality (GQS). The statistical significance level was set at $p \leq 0.05.$ ## 3.1. Description of the Sample Of the 72 videos selected after review by the researchers, the total number of views was found to be 44,682,055. Each video was viewed a total of 620,584.09 (CI$95\%$: 177,449.33–1,068,942.71) times (Table 1). Regarding the remaining totals, it was found that 32,956 comments were obtained, with 708,351 likes and only 191 dislikes. The DISCERN value in the total number of videos viewed was 2.25 (±0.88) points, indicating low reliability. A similar situation was observed when analyzing the GQS, with an average value of 2.208 (±1.11). Values below 3 indicate a low quality of the information, as well as difficulty in contrasting the statements found in the videos. When analyzing the videos in terms of scientific evidence, it is found that the average DISCERN for videos with scientific evidence is 3.61 (0.67) and video without scientific evidence was 2.09 (0.693). The GQS shows a value of 3.11 (1.17) to videos with scientific evidence and 2.22(1.02) to videos without scientific evidence, finding that the validity and quality are perceived as better in videos with scientific evidence. Regarding the geographical location of the channels where the analyzed videos were posted, it was found that 56 ($77.78\%$) were in the USA, UK (6; $8.34\%$), India (4; $5.55\%$), Canada (4; $5.55\%$), and other countries (2; $2.78\%$). Regarding the categorization, it was found that the videos uploaded by HRU represented only $20.8\%$ ($$n = 15$$) of the total number of videos analyzed. Of these, 12 ($16.66\%$) correspond to users defined as physicians, 2 ($2.77\%$) belong to hospitals, and 1 ($1.38\%$) to a research center. Of the 57 videos categorized as non-health related, 38 ($52.8\%$) were found to correspond to Blog Channels, 13 ($18\%$) were broadcast by individual user accounts (youtubers), and 6 ($8.3\%$) corresponded to television channels. It was also observed that the videos that offered only testimonials represented a total of $31.9\%$ ($$n = 23$$). Of these, only 5 videos ($21.74\%$) were from HRU. On the other hand, videos suggesting that the use of foods defined as “real food” could cure cancer without the intervention of any other treatment accounted for $12.5\%$ ($$n = 9$$) of the total, while the remaining videos (63; $87.5\%$) stressed the need to treat cancer with conventional medical treatments. All videos that presented real food as the one valid treatment came from blog channels ($$n = 7$$; $77.7\%$) and youtubers ($$n = 2$$; $22.22\%$). Likewise, videos that provided external links to scientific/technical evidence verifying the information represented only $13.89\%$ ($$n = 10$$) of the total number of videos (Table 2). Of these videos, 7 corresponded to HRU ($70\%$) versus 2 that came from Blog Channels and 1 from a TV channel. The DISCERN value for videos from HRU users was 3.05 (0.88), a value that reflects a good reliability of videos from these users. However, the GQS score is 2.8 (1.02), indicating moderate quality. Finally, we analyzed the different kinds of cancer treated in the videos selected. It could be observed that 19 videos ($26.39\%$) were focused on a specific kind of cancer meanwhile 53 ($73.61\%$) treated cancer in a generic way. From these 19 videos, the cancers mentioned were: (i) breast cancer with 9 videos ($47.37\%$), (ii) prostate cancer with 4 videos ($21.05\%$), (iii) colon cancer with 2 videos ($10.53\%$), (iv) ovarian cancer with 2 videos ($10.53\%$), (v) pancreatic cancer 1 video ($5.26\%$), and (vi) melanoma with 1 video ($5.26\%$) ## 3.2. Analysis According to the Type of Videos and Users When analyzing the results with respect to the type of video found, no significant statistical difference was found between the testimonial-type videos and those with an informational approach, for any of the categories analyzed (Table 3). However, among those videos stating that foods classified as “real food” can be used as a legitimate treatment, it was observed that there was a statistically significant difference in favor of videos underlining that the conventional medical treatment should be continued, and never abandoned ($U = 165$; $$p \leq 0.028$$), with no difference found in terms of views, video duration, time since upload, likes, dislikes, comments, views, views ratio, or GQS. The analysis of the typology of users shows that HRU presented a significant difference in likes versus those NHRU ($U = 260$; $$p \leq 0.02$$), as well as in the View Ratio ($U = 233$; $$p \leq 0.007$$). Finally, a significant difference was also observed in reliability (DISCERN), between videos of HRU versus those who were not ($U = 282$; $$p \leq 0.028$$). No statistically significant differences were observed in the rest of the variables analyzed between the two groups. Between the videos with external links to scientific evidence and those without significant differences were found in favor of videos with links both in terms of reliability (DISCERN) ($U = 86.5$; $$p \leq 0.0001$$) and quality of the information provided (GQS) ($U = 159$; $$p \leq 0.011$$). For the remaining variables, no significant differences were found between the two groups. Likewise, we compared the reliability (DISCERN) and quality of information (GQS) of videos that treat specific cancer types. It was observed that when comparing the validity of the videos, those that dealt with cancers in a specific way offered a higher level of confidence ($U = 366$; $$p \leq 0.017$$). However, the quality of the videos showed no difference between the two types of videos ($U = 437$; $$p \leq 0.114$$). It should be noted that, when assessing the different types of cancers and whether the videos had scientific evidence, it was found that: (i) breast cancer only had three videos out of nine that offer evidence ($33.33\%$). ( ii) In prostate cancer, three out of four videos offer evidence ($75\%$). ( iii) In both colon and ovarian cancer, there are two videos of which only one offers scientific evidence ($50\%$). ( iv) Finally, it was observed that the video on melanoma does not offer scientific evidence, while the video on pancreatic cancer does offer solid scientific evidence. In the remaining videos, where cancer is addressed in a general way, there is only one video that offers scientific evidence ($1.89\%$) ## 3.3. Correlation Analysis between Popularity Indexes, DISCERN and GQS The study of the possible correlations between the variables associated with video popularity in terms of reliability (DISCERN) and quality (GQS) (Table 4) shows that there is a positive correlation of medium intensity between video reliability and likes ($r = 0.245$; $$p \leq 0.038$$), comments ($r = 0.266$; $$p \leq 0.024$$), view ratio ($r = 0.353$; $$p \leq 0.002$$). However, when assessing the quality of the video (GQS), it was observed that there was no statistically significant correlation between the GQS score and the different popularity variables analyzed (Table 4). ## 4. Discussion This study analyzes the reliability of YouTube videos related to the real food movement and its impact on the development of cancer, either at the level of prevention -accompanying medical treatment- or even as a proposal to use only food as a treatment for cancer. YouTube is the world’s most widely used social network based on video sharing. Anyone can share information for free after registering. This has made the platform an important source of health information, especially during the pandemic, when the consumption of videos related to health problems and treatments increased exponentially [46,47]. However, it is important to note that this large amount of health information represents a non-negligible potential danger, as this information is not verified after publication [46]. To answer the first objective of the study, we assessed the role of users in generating content on YouTube channels. It was found that this information is mainly provided by accounts or users who do not identify themselves as healthcare professionals or health institutions. This trend can be observed in other studies analyzing YouTube as a source of information for pathologies such as diabetes [27], cancer [3,5,9,18], dermatological conditions such as psoriasis [25], and even vaccination against SARS-CoV-2 during the COVID-19 pandemic [47]. However, this situation is not exclusive to YouTube, as it has also been found in other social networks based on written communication, such as Twitter, also in pathologies such as cancer [20,48] and even in primary prevention strategies such as vaccination against the COVID-19 pandemic [48]. Previous studies analyzing the content posted on YouTube for other pathologies, such as pancreatic cancer or psoriasis, have shown that non-healthcare accounts have the highest number of likes [18,25]. An example of this is the study by Cakmak & Mantoglu in 2021 [18], where in an analysis of YouTube videos related to pancreatic cancer, it is the patients who generate the most likes compared to either health-related accounts, or those created by users defined as healthcare professionals. Similarly, Li et al., 2019 [46] described that users who watch health content on YouTube seem to like videos with low scientific quality information more than those with high quality. This situation is also observed in other studies such as the one developed by Barlas et al., 2022 [27], on myocarditis and vaccines against COVID-19 where videos with a low level of scientific evidence were also the most viewed. This previous scenario contrasts with what was observed in our study, where the number of likes and the view ratio show a significant difference in favor of the videos provided by health-related accounts compared to the other accounts. This may be since the information provided by health-related accounts was easily recognized as relevant by the viewers or that it was sufficiently attractive, in contrast to the studies [25]. When analyzing our second objective, which was to explore the possible relationship between the validity and quality of the information found in the videos and the provision of scientific evidence, we observed that the information conveyed in the analyzed videos had a very low reliability. In addition, it is very difficult to corroborate the information provided. These results confirm the conclusions of several studies on the low quality of the information communicated through YouTube in different pathologies [49,50]. In addition, a result of great interest found in this study is that the videos that focused on treating cancers specifically were those that offered a higher level of scientific evidence and were also those that were perceived as having more validity for users. Most of the content shared in the videos came from users who did not identify themselves as healthcare professionals, and only a small portion was shared by these professionals. As seen in previous studies, the information they provide is more reliable, and they are also the ones who provide most external links to scientific evidence [18,44]. In this case, the percentage of videos uploaded by health-related accounts is only $20.8\%$, while the rest of the videos are shared by other accounts unrelated to this field. This data is particularly noteworthy when compared to previous studies analyzing the relationship between YouTube and other pathologies, in which a large part of the videos was shared by accounts related to the health world [18,25]. We can conclude that there is much more intrusiveness when it comes to sharing content on YouTube related to food and cancer. Regarding the third objective of this study, to analyze the possible emergence of sources of misinformation for patients, it is noteworthy that there is a significant difference in favor of videos that recommend never abandoning conventional treatment. This kind of videos are shared by accounts related to the healthcare world, which in turn are the ones that provide the most external links to scientific evidence. Since these recommendations are based on verified information [37,38], we can establish an association between the quality of the information in the videos and the presence of scientific evidence. We also find videos that directly misinform about cancer treatment, such as those claiming that real food can cure this disease [28,29]. On the other hand, health-related users highlight the importance of nutrition and diet in the development of cancer [35,36] in parallel with antineoplastic treatments [37,38]. Some of them emphasize the importance of avoiding the consumption of ultra-processed foods which can be harmful to health [33,36,40]. These users who create sound content linking real food and cancer attempt to refute the unverified information that suggests the consumption of certain foods as the only treatment for cancer [28,29] and even referring to them as “anti-cancer” or “cancer-busting” foods [29]. Regarding the fourth objective, which seeks to know the existing types of videos, we do indeed find these videos recommending the intake of the so-called real food as the only treatment for cancer [28,29], disregarding any other type of treatment. Their presence confirms what other studies have previously concluded, about the use of social media to promote treatments that have already been shown to be ineffective [51,52]. However, this study yields a significant difference in favor of videos stating that medical treatment should never be abandoned. On another note, the study has several limitations mainly related to the design, since one of the characteristics of Internet analysis in general, and YouTube in particular, is that the content is constantly changing, which means that a cross-sectional design cannot be applied. Moreover, conducting the study in one single social network, YouTube, is a limitation that must be considered, since it is possible that the topics of real food and cancer can be addressed in other audiovisual social networks, such as TikTok or Instagram. Finally, since specific keywords and hashtags were used to retrieve the information, it is possible that some videos that do not use this combination of keywords in their description and/or title may have been lost. An interesting line of research that can be explored following the results of this study is to investigate why in some YouTube-based studies the information provided by health-related accounts becomes relevant in both views and likes, while in other cases it is the unverified information that stands out [25,46]. On the other hand, this study presents great strengths, being one of the few to conduct such a comprehensive analysis of real food and cancer on YouTube. The chosen social network is the most popular in the world in terms of video posts. Its content was analyzed, but also the repercussions it generated, which are very relevant findings when it comes to understanding the way information travels across the Internet. ## 5. Conclusions Social network is one of the most relevant tools for transmitting information today. The ease of access and the usefulness of conveying the message in the form of a video or audio make YouTube one of the main sources of data transmission in the world. This also means that there is a large amount of unverified information being shared by people who do not identify themselves as healthcare professionals and do not base their statements on any scientific source. This situation can affect people’s health and become a public health problem. In this study, a search was conducted for information related to “real food” and its association with cancer. Due to the ease of sharing content on social media, it was expected that there would be videos with unverified or incorrect information. However, as we conducted the study, we found that the conclusions go far beyond that, to the point of some users sharing information that can be harmful to patients. Videos have been found going so far as to recommend stopping antineoplastic treatment just to focus solely on a superfood approach. One of the characteristics of social networks is that, to date, there are no health-related content filters implemented by the platforms themselves, so any user can share a video, including those that have no scientific basis. These videos offer recommendations or spread information, that expose contradictory messages, compared with the information that patients and their families receive from physicians, nurses, and other healthcare professionals. Even when this type of videos is detected, when it is done, since it is very complicated to find them due to the huge amount of existing videos, it is a very complex process by which the video could be removed from the social network to prevent it from continuing to be displayed. As this study shows, it is a public health problem that is being ignored by the companies allowing this kind of content, and by the public administrations that do not act. It is important that measures are set in motion to prevent the dissemination of unverified material, or at least establish some kind of verification by the scientific community labeling which videos are based on evidence, so that when a person turns to this platform, they quickly know if what they are watching is verified content or, on the contrary, if they are voluntarily consuming content that has no quality control. This is the key, and the danger of this situation is that viewers do not know what they are watching. With a simple “check” on the videos, we could make the population aware of the information they are about to consume. If they still choose to consume unverified videos, they do so on their own accord. But right now, people do not know if what they are about to consume is or is not verified content. And in that case, whose responsibility, is it? Is it only the responsibility of the individual who does not know the scientific method, or is it also the responsibility of the platform that allows the content, and even of the institutions that overlook the situation? But not everything we found in this study is negative. Videos shared by health-related accounts with verified data do have a greater impact than videos uploaded by non-health-related accounts. This is an essential element in the fight against misinformation on social networks. The upside is that in the case of real food and cancer, verified information is more relevant, despite having a lower number of shared videos than those without scientifically valid facts. Faced with this situation, where platforms and institutions do not act, it seems to be of utmost importance that healthcare professionals understand the need to be more present in social media from a professional point of view. 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--- title: A Study on the Interactions of Proteinase K with Myricetin and Myricitrin by Multi-Spectroscopy and Molecular Modeling authors: - Kefan Liu - Yubo Zhang - Wei Zhang - Liyan Liu - Zhan Yu journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048853 doi: 10.3390/ijms24065317 license: CC BY 4.0 --- # A Study on the Interactions of Proteinase K with Myricetin and Myricitrin by Multi-Spectroscopy and Molecular Modeling ## Abstract Myricetin (MYR) and myricitrin (MYT) are well recognized for their nutraceutical value, such as antioxidant, hypoglycemic, and hypotensive effects. In this work, fluorescence spectroscopy and molecular modeling were adopted to investigate the conformational and stability changes of proteinase K (PK) in the presence of MYR and MYT. The experimental results showed that both MYR and MYT could quench fluorescence emission via a static quenching mechanism. Further investigation demonstrated that both hydrogen bonding and van der Waals forces play significant roles in the binding of complexes, which is consistent with the conclusions of molecular modeling. Synchronous fluorescence spectroscopy, Förster resonance energy transfer, and site-tagged competition experiments were performed to prove that the binding of MYR or MYT to PK could alter its micro-environment and conformation. Molecular docking results revealed that either MYR or MYT spontaneously interacted with PK at a single binding site via hydrogen bonding and hydrophobic interactions, which is consistent with the results of spectroscopic measurements. A 30 ns molecular dynamics simulation was conducted for both PK-MYR and PK-MYT complexes. The calculation results showed that no large structural distortions or interaction changes occurred during the entire simulation time span. The average RMSD changes of PK in PK-MYR and PK-MYT were 2.06 and 2.15 Å, respectively, indicating excellent stability of both complexes. The molecular simulation results suggested that both MYR and MYT could interact with PK spontaneously, which is in agreement with spectroscopic results. This agreement between experimental and theoretical results indicates that the method herein could be feasible and worthwhile for protein–ligand complex studies. ## 1. Introduction Myricetin (MYR, Figure 1) is a flavonoid compound found in the bark and leaves of Chinese bayberry (Morella rubra) family of plants [1], which has various pharmacological activities, such as acting as an antioxidant, protecting against cardiovascular disease, and having antitumor, antibacterial, hypoglycemic, lipid-regulating, and liver-protecting effects [2]. Bertin et al. [ 3] discovered that MYR has a strong antioxidant capacity and was able to effectively slow down the oxidation of free radicals in human low-density lipoprotein and vascular endothelial cells. Kim et al. [ 4] also found that MYR induced apoptosis in human colon cancer cells and may be useful in developing therapeutic agents for colon cancer. Myricitrin (MYT), a flavonol compound and a glycoside of MYR, is found in large quantities in fruits, bark, leaves, and other natural plants of the waxberry family [5]. It plays a key role in reducing blood sugar levels, blood pressure, and cytotoxicity. Zhang et al. [ 6] found that MYR significantly reduces blood glucose levels in alloxan-induced diabetic mice, and Yan et al. [ 7] demonstrated that MYT can inhibit IL-1β-induced extracellular matrix degradation in mouse chondrocytes, potentially making it useful in the treatment of osteoarthritis. Jo et al. showed that both MYR and MYT have prominent anti-African swine fever virus protease activity by forming protein–flavonoid complexes [8]. Vojta and coworkers demonstrated both MYR and MYT have intramolecular hydrogen bonds, which are important to their biological activities [9]. However, both MYR and MYT have low oral bioavailability due to their poor aqueous solubility, which limits their pharmaceutical use [10]. Proteinase K (PK) is a serine protease from the fungus Tritirachium album limber that belongs to the subtilisin family of enzymes [11]. It is a single-chain protein consisting of 279 amino acid residues and has a molecular weight of 28,930 Da with two disulfide bonds. PK is often used for spectroscopic assays due to its 2 tryptophan residues (Trp8 and Trp212) and 17 tyrosine residues, which are suitable for spectroscopic analysis [12]. It is commonly used to digest proteins, remove contaminants, and inactivate DNases and RNases in protein-free DNA or RNA samples. Despite the widespread use of PK in various industries and scientific research, few studies have been conducted on the binding ability of flavonoids to PK. In order to better understand the potential molecular recognition between PK and typical flavonoid compounds, we used a combination of experimental and theoretical techniques including UV–visible absorption spectroscopy, fluorescence spectroscopy (FL), molecular docking, and molecular dynamic simulation methods to study the interactions between MYR/MYT and PK. The investigation of PK-MYR and PK-MYT binding mechanisms could provide valuable insights for the application of MYR and MYT to the food and pharmaceutical industries. Additionally, we hope that this study would provide valuable information to understand the impact of flavonoids on the structure and stability of PK. ## 2.1. Fluorescence Quenching Mechanism Fluorescence spectroscopy is an effective method used to investigate conformational changes of proteins and binding parameters of ligands to fluorophore residues of proteins such as Trp, Tyr or Phe [13,14]. PK contains two Trp residues (Trp8 and Trp212), which make it possible to emit fluorescence. The fluorescence emission intensity of PK decreases gradually when MYR or MYT interacts with PK. The process by which this phenomenon occurs is called fluorescence quenching, which is the result of the altered microenvironment of PK. To eliminate the interference of fluorescence quenching from the inner filter effect, the experiments were corrected for fluorescence intensity using Equation [1]. [ 1]Fcorr=Fobs×e(Aex+Aem)2 where Fcorr and Fobs are the fluorescence emission intensity values of the corrected and pre-corrected systems, respectively. Aex and Aem are the UV–Vis absorption intensities of quencher and PK at excitation and emission wavelengths, respectively. Figure 2 clearly shows that both MYR and MYT, in conjunction with PK, have a fluorescence emission peak at 330 nm under an excitation wavelength of 278 nm. The fluorescence intensity of PK progressively decreased with the increase in temperature from 290 to 310 K. The higher the temperature, the lower the fluorescence intensity of PK. Meanwhile, a significant decrease in the fluorescence intensity of PK was investigated with increasing concentrations of MYR and MYT at a fixed concentration of PK, suggesting that PK strongly interacted with MYR and MYT, and the quencher resulted in a reduction of the hydrophobic microenvironment around the tryptophan residue of PK, producing PK–myricetin and PK–myricitrin complexes. To investigate the differences in the fluorescence quenching patterns of the PK-MYR and PK-MYT complexes, the Stern–Volmer equation (Equation [2]) was used for analysis [15,16]. [ 2]F0F=1+Kqτ0[Q]=1+Ksv[Q] where F and F0 are the fluorescence emission intensities of PK in the presence or absence of the quencher (MYR or MYT), respectively, [Q] is the concentration of the quencher, Ksv is the ratio of the bimolecular quenching model constant to the single-molecule decay rate constant, namely, the Stern–Volmer equation quenching constants, Kq is the fluorescence quenching rate constant, and τ0 is the average lifetime of the fluorescent molecule when no quencher is added, while the average lifetime of biological macromolecules is 1 × 10−8 s [17]. Fluorescence quenching could be categorized into two processes, namely dynamic quenching and static quenching. Dynamic quenching occurs when the quencher collides with biological macromolecules, while static quenching is caused by the formation of complexes. For the dynamic process, the increase in Ksv along with the increase in temperature is expected, and the opposite is seen for the static quenching process [18]. By examining the interactions between MYR/MYT and PK at temperatures of 290, 300, and 310 K, an apparent linear relationship between F0/F and [Q] was achieved, as shown in Figure 3A, indicating that the Stern–Volmer equation quenching constants (Ksv) show a negative correlation with temperature (T). Consequently, we know that the combination of PK and MYR/MYT could form PK-MYR and PK-MYT complexes, leading to the static quenching mechanism. Normally, the maximum collisional quenching constant Kq is about 2 × 1010 L·mol−1·s−1 for small molecule quenchers and biomolecules [19]. If a measured Kq is much larger than 2 × 1010 L·mol−1·s−1, it indicates that the quenching induced by the quencher is static quenching. Table 1 gives the Kq values for MYR and MYT at different temperatures, indicating that the quenching processes are both static. Meanwhile, the static quenching constants of PK-MYR exhibit a more remarkable change with increasing temperature, suggesting that the PK-MYR complex is less stable and more sensitive to higher temperature compared to the PK-MYT complex. The binding constants and the number of binding sites can be calculated by using a double logarithmic equation [20,21] (Equation [3]). [ 3]log[(F0−F)F]=logKA+nAlog[Q] where KA represents the binding constant between the biomolecule and the quencher, and nA implies the number of binding sites between the quencher and the fluorophor. The binding constants of PK in Figure 4A,B decrease with increasing temperature, suggesting that the stability of PK-MYR and PK-MYT complexes decreases at high temperatures and increasing temperature is not favorable for complex stability. Applying Equation [3] to the data in Figure 4, the KA and nA values could be calculated and are listed in Table 1. It could be proposed that the binding constant of PK-MYT is larger than that of PK-MYR at the temperature of 290 K, and these two complexes share a similar binding site number n around one. Based on the binding constants KA at different temperatures, namely 290, 300, and 310 K, respectively, listed in Table 1, the interaction forces between PK and MYR/MYT by the Van’t Hoff equation [22,23], Equations [4] and [5], could be elucidated. [ 4]lnKA=−ΔHRΤ+ΔSR [5]ΔG=−RTInKA=ΔH−TΔS where R is the molar gas constant with a general value of 8.314 J·mol−1·K−1. ∆H and ∆S are the enthalpy and entropy changes during the fluorescence quenching, respectively. The ∆G in Equation [5] is the Gibbs free energy change in the reaction. In general, the interaction between quenchers and fluorophors can be explained by four forces, including hydrogen bonding, van der Waals forces, electrostatic forces, and hydrophobic interactions [24,25,26]. The sign and magnitude of ∆H and ∆S can be used to distinguish the binding forces and properties of the complexes. When ∆H and ∆S are positive, hydrophobic interactions dominate; when both ∆H and ∆S are negative, they can be explained as hydrogen bonding and van der Waals forces; when ∆H and ∆S are very low negative or positive values, there exist electrostatic interactions. In Table 2, the negative ∆G value illustrates that the processes of PK-MYR and PK-MYT formation are spontaneous. Correspondingly, the negative values of ∆H and ∆S for both processes indicate that the formation of both complexes is exothermic, where hydrogen bonding and van der Waals forces may dominate the host–guest interactions, and where PK could be considered as the host and the quencher, MYR or MYT, which would be the guest herein. In Table 2, the value of ∆H for PK-MYR is slightly lower than that of PK-MYT, showing that PK-MYT might have higher thermodynamic stability. ## 2.2. Förster Resonance Energy Transfer The distance and energy transfer between two chromophores, commonly known as the donor and the acceptor, may be computed by the Förster resonance energy transfer (FRET) theory [27]. The UV–Vis absorption spectra of MYR and MYT were chosen to overlap with the fluorescence emission spectra of PK at 290 K [28]. The energy transfer efficiency and binding distance were estimated according to Equation [6]. [ 6]$E = 1$−FF0=RC6RC6+r6 where E is the energy transfer efficiency between the donor and the acceptor, r is the binding distance between the donor and the acceptor, and RC is the critical distance when the energy transfer efficiency is $50\%$, the value of which can be calculated using Equation [7] [29]. [ 7]RC6=8.8×10−25(K2⋅Φ⋅n−4⋅J) where K2 is the spatial orientation factor associated with the geometry of the dipole donor and acceptor and generally takes the value of $\frac{2}{3.}$ Φ is the fluorescence quantum yield of the donor. The symbol n is the average refractive index of the medium and usually takes the value of 1.336 as the average value of water and organic matter [30]. J is the spectral overlap integral between the fluorescence emission spectrum of the donor and the absorption spectrum of the acceptor, and the value is calculated [29] by Equation [8]. [ 8]J=∑(FD(λ)⋅ε(λ)⋅λ4⋅Δλ)∑(FD(λ)⋅Δλ) where FD(λ) is the corrected fluorescence intensity of the donor in the wavelength range, ε(λ) is the molar absorption constant of the acceptor at the wavelength, and ∆λ is the span of the wavelength. Following the FRET theory, the main factors affecting the energy transfer efficiency include the fluorescence production of the donor, the overlap of the fluorescence emission spectrum of the donor with the UV–Vis absorption spectrum of the acceptor, and the binding distance between the donor and the acceptor that must be within a specified range of 1 to 10 nm [31]. Referring to Figure 5 and Table 3, the binding distances of MYR and MYT are in the range of 0.5 RC< r < 1.5 RC, which confirms that the binding processes are static quenching processes, which is consistent with the results of fluorescence spectroscopy. The binding distances of MYR and MYT to PK were 5.11 and 3.89 nm, respectively, illustrating the energy transfer from PK to MYR and MYT. Coherently, the binding distance of MYR and PK is larger than that of MYT and PK, indicating that the complex of PK-MYR is not as stable as PK-MYT, which is consistent with the conclusion from the Van’t Hoff equation. ## 2.3. Synchronous Fluorescence Spectroscopy Synchronous fluorescence spectroscopy studies were performed to observe changes in the molecular environment of Tyr residues and Trp residues in the host structure, which are mainly responsible for fluorescence emission [24,32,33]. Therefore, we used the characteristic information of Tyr and Trp residues, ∆λ = 15 nm and ∆λ = 60 nm, respectively, to probe the changes in the PK-MYR and PK-MYT complexes. In Figure 6A, the maximum emission wavelength of PK was retained at 279.0 nm, although the concentration of MYR was increased from 0.0 to 25.0 μM. For the case of the PK-MYT complex shown in Figure 6C, the maximum emission wavelength of PK exhibited a slight red shift from 279.0 to 280.0 nm. Similarly, in Figure 6B,D, the maximum emission wavelength of PK exhibited no shift and a red shift (from 274.0 to 276.0, respectively). Normally, the shift of maximum emission wavelength correlates with changes of the hydrophobicity around the chromophores [34]. The red shift indicated that the chromophores moved to a more polar environment, while the blue shift indicated a more hydrophobic environment around the chromophores. These results suggested that the binding phenomenon of MYT to PK induced conformational changes in PK. Meanwhile, the hydrophobic environment of both Tyr and Trp residues gradually decreased according to the addition of MYT. ## 2.4. Competitive Binding Experiments To determine the binding site of PK, two typical ligands including ibuprofen and cytisine were chosen to perform competitive binding experiments [33,35]. From Figure 6, it can be seen that the fluorescence intensity of PK–ibuprofen and PK–cytisine complexes decreases along with the increasing concentration of MYR and MYT. The site-labeling experiments were still performed using Equations [1] and [2] for correction and data analysis, respectively. As shown in Table 4, Ksv of PK–ibuprofen and PK–cytisine complexes were only $69.51\%$ and $70.73\%$ of that of free PK as 2.46 × 104 L·mol−1, indicating that there exist site competition for both ibuprofen and cytisine in competition with MYR, and suggesting that MYR preferentially binds to the hydrophobic cavity of PK. Similarly, it could be remarked that for MYT, the Ksv of PK–ibuprofen and PK–cytisine complexes were only $61.52\%$ and $38.14\%$ of that of free PK as 2.06 × 104 L·mol−1. These results revealed that both cytisine and ibuprofen compete for the same binding site of PK. It has been well known that neutral compounds such as ibuprofen and cytisine are likely to bind with proteins through hydrophobic interactions and hydrogen binding [36,37]. The competition of MYT was greater than that of MYR, which shows the that binding site for MYT is different to that of MYR. This is consistent with the results obtained from the thermodynamic calculations. ## 2.5. Molecular Docking Study Molecular docking could be regarded as an appropriate way to propose the plausible sites for protein–ligand interactions. By the help of molecular docking, the conformation and orientation (referred as the “pose”) of ligands into the binding site of a macromolecular target could be analyzed. The pose with the lowest binding energy could be looked as the most likely binding mode of the ligand [38]. Thus, to probe the possible binding modes of MYR and MYT with PK and to better understand the interactions between the host and the guests, molecular docking was used to realize how ligands bind to the host. The lowest-binding-energy docking poses are shown in Figure 7, which reveals that MYR fits deeply into PK and is docked into a pocket of PK. Hydrogen binding between MYR and residues of Asn119, Arg121, Val 127, Gly152 and Ala 245 could be responsible for the stabilization of the PK-MYR complex. Hydrophobic interactions might be much weaker and only play an auxiliary role in PK-MYRA complex stability. For the case of MYT, there were four hydrogen bindings between MYT and residues of Gly259, Asn263, Ile264, Gly267, Thr268 and Asn270. There were no hydrophobic interactions found for PK-MYT. It could be seen that MYT has more binding sites and more binding interactions with PK than with MYR. Considering the structural characteristics of PK [39], because MYT has a larger molecular shape and higher hydrophilicity, it might occupy a larger space of docking, have more interactions with polar residues of PK and bind to the host more tightly. This result is consistent with the fluorescence quenching results. ## 2.6. Molecular Dynamics Simulation Study Molecular dynamics (MD) is an invaluable technique used to investigate the structural changes of the host in the presence of the ligand as a way to further investigate the stability and flexibility of the host after binding with the ligand [40]. The lowest binding energy poses of PK-MYR and PK-MYT were subjected to 30 ns all-atom MD simulations at 300 K to evaluate the dynamic stability of the complexes. Root mean square deviation (RMSD) of the host and the ligand in PK-MYR and PK-MYT are presented in Figure 8. It is clear that the RMSD of both the host and the ligand in Figure 8A is in rapid increase when the simulation time is in the first 2 ns, illustrating that the confirmations of the host and the ligand are in the process of changing from dynamic instability to stability. During the time from 2 to 17 ns, the RMSD of PK are stable while MYR still fluctuates. The average RMSD of PK and MYR was found at 2.06 and 0.81 Å, respectively, suggesting that MYR was bound to PK, but the stability of MYR was not stable enough after the complex formation. Similarly, as shown in Figure 8B, the RMSD of PK and MYT reached equilibrium at 2 ns, and the RMSD of PK and MYT did not fluctuate drastically from 2 to 30 ns, with averages of 2.15 and 0.36 Å, respectively. The convergence of the RMSD trends demonstrated that the conformational changes of both the host and ligand were small, indicating that the structural stability of the complex is excellent. This result is consistent with the discussions in FRET. ## 3.1. Materials PK was obtained from Jiuding Chemicals (Shanghai, China). Both MYR and MYT were purchased from Kailai Biological Co., Ltd. (Xi’an, China). Tris(hydroxymethyl)aminomethane (Tris) was purchased from Amresco (Solon, OH, USA). HCl and ethanol were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Ibuprofen and cytisine were sourced from Macklin Biotechnology (Shanghai, China) and Yuanye Biotechnology (Shanghai, China), respectively. The reagents were of analytical grade or higher, stored at 0–4 °C, and used directly without extra pretreatment. Ultrapure water (18.2 MΩ·cm) from an Elga PureLab Classic system was used for the solution preparation and dilution. ## 3.2. Instruments A Cary Eclipse fluorescence spectrometer (Varian, Palo Alto, CA, USA) was used to determine specific changes in the fluorescence emission of PK at different temperatures. All measurements were recorded at an excitation wavelength of 278 nm and the emission wavelength of 290–450 nm [41]. The excitation and emission slits were adjusted to 5 nm. In synchronous fluorescence measurements, ∆λ was set to 60 and 15 nm. Typically, a 25 mM Tris-HCl (pH 8.0) solution containing 3.46 μM PK and 0–25 μM MYR or MYT at 290, 300, or 310 K is used for fluorescence spectroscopy measurements. UV–Vis absorption spectra were recorded by using a UH5300 UV–Vis spectrometer (Hitachi, Japan) at wavelengths ranging from 225 to 450 nm, with a band width of 1 nm and data interval of 0.5 nm [42]. ## 3.3. Site-Tagged Competition Experiments with Ibuprofen and Cytisine For in site labeling competition experiments, an agent such as ibuprofen or cytisine of 3.46 μM was first mixed with PK. Then, different concentrations of MYR and MYT (0–25 μM) were added dropwise to the mixture and incubated at room temperature for 1 h. The excitation wavelengths for PK–ibuprofen and PK–cytisine were 265 and 280 nm, respectively. Fluorescence spectra were analyzed using the Stern–Volmer equation. ## 3.4. Förster Resonance Energy Transfer Studies Förster resonance energy transfer (FRET) was tracked by overlapping PK emission spectrum (donor) with MYR or MYT absorption spectrum (acceptor). A concentration of 3.46 μM PK was chosen for the fluorescence emission spectroscopy measurements. After that, the UV–Vis absorption spectra were collected by the molar ratio of 1:1 type PK and MYR/MYT mixture, and the reference was secondary deionized water. ## 3.5. Molecular Docking Studies Three-dimensional structure of PK was obtained from the RCSB Protein Data Bank [43] with the entry 2ID8 (https://doi.org/10.2210/pdb2ID8/pdb) (accessed on 15 February 2023). Structural data of MYR (PubChem CID: 5281672) or MYT (PubChem CID: 5281673) were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov) (accessed on 15 February 2023) and optimized by the MMFF94 force field [44]. Molecular docking studies were performed by using AutoDock 4.2 (Scripps Research Institute, San Diego, CA, USA) package [45]. Rotatable bonds and non-polar hydrogen atoms of the ligands, namely MYR and MYT, were kept according to the default setting of Autodock. The size of the cubic grid for docking was set as 126 × 126 × 126 Å3, and the spacing of the grid was 0.375 Å. To find the optimal binding site for PK and the ligand, the Lamarckian Genetic Algorithm (LGA) with parameters set to 300 GA operations at a maximum number of energy evaluations of 2.5 × 106, population size set to 150, and the rest of parameters were used as default values. Molecular docking results were analyzed using Protein–Ligand Interaction Profiler (PLIP) [46]. ## 3.6. Molecular Dynamics Studies In this work, molecular dynamics of highest-scored molecular docking results for the PK-MYR and PK-MYT complexes were performed using the free package for molecular dynamics Desmond 2018.04 (DE Shaw Research, NY, USA) [47]. The energy-optimal molecular docking results were introduced into the center of a 10 × 10 × 10 Å cubic box, which was subsequently filled with TIP3P water molecules. To neutralize the solvent system, 5 Cl− as the counter ions were automatically added by Desmond. The system energy minimization and system relaxation processes were achieved using the Desmond default parameter setting procedure. The NPT ensemble was used, with a simulated temperature of 300 K and a pressure of 1.01325 bar, the temperature was controlled by the Nose-Hoover coupling method with a relaxation time of 1 ps, and the pressure was controlled by the Martyna–Tobias–Klein method with a typical relaxation time of 2 ps. The isotropic pressure coupling was used, and the integration step of the simulation was set to 2 fs. After 1 ns equilibrium, molecular dynamics simulations of the system were performed for 30 ns. ## 4. Conclusions In this work, PK was used as a fluorescent agent, and both MYR and MYT were selected as quenchers for the fluorescence spectroscopy and molecular simulation studies. By fluorescence spectroscopic studies, it was clear that the formations of PK-MYR and PK-MYT complexes are both processes of static quenching. The binding sites and binding constants of the complexes could be calculated by using double logarithmic equations. Compared with MYR, MYT has more binding sites and is closer to the Trp residue in the complex. Moreover, temperature affects the stability of the complexes of PK-MYR and PK-MYT. High temperature could decrease the complex stability, especially for PK-MYR. The thermodynamic calculations revealed that ∆H and ∆S for both processes were both negative, indicating that the complex formation processes are all exothermic. Hydrogen bonding and van der Waals forces are the main driving forces for complex formations. Negative ∆G values demonstrate that these complex formations are spontaneous processes. FRET experiments showed that MYT might bind closer to PK and has a higher energy transfer efficiency than MYR. Simultaneous fluorescence spectroscopy proves the conformational change of PK in the local environment of amino acid residues due to a change in polarity around Tyr residues, leading to a decrease in hydrophobicity. Molecular docking results help to understand the binding mode and host–ligand interactions between PK and MYR/MYT. Molecular dynamics results revealed the stability of the host and the ligands after a 30 ns simulation. It is clear that MYT has higher stability and low flexibility when bound to PK. 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--- title: 'Sedentary Behaviour and Telomere Length Shortening during Early Childhood: Evidence from the Multicentre Prospective INMA Cohort Study' authors: - Daniel Prieto-Botella - Dries S. Martens - Desiree Valera-Gran - Mikel Subiza-Pérez - Adonina Tardón - Manuel Lozano - Maribel Casas - Mariona Bustamante - Alba Jimeno-Romero - Ana Fernández-Somoano - Sabrina Llop - Martine Vrijheid - Tim S. Nawrot - Eva-María Navarrete-Muñoz journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048855 doi: 10.3390/ijerph20065134 license: CC BY 4.0 --- # Sedentary Behaviour and Telomere Length Shortening during Early Childhood: Evidence from the Multicentre Prospective INMA Cohort Study ## Abstract Sedentary behaviour (SB) may be related to telomere length (TL) attrition due to a possible pro-inflammatory effect. This study examined the association between parent-reported sedentary behaviour (SB) and leukocyte TL at the age of 4 and telomere tracking from 4 to 8 years. In the Spanish birth cohort Infancia y Medio Ambiente (INMA) project, we analysed data from children who attended follow-up visits at age 4 ($$n = 669$$) and 8 ($$n = 530$$). Multiple robust regression models were used to explore the associations between mean daily hours of SB (screen time, other sedentary activities, and total SB) at 4 years categorised into tertiles and TL at 4 years and difference in TL rank between age 4 and 8, respectively. At the age of 4, the results showed that children with the highest screen time (1.6–5.0 h/day) had a shorter TL of −$3.9\%$ ($95\%$ CI: −7.4, −0.4; $$p \leq 0.03$$) compared with children in the lowest tertile (0.0–1.0 h/day). Between 4 and 8 years, a higher screen time (highest tertile group vs. lowest tertile) was associated with a decrease in the LTL rank of −$1.9\%$ ($95\%$ CI: −3.8, −0.1; $$p \leq 0.03$$) from 4 to 8 years. Children exposed to a higher screen time at 4 years were more prone to have shorter TL at 4 and between 4 and 8 years of age. This study supports the potential negative effect of SB during childhood on cellular longevity. ## 1. Introduction Telomeres are DNA–protein complexes located at the end of chromosomes consisting of tandem-repeated TTAGGG sequences that maintain chromosome stability and integrity [1,2]. However, due to the end replication problem of cell division, full DNA replication cannot be completed, leading to a progressive natural telomere attrition [2]. In addition, telomeres can be vulnerable to oxidative stress and systemic inflammation induced by exogenous factors that can accelerate telomere length (TL) shortening [3,4]. As a biomarker of cellular aging, [5] a reduced TL has been associated with all-cause mortality and age-related diseases, including cardiovascular pathologies [6,7,8]. Non-genetic factors that can alter the oxidative stress and inflammation balance, including environmental exposures, have been associated with shorter TL in newborns, children, and adults [9,10,11]. In recent years, several studies have shown that unhealthy lifestyles, such as sedentary behaviour (SB) in adults, can negatively affect TL [12,13,14]. SB is defined as any waking activity with an energy expenditure ≤ 1.5 metabolic equivalent of task (MET), such as watching TV or reading [15]. In children and adolescents, SB has been associated with an increased risk of obesity, cardiometabolic diseases, or psychological ill-being [16,17]. Biologically, a sedentary lifestyle could induce significant pro-inflammatory effects [18,19,20], although the underlying mechanisms still need to be clarified. To date, no evidence for the effect of SB on TL has been shown during childhood and/or adolescence. Alternatively, few studies have examined the association, although indirectly, between physical activity (PA) and TL in young populations. The study conducted by Zhu et al. reported that vigorous PA could have a beneficial effect on TL in adolescents aged 14–18 years [21]. More recently, a randomised clinical trial in obese children aged 7–16 years showed that light PA and sedentary time were inversely associated with TL [22]. Although these results are consistent with earlier evidence for adults, the available research on TL shortening factors during childhood is still very incipient. Importantly, since TL attrition in early life has been shown as predictor of later life TL, [23] studying the environmental factors that can affect TL in paediatric populations remain crucial for understanding age-related disease in adulthood [24]. Therefore, this study had the following aims: first, to examine the cross-sectional association between parent-reported SB at the age of 4 and leukocyte TL in 4-year-old children, and second, to explore the association between parent-reported SB at 4 years and telomere tracking from 4 to 8 years. We hypothesised that higher mean daily hours of SB at age 4 would be associated with a shorter TL at the same age and a decrease in the TL rank between 4 and 8 years of age. ## 2.1. Study Population This study was performed using data from the birth cohort study INMA (Infancia y Medio Ambiente, https://www.proyectoinma.org/). Details of the INMA study protocol have been described elsewhere [25]. Briefly, 1909 women with singleton pregnancy were recruited between 2003 and 2008 in three areas of Spain (Asturias, Gipuzkoa, and Sabadell). A sample of 1383 ($72.4\%$) mother–child pairs were evaluated at the 4-year follow-up visit after delivery, accounting for the baseline population of the present study. In addition, a second follow-up visit was performed at 7 years in Asturias and Gipuzkoa and at 9 years in Sabadell. From now on, we refer to this second visit as the 8-year assessment. Based on the available data on the child’s SB and TL, a total of 669 children participated at the 4-year follow-up and 530 children participated at the 8-year assessment. The flowchart of the population sample included in this study is displayed in Figure S1 (Supplementary Material). This study was approved by the regional Ethical Committees and a written informed consent was obtained from all participants at each phase of the study. This study complies with the Helsinki declaration for human studies [26]. ## 2.2. Parent-Reported Sedentary Behaviour Parent-reported SB information was collected by a questionnaire based on the Children’s Leisure Activities Study Survey (CLASS) [27]. Parents were asked how many hours their child spent during weekdays and weekends watching TV/videos (screen time) and doing other sedentary activities (e.g., puzzles, books, dolls, homework, computer/videogames) outside school. Mean daily hours of screen time and other sedentary activities were calculated by averaging the time spent in these activities during weekdays and weekends as follows: (((SB time weekday × 5) + (SB time weekend × 2))/7). Once mean daily hours of screen time and other sedentary activities were estimated for each child, we calculated the total SB as the sum of these two variables. All SB variables (i.e., screen time, other sedentary activities, and total SB) were categorised into tertiles to classify the children according to low, middle, or high SB. ## 2.3. Blood Collection and DNA Extraction Child blood samples were collected during clinical examination and properly stored in EDTA tubes. At 4 years, DNA was extracted from whole blood using the Flexigen AGKT-WB-640 (Qiagen) kit in Gipuzkoa samples, Chemagen kit (Perkin Elmer) in Sabadell, and from buffy coat applying the QIAamp DNA Mini Kit (Qiagen) in Asturias. At 8 years, DNA was extracted from buffy coat using the above-mentioned kits. ## 2.4. Leukocyte Telomere Length Measurement Technical details on leucocyte TL measurement using qPCR [23] are described in Methods S1 (Supplementary Material). Telomeres were measured in triplicates, and on each run, a 6-point serial dilution of a pooled DNA ($$n = 12$$ DNA samples) was run to evaluate qPCR efficiency for telomere (T) and single-copy gene (S) runs. The efficiency was $107\%$ for T runs (R2 ranged from 0.995 to 0.999). Leucocyte TL at 8 years in the Sabadell cohort samples was assayed previously [28] using different single-copy gene primers (see Supplementary Material for more details). Relative leucocyte TL was calculated separately for each cohort using qBase software (Biogazelle, Zwijnaarde, Belgium). In qBase, TL is calculated as a calibrated normalised relative quantity (CNRQ) [29]. The latter is achieved by first calculating the RQ based on the delta-Cq method for T and S obtained Cq values, using target specific amplification efficiencies. As the choice of a calibrator sample (sample to which subsequent normalisation is performed) strongly influences the error on the final relative quantities (as a result of the measurement error on the calibrator sample), normalisation is performed to the arithmetic mean quantification values for all analysed samples per cohort, which results in the NRQ. Finally, as samples per cohort are measured over multiple qPCR plates, 8 inter-run calibrators (IRCs) are used to calculate an additional correction factor to eliminate run-to-run differences, resulting into the final T/S ratio (CNRQ). Mathematical calculation formulas to obtain RQ, NRQ, and CNRQs are provided by Hellemans et al., 2007 [29]. On each run, the reliability/accuracy of the applied protocol was assessed by calculating the intraclass correlation coefficients (ICC) of triplicate measures for T values (0.957; $95\%$ CI: 0.954–0.96; $p \leq 0.0001$), S values (0.968; $95\%$ CI: 0.965–0.97; $p \leq 0.0001$), and T/S ratio’s (0.925; $95\%$ CI: 0.918–0.93; $p \leq 0.0001$), using the ICC R-code provided by the Telomere Research Network [30]. In addition, based on the 8 IRCs ran over all the qPCR plates, an inter-assay ICC was calculated (0.898; $95\%$ CI: 0.77–0.948; $p \leq 0.0001$). Based on the standard curves, qPCR efficiency for T runs was 107 on average. ## 2.5. Study Covariates Covariates included data collected during pregnancy or at birth: child’s sex (male or female), cohort (Asturias, Gipuzkoa, or Sabadell), preterm birth (no or yes), mother’s periconceptional body mass index (BMI, calculated as weight in kilograms (kg) divided by height in meters squared (m2)), mother’s country of origin (Spain or other) and mother’s educational level (primary or less, secondary school, or university). At the 4 years follow-up interview, the following was collected: child’s characteristics (age (years)), BMI (kg/m2), blood extraction date, season of blood extraction (spring, summer, autumn, or winter), total energy intake (in kilocalories (kcal) per day), ultra-processed food intake according to the NOVA classification (grams (g) per day) [31], relative Mediterranean diet (rMed) score [32], extracurricular PA (MET-hours per day) [33], and mother’s characteristics (age (years) and smoking status (yes or no)). Child nutritional data were assessed using a food frequency questionnaire previously validated in Spanish children [34]. Follow-up time was defined as the interval between the child’s age at the baseline and age at the visit assessment at 8 years. ## 2.6. Statistical Analysis R software version 4.1.0 (R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; http://www.R-project.org) was used to conduct the statistical analyses. All applied statistical tests were bilateral, and the significance level was established at 0.05. Distribution of the continuous variables was checked using the Kolmogorov–Smirnov test. Mother and child characteristics were described according to the children’s total SB time tertiles and compared using the ANOVA, Kruskal–Wallis, and Chi-square tests. Multiple robust linear regression models using the robustbase R package [35] were conducted separately to explore the associations with TL at 4 years and changes in TL ranking from 4 to 8 years. To control potential confounding, several models were fitted by including all covariates with $p \leq 0.2$ in the bivariate analysis and those that changed the magnitude of main effect by $10\%$ after a backward–forward elimination procedure [36]. In the cross-sectional analysis at 4 years of age, the association between SB and TL was explored using log-level regression models, where TL was log10-transformed. Three different models were fitted for this analysis: Model 1 was adjusted for blood storage date and cohort; Model 2 included variables of Model 1 plus child’s daily total energy intake, daily ultra-processed food intake, and age at baseline; and Model 3 included variables of Model 2 plus child’s sex. Final estimates were presented as percentage (%) change in TL. Since TL at 8 years in children from Sabadell was measured using different single-copy genes, the values of TL at baseline (i.e., 4 years) were not directly comparable to those at follow-up (i.e., 8 years). Therefore, to evaluate the association between SB at 4 years and changes in TL from 4 to 8 years, we used a ranking method for examining telomere tracking [37]. Firstly, we ranked TL at 4 and 8 years separately by cohort from the longest to the shortest value (coded from 1 to n). The Spearman’s correlation between 4- and 8-year TL ranking was 0.58 ($p \leq 0.001$). Secondly, we calculated the difference in telomere rank for each child between periods. The following formula was used to calculate the difference in telomere ranking (ΔR):[1]ΔR=R1−R2n−1100 where n is the sample size, R1 is the baseline rank, and R2 is the follow-up rank. Due to the difference in telomere ranking, which is directly determined by the sample size, we transformed it into a value of 100 to ease the interpretation in percentage terms. We explored the association with TL ranking using four different models. Models 1, 2, and 3 were equally adjusted for the variables used in the cross-sectional analysis. Model 4 was additionally adjusted for the follow-up time and TL at baseline (i.e., 4 years). Final estimates were provided as % change in ranking, where a negative value indicated a decline in telomere ranking as a relative change between TL at 4 years and 8 years. To quantify the heterogeneity among the study cohorts, all associations were initially analysed using meta-analytic techniques to obtain combined estimates. The heterogeneity was quantified using I2 statistics [36,38] with the meta R package [39]. Since all I2 values obtained for the main outcomes were < $50\%$, we performed the analysis adding the cohort variable to the adjustment of all the models. Several sensitivity analyses were also conducted to examine the robustness of the main findings. Using Model 3 and Model 4 as the main models for the association with TL at 4 years and with TL ranking from 4 to 8 years, respectively, we separately explored the effect of the following child variables: other sedentary activities, rMed score, BMI, season of blood extraction, and extracurricular PA. In addition, mother variables such as periconceptional BMI, smoking status at baseline, and educational level were added jointly. We also examined whether the associations changed substantially according to the child’s sex. Finally, we conducted an analysis excluding children born preterm. ## 3.1. Characteristics of the Study Population Table 1 describes the general characteristics of the study population by the total SB time tertiles. A total of 350 children ($52.3\%$) were boys and the median (IQR) of BMI was 16.0 (15.2–17.0) kg/m2. The mean (SD) of age was 4.4 (0.2) and 8.5 (0.6) years at baseline and follow-up, respectively. Overall, children spent a total of 2.6 (2.0–3.6) h/day in SB. Children located in the highest total SB tertile showed higher daily energy (1616 kcal.; $95\%$ CI: 1453, 1862) and UPF (417 g; $95\%$ CI: 291, 576) intakes. In addition, children grouped into the middle and high tertiles of total SB presented lower extracurricular PA (9.3 MET-h/day; $95\%$ CI: 6.6, 12.4 and 9.5 MET-h/day; $95\%$ CI: 6.5, 12.5, respectively) compared with the lowest tertile. Regarding maternal characteristics, women had a mean age at baseline of 37.1 (4.2) years, and a median (IQR) periconceptional BMI of 22.9 (20.9–25.5) kg/m2. The vast majority were mothers born in Spain ($94.0\%$) and $39.6\%$ had studied at university. However, mothers whose children were in the highest total SB time tertile had greater rates of primary or secondary studies compared to those whose children were in the other tertiles. The comparison of mother and child characteristics before and after participant selection is provided in Table S1 (Supplementary Materials). ## 3.2. Sedentary Behaviour and Telomere Length at the Age of 4 In both minimal adjusted and fully adjusted models, daily screen time was negatively associated with TL in children at the age of 4 (Table 2). Compared to children with lower daily screen time, those who spent from 1.1 to 1.5 hr/day and from 1.6 to 5.0 h/day watching TV/videos showed a shorter TL (−3.3; $95\%$ CI: −6.7, 0.4; $$p \leq 0.07$$ and −3.9; $95\%$ CI: −7.4, −0.4; $$p \leq 0.03$$, respectively). Estimates for children in the middle tertile of other sedentary activities and those with middle and high total SB did not reach statistical significance. ## 3.3. Association between Sedentary Behaviour at 4 Years and Telomere Length Ranking from 4 to 8 Years The association between SB variables at the age of 4 and telomere ranking from 4 to 8 years is shown in Table 2. An increased screen time was generally associated with a reduction in the TL ranking between 4 and 8 years of age, although the main results were observed after applying the fully adjusted model including the relevant variables besides the follow-up time and TL at baseline (Model 4). Children in the highest tertile of daily screen time presented a downward (accelerated shortening) shift in TL ranking of −$1.9\%$ ($95\%$ CI: −3.8, −0.1; $$p \leq 0.03$$) compared to those situated in the lowest tertile. No association was observed for other sedentary activities and total SB. ## 3.4. Sensitivity Analysis Figure 1 shows the sensitivity analyses for the associations between high daily screen time and TL at 4 years and TL ranking from 4 to 8 years. The association of high screen time with TL at 4 years increased substantially when excluding the boys (−5.7; $95\%$ CI: −10.6, −0.5), although it slightly dropped when excluding the girls (−3.4; $95\%$ CI: −8.3, 1.7). However, we found no statistically significant interaction term between screen time vs. child’s sex (p ≥ 0.2). The effect of high screen time on TL ranking from the age of 4 to 8 was slightly reinforced when adjusting for mother’s characteristics (−2.3; $95\%$ CI: −4.1, −0.4) and when excluding girls (−2.3; $95\%$ CI: −4.6, 0.0). Our findings remained robust after excluding or adjusting for other relevant potential variables. ## 4. Discussion This study supports the fact that a higher screen time at 4 years is associated with a shorter TL at the same age and with a reduction in telomere ranking between the ages of 4 and 8. Although we observed that a general sedentary lifestyle also tended to have a negative effect on TL, the main findings disclosed that TL attrition during childhood was mainly due to a higher screen time after adjusting for relevant variables such as a child’s BMI or extracurricular PA. Our findings are consistent with previous studies conducted in adults and, to our knowledge, this is the first time that this association has been reported at early ages. Moreover, it should be noted that the analysis for the TL ranking may suppose valuable evidence to reinforce the limited results obtained from the research on the child population. To date, few studies have explored the relationship between lifestyle factors such as SB, PA, or TL in youth. A cross-sectional study conducted in 667 adolescents aged 14–18 years showed a positive association between vigorous PA and TL [21]. Similarly, a recent randomised clinical trial of a lifestyle intervention in 102 Spanish children (7–16 years) with abdominal obesity indicated that higher levels of PA were positively associated with TL, whereas SB and light PA showed a negative effect [22]. Based on these previous results, in this study, we examined whether a child’s PA or BMI could be likely confounders of the detrimental effect of SB that we observed on TL. After accounting for these covariates, the estimates did not show changes, suggesting that the effect found for screen time was independent of them. Several studies have suggested that a sedentary lifestyle could increase the concentration of pro-inflammatory cytokines and adipokines, incrementing chronic low-grade inflammation [13,19]. Although the underlying biological mechanism for the association between SB and TL shortening still needs to be clarified, a potential explanation may be attributed to the fact that a low-grade inflammatory state may affect telomere homeostasis, thus inducing TL shortening via oxidative stress [40]. Importantly, SB has been associated with the rise of pro-inflammatory biomarkers such as C-Reactive Protein (CRP), leptin, and interleuckin-6 in children aged 6–8 years [41,42]. In addition, higher TV viewing time in 7–10-year-old children has been associated with greater levels of CRP and of sVCAM-1, a biomarker of endothelial dysfunction [43]. Biologically, the increment of pro-inflammatory biomarkers may induce an increase in apoptosis, cellular senescence, and oxidative stress, augmenting systemic inflammation and cellular aging, which are subsequently linked with TL shortening [44]. The results of this study are in accordance with cross-sectional studies conducted in the adult population. A study published by Xue and colleagues with 518 participants (20–70 years) showed that every hour/day spent watching TV was associated with an TL shortening of 72 base pairs [12]. In fact, results from the same study indicated that adults aged 20 to 40 in the highest tertile of daily screen time had a $4.0\%$ shorter TL, which is similar to the results obtained in the present study ($3.9\%$ reduction in TL). Another study with 6405 adults (20–84 years) from the 1999–2002 National Health and Nutrition Examination Survey (NHANES) observed that for every 1 h/day of screen-based SB, participants had $7\%$ increased odds of having TL in the lowest tertile [13]. However, a later study based on the same data did not find an association between SB and TL [45]. In sum, although the available evidence seems to indicate that SB has a negative effect on TL, the results remain inconsistent, probably as a consequence of the different criteria used for the measurement of SB as well as resulting from other factors. More research is therefore needed to clarify the role of SB on cellular longevity. Our study presents several strengths. First, the prospective design of the INMA project allowed us to verify the negative effects of SB on TL attrition observed at the age of 4 in a later assessment during childhood. Second, we included different types of SB that we examined as independent variables to account for possible differences in sedentary lifestyles, which may provide more support to the effect found for the screen time. Third, the main associations remained similar after applying the sensitivity analyses, reinforcing the findings’ robustness. Finally, it should be noted that the study population size was considerably reduced after participant selection. However, there were no differences between the included and non-included participants, suggesting that a selection bias did not occur (Table S1 in the Supplementary Material). However, our study should be interpreted within the context of its potential limitations. Our SB variables were assessed using a questionnaire based on the CLASS, which has not been validated in Spanish children. In this sense, although a potential misclassification may occur, it should be nondifferential. Regarding TL, different DNA extraction kits were used among cohorts. Additionally, TL at 8 years from the Sabadell cohort was measured with a slightly different qPCR methodology. Nevertheless, we minimised the impact of these variations by normalising the TL by cohort and using a ranking approach in the analysis. ## 5. 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--- title: Participating in a School-Integrated Daily Exercise Program Improves Motor Performance Significantly in School-Children authors: - Denise Homeyer - Nima Memaran - Momme Kück - Lena Grams - Jeannine von der Born - Elena Bauer - Martina Schwalba - Arno Kerling - Nadine von Maltzahn - Alexander Albrecht - Axel Haverich - Meike Stiesch - Anette Melk - Uwe Tegtbur journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048861 doi: 10.3390/ijerph20064764 license: CC BY 4.0 --- # Participating in a School-Integrated Daily Exercise Program Improves Motor Performance Significantly in School-Children ## Abstract Children’s sedentary time has increased, while daily physical activity and motor performance have decreased. We evaluated an integrated school-based exercise program by assessing changes in motor skills after one year and comparing these changes to children who did not participate. We included 303 children from five schools in this longitudinal study and assigned them either to the exercise group (EG; $$n = 183$$ with daily exercise program) or the waiting group (WG; $$n = 120$$). Motor skills were assessed at baseline and after one year. Mixed modeling was used to analyze inter-group differences of change in motor skills and to determine the effect of sex, age group, and weight status. EG improved more strongly than WG for sprint, side jumps (both $$p \leq 0.017$$), stand and reach ($$p \leq 0.012$$), and ergometry (p ≤ 0.001) when compared to WG. Girls improved more strongly in the sit-ups than boys, second graders more than fifth graders in the backwards balance and the ergometry, and non-overweight children more in the standing long jump than overweight children. The exercise program is effective in increasing motor skills and physical fitness. Girls were not disadvantaged, and overweight children profited as much as their non-overweight peers in all categories but one. ## 1. Introduction Children and adolescents do not exercise sufficiently [1,2]. The target level of 60 min physical activity per day recommended by the World Health Organization (WHO) [3] is only reached by $22.4\%$ of girls and $29.4\%$ of boys aged 3–17 years in Germany and is less likely to be reached with increasing age [2]. This lack of physical activity negatively affects overall health and physical capability in children [4,5] and can be both a cause and consequence of being overweight [6,7]. Likewise, sedentary time, associated with poorer motor coordination [8], has increased for children, especially during school [9]. In parallel, motor performance is low [10,11]. Between 1975 and 2002, motor performance of German children and adolescents decreased by $10\%$ [12], and was interestingly more pronounced in the 12- to 17-year-olds with $12.5\%$ than the 6- to 11-year-olds with $5.5\%$ [12], demonstrating a clear role of age. Furthermore, motor performance is dependent on weight status as well as sex. For instance, overweight and obese children exhibited impaired motor performance in $43.4\%$ to $70.8\%$, respectively, and show poorer results in dynamic body coordination [13]; girls exhibited higher performance in fine motor skills, while boys showed higher performance in catch and dribble gross motor skills [14,15]. The downward trend in motor performance persisted until the turn of the century and seems to have stabilized for Germany [16] and internationally [17], with no further decrease in motor skills. Working towards increasing children’s motor skills again is vital, as they are essential for healthy and appropriate development [18]. Most importantly, low motor performance in childhood is associated with an increased risk of cardiovascular disease in adulthood [19,20]. The value of health interventions during childhood is, therefore, evident. Children spend much time in school, mostly seated, leading to lower daily physical activity [21]. Therefore, interrupting sedentary periods by integrating several brief activities over the school day can significantly increase daily physical activity. Such a lifestyle modification is as effective as a structured exercise program, improving cardiorespiratory fitness [22] and motor skills [23]. Several studies have described the effect of a school-based physical activity program on improving motor skills. They, however, may show particular disadvantages. Some can often only be implemented with a professional physical activity teacher [24,25,26], have a short intervention duration [24,27], or only offer extracurricular activities [25,28,29], which may lead to predominantly targeting children that already show a certain motivation for exercise. Others, such as the One Mile program, successfully increased daily physical activity with a practical, low-cost in-school program that improved endurance and skinfold thickness [30] but did not investigate the program’s effect on motor skills. Our study aimed to develop and implement a comprehensive exercise program that can effectively improve a variety of motor skills; a program that is tailored to children that is simple, inexpensive, and can easily be integrated into the school setting; can be conducted with little additional staff beyond the initial phase; and takes place during the regular curriculum, so that all children have the opportunity to participate. We further aimed to evaluate this program with children from two different age groups by assessing changes in motor skills after one year and comparing these changes to children who did not participate. ## 2.1. Study Design A convenience sample of primary and secondary schools in Lower Saxony and North Rhine-Westphalia was approached. Of those, five schools agreed to participate. The 2nd and 5th grades were chosen because 4th-grade children would have been lost to follow-up as they changed from primary to secondary school. The 1st grade was not chosen as it was their first year in school. Therefore, this longitudinal cohort study investigated children from all 2nd-grade and 5th-grade classes (aged 8.2 ± 0.5 and 11.4 ± 0.5, respectively) after obtaining written informed consent from the parents or caregivers and assent from the participating children. The baseline examination was performed between April and June 2017 (v0), and the follow-up examination was one year afterward (v1). The assignments to the respective groups could not occur on an individual level but per class. Therefore, after completing v0, classes were randomly assigned to the exercise program (i.e., the exercise group, EG) or made to wait one year before starting with the exercise program (i.e., the waiting group, WG). Every participating grade of each school contained WG as well as EG classes in order to assure comparability. This study is in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Hannover Medical School (No. 7290). ## 2.2. Motor Tests For the comprehensive motor skill assessment, we chose the German Motor Test 6–18 (Deutscher Motorik-Test 6–18, DMT 6–18), an established tool to assess motor skills in children that is recommended by the German Society of Sport Science [31]. This test battery has established high objectivity, reliability, and validity for evaluating motor skills in children and adolescents aged 4–17 [31]. A detailed description for each test item is given in the supplementary material. In short, speed was assessed through a 20 m sprint (measured in seconds), coordination in precision-tasks by backwards balancing (measured in the number of steps), coordination under time pressure with side jumps (measured in the number of jumps), trunk and sciatocrural muscle group flexibility through the stand and reach test (measured in centimeter), endurance-strength of the upper extremities and trunk muscles with push-ups (measured in the number of push-ups), endurance-strength of the torso with sit-ups (measured in the number of sit-ups), and lower extremity jumping power with standing long jumps (measured in centimeter). All assessors were uniformly trained by an experienced sports scientist (D.H.) to ensure the accuracy of the measurements. Every assessment of motor skills (i.e., at v0 and v1) was conducted under the supervision of the same experienced sports scientist (D.H.). ## 2.3. Ergometry Maximum endurance capacity was assessed via bicycle ergometry according to a modified Godfrey protocol [32], with maximum Watt per kg bodyweight (W/kg) as the endpoint. The workload was increased in 15 Watt steps at 1-min intervals. All subjects were encouraged to exercise until exhaustion (breathlessness and leg muscle pain or a maximum calculated heart rate (calculated 220—age in years) [33], multiplied by 0.85 to account for potential overestimation of the formula in children [34]). ## 2.4. Exercise Program The exercise program was designed to be primarily conducted by teachers, who were guided by sports scientists who visited every class on two days every week for the initial phase. It comprised of several modules. The main module was the exercise impulses, where children performed a 5-min exercise during every single lesson, coordinated by the teachers (5–6 lessons per day, 5 days per week; identical within a school grade for both groups). Exercises were chosen from the literature [35,36] to be specifically suitable and fun for children, take a maximum of 5 min, and easily be performed in the classroom. They represent four categories (strength, endurance, coordination, and relaxation), with 10 different exercises for every category. Since speed and flexibility were aspects in almost all exercises, they were not assigned separate groups. Most of the exercises were related to a school subject to facilitate integration into the classroom (e.g., “math jogging”, where children jump and squat depending on the result of the calculation). EG classes were provided with a box of index cards describing every exercise. A list and short description of every exercise are given in the supplemental material, and an example of the cards in Figure S1. Teachers received detailed instructions during an evening workshop by trained sports scientists on how to perform these exercises correctly and to use all four categories evenly. In addition to the movement impulses, the following modules were provided once per week, under instructions from a sports scientist. A 15–20 min early morning exercise module was offered before the first lesson; a 15-min exercise module was provided during the great break and a 45-min module after school or before afternoon classes for the secondary school students. Once per week, an afternoon module of 45 min was organized for every class. Lastly, teachers and children from the EG received general information on the benefits of daily physical activity before the start of the program. Teachers were furthermore instructed to document each exercise impulse and the additional modules. ## 2.5. Anthropometric Measurements Body height and body weight were measured according to Dippelhofer et al. [ 37] and used to calculate the body mass index (BMI). BMI was normalized for age and sex, using WHO normative data [38] and expressed as z-scores, with a z-score of 0.0 representing the 50th percentile. Children were grouped according to BMI into non-overweight (defined as BMI < 85th percentile, i.e., z-score < 1.036) or overweight (defined as BMI ≥ 85th percentile, i.e., z-score ≥ 1.036) [39]. ## 2.6. Statistical Analysis All calculations were performed using Statistical Analysis Software Enterprise Guide 7.1 (Cary, NC, USA) and SPSS (Version 28, IBM corp., Armonk, NY, USA). Categorical variables are given as frequencies and percentages, and continuous variables as means and standard deviations. The change in motor test results was the primary endpoint. For the univariate analyses at baseline, the two-sided t-test was used for continuous variables and the χ2-test for categorical variables. For the univariate analysis, a paired t-test was used for within-group comparisons. The interaction between time and group was calculated with an ANOVA with repeated measurements for the between-group comparison. As sex, age, and weight status are known to influence motor skills, linear regression modeling was utilized to calculate corrected means for the change in motor skills (calculated as the Delta (Δ) of the result of v1 − v0) adjusted for sex, age, and BMI, differentiated by the WG and EG group category. For the analysis of interdependent factors, we calculated a separate multivariable linear regression model for every endpoint, each with the group category and interaction terms between (i) the group category and sex, (ii) the group category and age group, and (iii) the group category and BMI category, as covariates. A p-value of <0.05 was considered significant. ## 3.1. Study Population Out of 598 children that were invited, 357 ($$n = 205$$ for second grade, $$n = 152$$ for fifth grade) agreed to participate and were included in the study. The whole cohort of 357 children is described in detail [40]. Out of these, 303 children completed one or more of the motor skill tests at both time points v0 and v1 and comprise the sample for this study (EG $$n = 183$$; WG $$n = 120$$; Figure S2). From these 54 dropouts, 13 children were absent from school during the day of examination due to illness, 22 had left the school, and 19 declined to participate further. Baseline characteristics are given in Table 1. At baseline, there were no significant differences between the EG and the WG regarding gender, age, weight, height, or BMI. ## 3.2. Motor Skills at Baseline At baseline, there were no significant differences in the motor skills between the WG and the EG, except for the push-ups, where the EG achieved more repetitions (WG 12.1 ± 3.3 repetitions, EG 13.2 ± 3.9 repetitions, $$p \leq 0.026$$; Table 1). Boys performed better in the sprint, stand and reach, sit-ups, standing long jumps, and ergometry than girls, with no difference for the backwards balance, side jumps, and push-ups (Table S1). Fifth graders were better in the sprint, side jumps, and standing long jump, and second graders were better in the stand and reach test (Table S2). Compared to the overweight children, non-overweight children performed better in all tests except for the stand and reach test, where no significant difference could be seen between the two groups (Table S3). ## 3.3. Physical Activity Due to the Exercise Program As a measure of program fidelity, teachers were tasked to document when performing an exercise impulse during the lesson and to document participation in the additional modules of the exercise program. Based on this documentation, an average of 24 min and 34 min of additional exercise per day due to participation in the total exercise program could be achieved for the second and fifth graders, respectively. This calculation did not include regular curricular physical education classes that were available to EG and WG. ## 3.4. Within-Group Change in Motor Skills after One Year The assessments of motor skills were one year apart (time between v0 and v1 for EG 370 ± 22 days; WG 367 ± 19 days, $$p \leq 0.235$$). When looking at within-group changes after one year, the EG showed significant improvements in all motor skill test items. The WG revealed significant improvement for the sprint, backwards balance, side jumps, push-ups, and the standing long jump, but not in the stand and reach, sit-ups, or ergometry (Table 2). ## 3.5. Between-Group Change in Motor Skills after One Year After one year, the unadjusted between-group analysis of the change showed that EG children improved significantly more strongly in the sprint, backwards balance, side jumps, stand and reach, sit-ups, and ergometry, than the WG children (Table 2). After adjusting for sex, age, and BMI, the EG still exhibited a significantly stronger improvement in the sprint, side jumps (both $$p \leq 0.017$$), stand and reach ($$p \leq 0.012$$), and ergometry ($p \leq 0.001$), with a tendency for backwards balance and sit-ups ($$p \leq 0.056$$ and $$p \leq 0.053$$, respectively; Figure 1). ## 3.6. Interdependent Factors Associated with the Change in Motor Skills In order to analyze whether the exercise program affected children differently according to their sex, age group, or BMI category, separate linear regression models were calculated for each endpoint, with the change in the motor skill test item as the dependent variable and interaction terms between group category and sex, group category and age group, or group category and BMI category, as covariates. The complete models are given in Table S4a–h. Table 3 summarizes the results of these separate models. WG boys increased the number of push-ups more than WG girls, and EG girls expanded the number of sit-ups more greatly than EG boys. Second-grade WG children improved significantly more strongly than fifth-grade WG children in the sprint and push-ups, and second-grade EG children improved significantly more strongly than EG fifth graders in the backwards balance, push-ups, and ergometry. Regarding the BMI category, non-overweight EG children improved the standing long jump more robustly than their overweight EG peers. ## 4. Discussion This prospective, longitudinal cohort study demonstrates an effective school-based exercise program that can be easily implemented into school life and significantly improves children’s motor skills after one year compared to peers who did not participate. As school is a socialization institution that is a well-suited setting for programs intending to positively influence healthy behavior [41], it is ideal for this inclusive and effective exercise program. Furthermore, a school-based program is easily accessible for all children, regardless of their socioeconomic status or intrinsic motivation to be physically active in their free time. Our exercise program shows particular strengths. Its simplicity is particularly noteworthy, as this is the prerequisite for long-term implementation, in contrast to other programs requiring additional equipment [26] or staff [28,29]. We only require the index card box and no additional space or venues. Teachers can effectively conduct it after the initial phase. The exercise program was implemented and carried out for one year. The longer duration of the intervention compared to other studies [25,27], allowed analysis of the long-term effects. Lastly, the program requires minimal additional time and does not disrupt the curriculum [26,28,29]. These characteristics make it easily implementable in different school settings. A major strength of the study is the objective assessment of the effect of participating in the exercise program on a variety of motor skills and comparing the results with those of age-matched peers from the same school who did not participate. The comparison of the change in motor skills between the EG and WG enabled us to concisely discern the impact of the exercise program from potential confounders, such as physiological development, or a potential learning effect from repeatedly performing these motor tests. By engaging in the exercise program for one year, children saw a clear improvement in their speed, coordination under time pressure, and flexibility (measured by the sprint, side jumps, stand and reach tests, respectively), as well as their maximum endurance capacity (gauged bicycle ergometry). These advancements were significantly stronger in the EG than the WG and remained significant after adjusting for age, sex, and BMI. Moreover, a tendency could be observed for coordination in precision-tasks and endurance-strength of the torso (showed by the backwards balance and sit-ups, respectively). Apart from the apparent reason for the observed effects, i.e., the additional physical activity in the EG, another factor may also play a role. It can be speculated that participation in daily exercise and performing fun movement impulses during every lesson every day for the intervention period of one year leads to a higher personal affinity to exercise and, therefore, being physically more active outside the school setting as well [42]. It would be especially desirable if this effect could be achieved with children that would otherwise not have considered sports and exercise a leisure activity, thereby creating a positive trajectory towards a healthier lifestyle. To address this aspect, we are currently enrolling a study with a larger sample and quantifying 24 h of physical activity to gauge the change in daily activity after school. Of note, upper extremity strength and lower extremity jumping power (tested through push-ups and standing long jumps, respectively) did not show a differing development between the groups. This result could be due to the fact that building additional strength, such as jumping power, may require a longer intervention period and higher intensity [43]. Upper extremity strength and, to a lesser degree, lower extremity jumping power could not be targeted as intensely by the exercise impulses during lessons, which constituted the main module of the exercise program. Possible reasons may be that in order to generate an adequate stimulus for muscle growth, the use of weights or a higher number of repetitions and sets would be necessary and would require instructions on the proper exercise technique in order to prevent injuries (which could compromise the game character of the exercise). Therefore, it is possible that the exercise program may not have been intense enough to boost these specific motor skills significantly. In addition, these two tests include complex movement sequences (e.g., the timing of the arm swing and jump), which might require more targeted training. At baseline, we saw differences between the boys’ and girls’ results, which align with the literature [44]. However, the results were similar for the side jumps and push-ups, whereas other studies saw sex differences as well [45,46]. When investigating the differential effect of the program between the sexes, our data showed that the increase in push-ups was less for girls than boys in the WG. Interestingly, this effect could not be observed in the EG, suggesting that the exercise program helped the girls keep up with the boys. With the sit-ups, EG girls profited significantly more than EG boys. Considering that no sex disadvantage for EG girls was seen for the push-ups and even an advantage for EG girls for sit-ups, it seems that our program led to girls benefiting more in building strength-endurance than boys. The program was more effective for second graders in the backwards balance and ergometry. Second graders increased the number of push-ups more strongly than fifth graders, but this was seen in both WG and EG. Taken together with the fact that WG and EG were not significantly different in this particular test item, it may be that the observed effect is due to physiological development in this age group [47]. It was not surprising to find that overweight children performed worse than their non-overweight peers in all the tested motor skill categories (except for flexibility), in line with the literature [48,49]. Overweight children are known to show less development in their motor skills [50]. It was, therefore, surprising to see that the change in the tested motor skills in overweight children did not significantly differ from that in their non-overweight peers in any test item other than the standing long jump. In particular, our data showed no disadvantage for overweight children for the change in ergometry. This result is especially important, as high physical fitness alleviates the metabolic risk of being overweight [51,52]. Even though we could not see a significant catch-up development (i.e., a stronger effect in overweight than non-overweight children), this finding still is very encouraging, as it suggests that overweight children can keep up with their non-overweight peers at this age if they are physically active. Overweight children are more likely to originate from socially disadvantaged backgrounds [53,54], associated with lack of exercise [55,56]. Furthermore, overweight children are more likely to be reluctant to engage in sports activities because of shame or the fear of being bullied [57]. Placing this easily accessible exercise program into the school setting free of charge can represent a suitable tool to reach disadvantaged groups that may not be able to engage in extracurricular sports due to financial opportunities, lack of intrinsic motivation, or may be reluctant to do so. Our study shows several strengths. It is a multi-site prospective longitudinal study on a large cohort of children from two age groups. Secondly, we employed a standardized protocol of motor tests that are suitable for children conducted by uniformly trained sports scientists. Thirdly, comparing the change in motor skills of children to that of age-matched peers from the same schools allowed us to discern the exercise program’s effect precisely. The study also carries a few limitations. Based on the modules’ documentation, we could not reach the intended goal of an additional 60 min of daily activity. One reason was partially incomplete documentation of exercise impulses they conducted during the lessons by teachers. It can, therefore, be assumed that the actual average exercise time was higher than reported. We intended to use accelerometers as a measure of daily physical activity and a measure of fidelity to the program. We could not achieve this because many devices were not returned or misused. These data would have allowed us to not only measure changes in overall physical activity but also to account for differences in physical activity outside of school, especially during the holidays. However, as WG and EG children attend the same school and are of the same age, it can be assumed that both groups share comparable social and financial backgrounds, live in the same area, and have comparable leisure habits. Therefore, the possibility to compare the EG to the WG is a particular strength of the study that allowed us to demonstrate the beneficial effect of the exercise program on the participants’ motor skills and physical fitness. A stringent and facile measure to document intervention fidelity is needed to capture all measures taken in future studies fully. Our results are limited to the two age groups of second-grade and fifth-grade children; therefore, generalization to other age groups is limited. We cannot, therefore, make valid inferences on trajectories of motor development in children across different age groups. Further studies involving other age groups, especially teenagers, are warranted. ## 5. Conclusions In conclusion, our study presents a simple and effective in-school exercise program that can be easily implemented into the daily school routine, significantly increasing motor skills and physical fitness. ## References 1. 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--- title: Ameliorative Effect of Posidonia oceanica on High Glucose-Related Stress in Human Hepatoma HepG2 Cells authors: - Marzia Vasarri - Emanuela Barletta - Maria Stio - Maria Camilla Bergonzi - Andrea Galli - Donatella Degl’Innocenti journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048879 doi: 10.3390/ijms24065203 license: CC BY 4.0 --- # Ameliorative Effect of Posidonia oceanica on High Glucose-Related Stress in Human Hepatoma HepG2 Cells ## Abstract Metabolic disorders characterized by elevated blood glucose levels are a recognized risk factor for hepatocellular carcinoma (HCC). Lipid dysregulation is critically involved in the HCC progression, regulating energy storage, metabolism, and cell signaling. There is a clear link between de novo lipogenesis in the liver and activation of the NF-κB pathway, which is involved in cancer metastasis via regulation of metalloproteinases MMP-$\frac{2}{9.}$ As conventional therapies for HCC reach their limits, new effective and safe drugs need to be found for the prevention and/or adjuvant therapy of HCC. The marine plant Posidonia oceanica (L.) Delile is endemic to the Mediterranean and has traditionally been used to treat diabetes and other health disorders. The phenol-rich leaf extract of Posidonia oceanica (POE) is known to have cell-safe bioactivities. Here, high glucose (HG) conditions were used to study lipid accumulation and fatty acid synthase (FASN) expression in human HepG2 hepatoma cells using Oil Red O and Western blot assays. Under HG conditions, the activation status of MAPKs/NF-κB axis and MMP-$\frac{2}{9}$ activity were determined by Western blot and gelatin zymography assays. The potential ameliorative role of POE against HG-related stress in HepG2 cells was then investigated. POE reduced lipid accumulation and FASN expression with an impact on de novo lipogenesis. Moreover, POE inhibited the MAPKs/NF-κB axis and, consequently, MMP-$\frac{2}{9}$ activity. Overall, these results suggest that P. oceanica may be a potential weapon in the HCC additional treatment. ## 1. Introduction Hepatocellular carcinoma (HCC) is the second leading cause of cancer death worldwide and remains a global health challenge [1]. HCC can have various etiologic causes, but among the most important risk factors for HCC are metabolic disorders characterized by elevated blood glucose levels, including diabetes, obesity, and metabolic syndrome. The alarming increase in these metabolic disorders worldwide reflects the rising incidence of HCC [2,3]. Although the mechanisms by which obesity and steatosis promote liver carcinogenesis remain quite unclear, the role of lipid dysregulation in this process is widely recognized [4]. Lipid dysregulation is critical in both the development and progression of liver cancer [5]. There is ample evidence that lipids promote tumor activity by controlling various biological processes, including energy storage and metabolism, epigenetic regulation, cell signaling, and many others [6]. De novo lipogenesis represents one of the hallmarks of cancer and is frequently upregulated in solid tumors, reducing cancer cell growth’s reliance on exogenous fatty acids [7]. Increased expression of fatty acid synthase (FASN), which catalyzes de novo synthesis of long-chain fatty acids, has been described in several tumor types, while its inhibition has been shown to have antitumor activity [8]. Recent literature reports a correlation between hepatic lipogenesis and nuclear factor kappa B (NF-κB) activation [9]. As a matter of fact, NF-κB is one of the major transcription factors that regulate development, inflammatory responses, and tackle nutritional stress (high carbohydrate diet/high-fat diet) by fostering lipogenic stimulus [9]. The NF-κB pathway activation is significantly associated with poor prognostic traits as well as stemness characteristics, which places modulation of NF-κB signaling in the focus of therapeutic interventions [10]. Stress-related pathways, such as the p38 mitogen-activated protein kinase (MAPK) signaling cascades, have been shown to contribute to the NF-κB response [11]. Furthermore, NF-κB signaling has been shown to contribute to cancer progression by controlling epithelial-mesenchymal transition and metastasis [12,13]. Indeed, NF-κB is one of the most important upstream regulators of matrix metalloproteinases, including MMP-$\frac{2}{9}$, which play a key role in HCC invasion and metastasis [14,15]. Therefore, targeting the NF-κB signaling pathway may be a promising strategy for the therapeutic management of HCC and the improvement of patient prognosis. For years now, scientific research on marine natural products has been widespread, demonstrating how the sea can be instrumental in combating human diseases. Marine compounds have the potential for pharmacological activities, such as anticancer, antiviral, antioxidant, antimicrobial, anti-inflammatory, and many more [16,17]. The marine plant Posidonia oceanica (L.) Delile is the only member of the Posidoniaceae family that is endemic to the Mediterranean. P. oceanica leaves were used in folk medicine to treat a variety of human health issues [18], including skin problems and sore throats [19], irritation and inflammation, joint pain, and acne [20]. P. oceanica leaf decoction was also used to treat diabetes and hypertension [21]. For years, our research group has been studying the bioactive properties of a hydroalcoholic extract of P. oceanica leaves (POE) [18,22,23,24,25,26,27,28]. The phenolic composition of POE has been identified as $88\%$ and is predominantly represented by (+) catechins, and minimally by gallic acid, ferulic acid, epicatechin, and chlorogenic acid (Table 1) [22]. POE has been shown to possess antioxidant and anti-inflammatory [23,24], anti-glycation properties [25], and to inhibit cancer cell migration [22,26,27]. Therefore, given the biological properties of POE, here, the potential role of POE under high glucose-related stress conditions in a cellular model of HCC was studied. Several commonly used human HCC cell lines are reported in the literature [29]. However, HepG2 is the most widely used cell line and is generally considered a good model of liver cancer, including HCC [30]. Because metabolic disturbances due to high blood glucose levels can be risk factors for tumor progression, in this work, HepG2 human hepatoma cells were exposed to 25 mM D-Glucose (high glucose, HG), which is higher than physiological glucose (normal glucose, NG, 5 mM), to study HG-related stress, an experimental model already described in the literature [31,32,33]. Specifically, lipid accumulation, MAPKs/NF-κB axis activation status, and MMP-$\frac{2}{9}$ activity in HepG2 cells were evaluated under HG conditions. Considering the described bioactivities of POE, the potential ameliorative role of the phytocomplex against high glucose-related stress in HepG2 cells was then evaluated. ## 2.1. Extraction Yield from P. oceanica Leaves and Its Biochemical Properties The hydroalcoholic extraction protocol, previously described [26,34], was applied to 4 g of dried and minced leaves of P. oceanica. The total yield of P. oceanica dry extract was 45 mg. POE was constituted by resuspending 1.8 mg of dry extract in 0.5 mL of EtOH $70\%$ (v/v), yielding a hydrophilic analyte concentration of 3.6 mg/mL. The Folin–Ciocalteau assay was used to determine the total polyphenol (TP) content of POE. Its antioxidant and radical scavenging properties were analyzed by Ferric Reducing Antioxidant Power (FRAP) and DPPH colorimetric assays. TP content of POE was found to be 3.6 ± 0.3 mg/mL of gallic acid equivalents (GAE), while its antioxidant and radical scavenging activity were found to be 1.0 ± 0.2 and 10 ± 2.0 mg/mL of ascorbic acid equivalents (AAE), respectively. Data of POE biochemical characterization are reported as mean ± standard deviation (SD) of three independent experiments (Table 2). Accordingly, these results support the efficacy and reproducibility of hydroalcoholic extractions from P. oceanica leaves [26,27,34,35]. As in a previous work [30], the non-cytotoxic 7 µg GAE/mL dose of POE was used for subsequent cell-based experiments. ## 2.2. Effect of POE on Lipid Accumulation under High Glucose Condition in HepG2 Cells High levels of glucose in the bloodstream are known to cause cell toxicity, resulting in cellular damage and organ dysfunction [36]. However, in cancer cells, the enormous energy demand for rapid proliferation and expansion is mainly provided by glucose utilization. Indeed, aerobic glycolysis is a distinctive process in many cancers, including HCC, and regulates tumor progression [37,38]. To evaluate the effect of high glucose (HG) concentrations on the viability of HepG2 human hepatoma cells, cells were exposed to normal glucose (NG, 5 mM D-glucose) or high glucose (HG, 25 mM D-glucose) conditions for 24 h. However, as shown in Figure 1, the exposure for 24 h to HG had no significant effect on cell viability, which remained comparable to that of cells under NG conditions. In addition, the presence of POE (7 μg GAE/mL) did not affect cell viability under both NG and HG conditions (Figure 1). Lipid metabolism is now recognized as an important pathway in cancer. It may provide additional energy sources needed for metastasis, assembly blocks for proliferation, and act as secondary messengers in various signaling pathways [39]. The literature reports that high glucose levels result in increased intracellular lipid accumulation in HepG2 cells over 24 h [33]. To evaluate the effect of HG on intracellular lipid accumulation, HepG2 cells were exposed to HG for 24 h, and total neutral lipids were estimated by Oil Red O (ORO) staining (Figure 2A). As expected, high concentrations of glucose (25 mM) resulted in a significant increase in neutral lipid accumulation of approximately $35\%$ (134 ± $2\%$) in HepG2 cells compared with control cells exposed to physiological concentrations of glucose (5 mM) (Figure 2B). Hence, the ability of POE to prevent HG-induced lipid accumulation in HepG2 cells was evaluated. As shown in Figure 2A, the high levels of HG-induced intracellular neutral lipids were significantly reduced in the presence of POE (101 ± $6\%$), which is comparable to those of control cells under NG conditions (Figure 2B). These data demonstrated the ability of POE to inhibit lipid accumulation induced by high glucose concentrations. ## 2.3. Role of High Glucose on FASN Expression in HepG2 Cells and the Effect of POE The high aerobic metabolism in HCC is accompanied by an activated glycolytic flux resulting in increased metabolic intermediates. These intermediates can be used for the biosynthesis of macromolecules, including triglycerides and phospholipids, to meet the demands of rapid tumor growth [40]. When glucose enters the cell by specific transporters, it is converted to pyruvate by glycolysis and then to acetyl-CoA to enter the Krebs cycle. In the presence of excess glucose, citrate from the Krebs cycle is exported to the cytoplasm. Citrate is the main inducer of acetyl-CoA carboxylase activity that produces malonyl-CoA, the major intermediate in fatty acid synthesis. A key role during this process is played by fatty acid synthase (FASN), which consumes acetyl-CoA and malonyl-CoA by catalyzing the de novo synthesis of fatty acids [41]. FASN plays an essential role in the lipid metabolic pathway and can rewire tumor cells to have greater energy flexibility to meet their high energy demands. As a consequence, FASN plays a crucial role in the production of lipids in the liver, which can then be exported to metabolically active tissues or stored in adipose tissue [42]. Some cancer cells, including HCC cells, exhibit high FASN expression and promote the endogenous synthesis of fatty acids, providing energy for their proliferation [43]. Here, FASN expression levels were monitored in HepG2 cells during 24 h of HG exposure by Western blot analysis (Figure 3A). Under HG conditions, FASN expression levels increased progressively over time to an approximately twofold and significant increase (215 ± $56\%$) at 24 h exposure to HG treatment compared with NG treatment (Figure 3B). The expression levels of FASN in the intervals in the presence of physiological NG glucose (5 mM) did not change significantly over time (Figure S1 in Supplementary Materials). These data agree with evidence in the literature showing an increase in hepatic FASN protein levels in response to increased glucose [44,45]. Indeed, FASN expression has also been shown to increase dramatically both in the liver of patients with the most severe degree of nonalcoholic fatty liver disease (NAFLD) and in mice with NAFLD induced by a high-fat diet [45]. Pharmacological inhibition of FASN has been shown to be effective in various malignant cells in vitro and in vivo but not in normal cells, and this represents a therapeutic window of intervention [39]. Therefore, the effect of POE on FASN expression in HepG2 cells exposed to HG was investigated (Figure 3C). As shown in Figure 3D, FASN levels were significantly reduced in POE-treated HG cells by about $35\%$ (64 ± $15\%$) at 24 h of treatment compared with untreated HG cells, whereas POE showed no significant effect on FASN expression levels under physiological NG glucose conditions (Figure S2 in the Supplementary Materials). These results are in agreement with data from the ORO assay and suggest that POE attenuates lipid accumulation induced by high glucose levels through down-regulation of the HG-related de novo lipogenesis. ## 2.4. Effect of High Glucose on NF-κB and MAPKs Signaling Pathways Metabolic reprogramming critically influences cancer pathogenesis and progression. Lipid metabolism is an essential source of energy for cancer cells and plays an important role in microenvironment adaptation and cell signaling [4]. In this context, altered lipid metabolism could induce inflammation and promote fibrosis and support the progression of HCC [4,46]. Recent literature reports a correlation between hepatic de novo lipogenesis and NF-κB activation, particularly with a high-carbohydrate diet [9]. In addition, NF-κB signaling has been shown to be constantly activated in HCC tissues and cells [14]. In this work, the activation status of the NF-κB signaling pathway under HG conditions was assessed by monitoring the expression levels of phosphorylated NF-κB transcription factor (p-NF-κB) and its cytosolic inhibitor IκBα by Western blot analysis (Figure 4A). As shown in Figure 4B, in HG cells, the phosphorylation levels of NF-κB increased progressively over time to a significant increase of $80\%$ at 24 h of treatment (180 ± $9\%$) compared with control cells under NG conditions. The progressive increase in p-NF-κB levels was matched by a progressive decrease in IκBα levels (Figure 4C). HG treatment resulted in a significant reduction in IκBα levels by approximately $27\%$ from 16 h (72 ± $3\%$) compared with control cells under NG conditions. These data demonstrate that HG induces activation of the NF-κB signaling pathway, in agreement with the literature [47]. The expression levels of p-NF-κB/NF-κB and IκBα in the time intervals under NG conditions did not change significantly over time (Figure S3 in Supplementary Materials). Stress-related pathways, such as the MAPK (mitogen-activated protein kinase) signaling cascade, have been shown to contribute to the NF-κB response in the liver [10]. MAPK is composed of extracellular signal-regulated kinases (ERKs), c-Jun NH2-terminal kinases, and p38-MAPK (p38), contributing to inflammation, cell survival, and natural cell death [48]. The literature reports that several oncogenic kinases, including p38 and extracellular signal-regulated kinase (ERK) participate in the activation of NF-κB, contributing to the progression of HCC [10,14,49]. In light of these considerations, activation of the p38 MAPK signaling cascade in HepG2 cells under HG conditions was evaluated here by monitoring up to 24 h phosphorylation of some components of cell signaling, namely p-p38 and p-ERK$\frac{1}{2}$, by Western blot analysis (Figure 4A). Figure 4D shows a progressive increase in p-p38 levels over time. At 16 h of HG exposure, p-p38 levels were significantly increased by about $77\%$ (177 ± $0.8\%$) compared with those of NG control cells, to reach an increase of about $127\%$ (227 ± $18\%$) at 24 h. Comparably, HG resulted in a progressive increase in p-ERK$\frac{1}{2}$ levels that were significantly higher (148 ± $11\%$) than those of control cells in NG at 24 h of treatment. The expression levels of p-p38/p38 and p-ERK$\frac{1}{2}$/ERK$\frac{1}{2}$ in the time intervals under NG conditions did not change significantly over time (Figure S3 in Supplementary Materials). Overall, these results indicate that HG stimulates phosphorylation of p38 and ERK$\frac{1}{2}$ in HepG2 cells, in agreement with the literature [31], suggesting the involvement of the MAPK signaling cascade in the NF-κB signaling response induced by high glucose levels. ## 2.5. Effect of POE on High Glucose-Induced NF-κB and MAPKs Signaling Pathways in HepG2 Cells Because NF-κB activation plays a central role in cancer development, the NF-κB signaling pathway has been recognized as a potential therapeutic target in cancer. Some experimental evidence has demonstrated the ability of inhibitors of the MAPKs/NF-κB axis to block HCC progression [50,51]. Other evidence suggests that suppression of NF-κB/p65 gene transcription makes HepG2 cells chemosensitive [52] and that pharmacological inhibition of NF-κB attenuated hepatic lipid accumulation both in vitro and in vivo in response to a high carbohydrate diet [9]. In this work, the effect of POE on the activation status of NF-κB and MAPK signaling pathways induced by HG was evaluated by Western blot analysis (Figure 5A). As shown in Figure 5B, POE induced a slight but significant decrease in HG cells of about $30\%$ in p-NF-κB levels as early as 16 h of treatment (70 ± $14\%$) compared with untreated HG cells. This inhibitory effect of POE was intensified at 24 h of treatment when p-NF-κB levels in HG-treated cells were about $40\%$ lower (62 ± $8\%$) than in untreated HG cells. The POE-induced inhibition of NF-κB activation was confirmed by the approximately $150\%$ increase in IκBα levels (247 ± $49\%$) observed at 7 h of treatment in HG cells compared with untreated HG cells (Figure 5C). This suggests that POE prevents degradation of cytosolic IκBα at early times and that at later times, it prevents phosphorylation, and thus, activation of NF-κB, following a precise temporal pattern. In addition to NF-κB signaling, POE showed an influence on the MAPKs cascade. Specifically, as depicted in Figure 6D,E, both p-p38 and p-ERK$\frac{1}{2}$ were slightly, but significantly, reduced at 16 h of POE treatment in HG cells (77 ± $7\%$ and 87 ± $1\%$, respectively) compared with untreated HG control cells. The inhibitory effect of POE on the activation of p38 and ERK$\frac{1}{2}$ was significantly more pronounced at 24 h of POE treatment in HG cells, when p-p38 and p-ERK$\frac{1}{2}$ were reduced by approximately $50\%$ (45 ± $19\%$ and 39 ± $1\%$, respectively) compared with untreated HG cells. The expression levels of these protein targets under NG conditions in the presence of POE did not change significantly over time (Figure S4 in Supplementary Materials). These findings are in line with the role of POE on the NF-κB signaling pathway previously demonstrated in lipopolysaccharide-stimulated murine macrophages [23]. This study is the first experimental evidence that POE is able to block HG-induced lipid accumulation in HepG2 cells by acting on the MAPKs/NF-κB axis. Several phytochemicals, particularly polyphenols, exert anticancer properties by targeting and inhibiting NF-κB and MAPK signaling pathways [53]. The action of POE could thus be attributed to the synergistic action of its phenolic constituents. ## 2.6. Effect of POE on MMP-2/9 Activity in High Glucose Conditions in HepG2 Cells HCC is one of the most lethal cancers, mainly because of its high tendency to metastasize. Metalloproteinases (MMPs) are key enzymes involved in extracellular matrix degradation. In particular, high levels of gelatinase MMP-2 and MMP-9 are known to correlate with invasion, metastasis, and poor prognosis in various types of cancer, including HCC [15]. In several human cancers, NF-κB has been observed to be one of the most important upstream regulators of MMPs, thus playing a key role in cancer development and progression [54]. Given the inhibitory role of POE on the NF-κB signaling pathway described above, the activity of MMP-$\frac{2}{9}$ was examined by gelatin zymography assay on the culture media of cells exposed to NG or HG in the absence or presence of POE for 24 h. Notably, POE resulted in a marked reduction of MMP-$\frac{2}{9}$ activity after 24 h of treatment of HepG2 cells in both NG and HG conditions (Figure 6A). Specifically, MMP-9 activity was reduced by $65\%$ (35 ± $6\%$) and $70\%$ (29 ± $7\%$) in POE-treated cells compared with untreated NG and HG control cells, respectively (Figure 6B), whereas POE treatment reduced MMP-2 activity by approximately $40\%$ (58 ± $2\%$) and $55\%$ (44 ± $5\%$) in HepG2 cells compared with untreated NG and HG control cells, respectively (Figure 6C). This evidence can be traced back to the ability of POE to inhibit the NF-κB signaling pathway. In fact, as described in the literature, blocking NF-κB signaling results in reduced invasiveness of HCC cells and reduced expression of invasion-related molecules, including MMP-$\frac{2}{9}$ [14,55]. Figure 6: Effect of POE on the activity of MMP-2/9 released in culture medium. (A) Representative image of gelatin zymography of cell culture media collected at 24 h from HepG2 cells untreated (−) or treated with POE (7 μg GAE/mL) under normal glucose (NG, 5 mM D-glucose) and high glucose (HG, 25 mM D-glucose) conditions. Culture medium of untreated HT1080 human fibrosarcoma cells was used as a control for the molecular size of MMPs (Control). Quantitative data of gelatinolytic bands of (B) MMP-9 and (C) MMP-2. Data are reported as means ± SD from different experiments. Values are given as percentages compared with untreated control cells under NG conditions. * p < 0.01 vs. NG control cells; °° p < 0.01, °°° p < 0.001 vs. HG control cells. Tukey’s HSD test.* ## 3.1. Materials and Reagents Dulbecco’s Modified Eagle’s Medium (DMEM, with high or low glucose), fetal bovine serum (FBS), L-glutamine, penicillin and streptomycin, 1-(4,5-dimethylthiazol-2-yl)-3,5-diphenyl formazan (MTT), 2,2-diphenyl-1-picrylhydrazyl (DPPH), 3-(2-pyridyl)-5,6-diphenyl-1,2,4-triazine-p,p′-disulfonic acid hydrate (Ferrozine®), Folin-Ciocalteau reagent, gallic acid, ascorbic acid, Oil red O solution, Coomassie Brilliant Blue G-250, gelatin, and all chemicals and solvents were purchased from Merck KGaA (Darmstadt, DA, Germany). Electrophoresis reagents were purchased from Bio-Rad Laboratories (Hercules, CA, USA). Primary antibodies were supplied by Cell Signaling Technology (Beverly, MA, USA), Molecular ProbesTM (Invitrogen, Carlsbad, CA, USA), and Santa Cruz (Heidelberg, Germany) (Table 3). HRP-linked anti-mouse IgG and anti-rabbit IgG secondary antibodies were obtained from Molecular ProbesTM (Invitrogen, Carlsbad, CA, USA). Disposable plastic was provided by Sarstedt (Milan, Italy). ## 3.2. Hydroalcoholic Extract from Leaves of P. oceanica: Preparation and Biochemical Characterization Fresh leaves of P. oceanica L. Delile were collected in July and washed thoroughly with double-distilled water to remove surface epiphytes. The hydrophilic component was recovered according to the previously described protocol [26]. Briefly, 1 g of dried leaves of P. oceanica were crushed and suspended overnight in 10 mL of EtOH/H2O (70:30 v/v) at 37 °C under stirring and then at 65 °C for 3 h. The debris was then removed by centrifugation at 2000× g, and the recovered supernatant was mixed with n-hexane in a 1:1 ratio. The hydrophilic component of the extract was then recovered after repeated agitation in a separatory funnel, dispensed into 1 mL aliquots, and then dried using a UnivapoTM vacuum concentrator. An aliquot of P. oceanica leaf extract containing 1.8 mg of dry extract was dissolved in 0.5 mL of EtOH/H2O (70:30 v/v) before use and hereafter referred to as POE. POE was then characterized for total polyphenol content (TP) by the Folin–Ciocalteau colorimetric method, as previously described [22,34]. Briefly, a solution of Folin–Ciocalteu’s phenol reagent (diluted 1:10 in distilled water) was added to scalar volumes of POE (final volume 20 µL) in a 96-well plate. After 5 min at room temperature, 80 µL of $7.5\%$ sodium carbonate solution was added per well and incubated for 2 h. Gallic acid (0.5 mg/mL) was used as a reference in the range 0–10 µg to determine TP values. The absorption values were recorded with a microplate reader at 595 nm. In addition, the antioxidant and radical-scavenging activities of POE were studied using the ferric-reductive/antioxidant power (FRAP) assay and the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, respectively [22,34]. Briefly, for FRAP assay, distilled water was added to graduated volumes of POE to a final volume of 50, and 200 µL of Ferrozine™ reagent (10 mM Ferrozine™ in 40 mM HCl:20 mM ferric chloride:0.03 M acetate buffer pH 3.6 ratio 1:1:10) were added to each well of a 96-well plate. Ascorbic acid (0.1 mg/mL) was used as a reference in the range of 0–2.5 µg to evaluate POE antioxidant activity. The absorption values were measured at 595 nm at room temperature with a microplate reader. For DPPH assay, scalar volumes of POE were diluted with methanol (final volume 100 µL) in a 96-well microplate and then mixed with 100 µL of freshly prepared DPPH solution (0.25 mg/mL in methanol). After 30 min incubation in the dark at room temperature, the absorbance was read at 490 nm with a microplate reader. Ascorbic acid (0.5 mg/mL) was used as a reference in the range of 0–15 µg to evaluate the radical scavenging activity of POE. The TP of POE was expressed as milligrams of gallic acid equivalent (GAE) per milliliter of POE, whereas the antioxidant and radical-scavenging activities were expressed as milligrams of ascorbic acid equivalent (AAE) per milliliter of POE. POE biochemical characterization results were obtained by plotting the POE absorption values on the respective standard curves of the colorimetric assays. The determinations were repeated in triplicate. Values are reported as mean ± standard deviation (SD). ## 3.3. Cell Line and Experimental Conditions American Type Culture Collection (ATCC®) provided the human HepG2 hepatoma cell line. Cells were grown in a humidified atmosphere with $5\%$ CO2 at 37 °C in DMEM supplemented with $10\%$ FBS, 100 µg/mL streptomycin, 100 U/mL penicillin, and 2 mM L-glutamine (complete medium), with a physiological glucose content (5 mM). Trypsin ($0.25\%$ trypsin, 0.5 mM EDTA) was used to detach cells at $90\%$ confluence. The following in vitro cell-based experiments were conducted in complete medium under normal glucose (NG, 5 mM D-glucose) or high glucose (HG, 25 mM D-Glucose) conditions in the absence or presence of POE (7 µg GAE/mL). ## 3.4. Cell Viability The MTT colorimetric assay was used to examine the viability of HepG2 cells. Cells were cultured in 96-well plates (3 × 104 cells/well) for 24 h under NG condition. Subsequently, cells were exposed to NG or HG conditions in the absence or presence of POE (7 µg GAE/mL) for 24 h. The culture medium was then removed and 100 µL of MTT solution (0.5 mg/mL) was added to each well. After incubation in the dark at 37 °C for 1 h, the insoluble formazan crystals were dissolved in 100 µL/well of dimethyl sulfoxide. An iMARK microplate reader (Bio-Rad Laboratories, USA) was used to measure absorbance values at 595 nm. Data were expressed as percentages compared with untreated control cells under NG condition. ## 3.5. Determination of Intracellular Neutral Lipids by Oil Red O (ORO) Assay HepG2 cells were seeded (6 × 104 cells/well) in 24-well plates overnight and then exposed to NG or HG conditions in the absence or presence of POE (7 µg GAE/mL) for 24 h. After washing with PBS, the cells were fixed in $2\%$ (v/v) paraformaldehyde for 10 min. The fixed cells must be allowed to dry completely after two washes with PBS. For 30 min at 37 °C, neutral lipids were stained with 200 µL/well of ORO working solution ($60\%$ in distilled water). Excess dye was washed with distilled water until the water no longer showed a visible pink color. After complete drying of the wells, the stained lipid droplets in the cells were examined and photographed with a Nikon TS-100 microscope equipped with a digital acquisition system (Nikon Digital Sight DS Fi-1; Nikon, Minato-ku, Tokyo, Japan). Finally, cellular lipid accumulation was measured by adding 200 µL/well of isopropanol. Absorption was measured at 490 nm using an iMARK microplate reader (Bio-Rad Laboratories, USA) [34]. ## 3.6. Western Blot Assay HepG2 cells (15 × 104 cells/well) were cultured in 6-well plates for 24 h. The cells were then exposed to NG and HG conditions in the absence or presence of POE (7 µg GAE/mL) for 7, 16, and 24 h. A Laemmli buffer solution containing Tris-HCl (62.5 mM, pH 6.8), $10\%$ (w/v) SDS, and $25\%$ (w/v) glycerol was used to lyse the cells. Lysates were centrifuged at 4 °C for 1 min at 12,000× g. The total protein concentration of each sample was determined using the BCA protein assay. Then, 30 µg protein from each sample was mixed with $5\%$ (v/v) β-mercaptoethanol and bromophenol blue and heated at 95 °C for 5 min. Protein samples were electrophoretically separated on $12\%$ or $15\%$ SDS-polyacrylamide gels and blotted onto PVDF membranes (0.45 µm). After a saturation step with a BSA blocking buffer [$5\%$ (w/v) BSA in $0.1\%$ (v/v) PBS-Tween®-20], the membranes were incubated overnight at 4 °C with primary antibodies appropriately diluted in the blocking buffer. The primary antibodies used are listed in Table 3. After three washes in $0.1\%$ (v/v) PBS-Tween®-20 solution, HRP-linked secondary antibodies of goat anti-rabbit IgG (1:10,000) or goat anti-mouse IgG (1:10,000) (Invitrogen, Waltham, MA, USA) were added to each membrane for 1 h at room temperature. After three washes in $0.5\%$ (v/v) PBS-Tween®-20, Clarity Western ECL solution was used to detect protein bands using the AmershamTM 600 Imager imaging system (GE Healthcare Life Science, Pittsburgh, PA, USA). Quantity One (version 4.6.6, Bio-Rad) was used as the instrument for densitometric analysis of protein bands. ## 3.7. Assessment of MMP-2/9 by Gelatin Zymography Gelatin zymography was used to assess MMP-2 and MMP-9 metalloproteinase (or gelatinase) activity [22,27]. In 24-well plates, cells were seeded at a density of 2×105 cells/well and incubated overnight. Cells were then treated with POE (7 µg GAE/mL) in DMEM medium supplemented with heat-inactivated serum (at 55 °C for 30 min) with high glucose (HG, 25 mM D-Glucose) for 24 h. As controls, untreated HG cells were used. For pellet cell debris, culture supernatants were collected and centrifuged at 9700× g for 1 min at 4 °C. The conditioned medium (2.5 µL) was then separated in an $8\%$ polyacrylamide gel containing gelatin (1 mg/mL) under nonreducing conditions. To remove SDS, the gel was washed twice (30 min/each time) in $2.5\%$ (v/v) Triton X-100 before being incubated at room temperature for 30 min in reaction buffer (50 mM Tris-HCl pH 7.4, 0.2 M NaCl, 5 mM CaCl2, 1 mM ZnCl2). Overnight incubation was performed in the reaction buffer. Then, the gel was incubated for 1h at room temperature with a solution containing $40\%$ (v/v) methanol and $10\%$ (v/v) acetic acid. Following two washes in double-distilled water (10 min each), staining was done with colloidal Coomassie Brillant Blue G-250 ($0.05\%$) dissolved in $1.6\%$ (v/v) phosphoric acid, $8\%$ (w/v) ammonium sulfate, and $20\%$ (v/v) methanol. Gelatinase activities were clear after stain removal, with $1\%$ (v/v) acetic acid appearing as clear bands on a blue background. A digital scanner was used to acquire zymography images. ## 3.8. Statistical Analysis Unless otherwise indicated, data are expressed as mean ± standard deviation (SD) of independent experiments. For cell viability (MTT assay), intracellular lipid accumulation (ORO assay), and gelatinase activity (gelatin zymography) experiments, signals acquired from independent experiments were normalized by centering the mean (i.e., each replicate measurement was divided by the mean of the triplicates in order to compensate for experimental batch fluctuations), and differences were assessed by one-way ANOVA followed by the post-hoc Tukey’s HSD test. For Western blotting analysis, differences between normalized intensity signals were evaluated by Kruskall–Wallis test followed by Conover’s post hoc test. Statistical differences were defined at p ≤ 0.05. ## 4. Conclusions There are good reasons to state that high glucose levels may have a pro-tumor role. High glucose activates various signaling pathways that cooperate in controlling the behavior of cancer cells contributing to its development and progression. Here, it was demonstrated that the polyphenol-rich leaf extract of P. oceanica (POE) prevents intracellular lipid accumulation and blocks the MAPKs/NF-κB axis, and consequently reduces MMP-$\frac{2}{9}$ in HG-exposed HepG2 cells, used as an in vitro model of HCC. Altered lipid metabolism may affect NF-κB signaling pathway, promoting inflammation and fibrosis and supporting HCC progression. Targeting glucose-induced de novo lipogenesis and NF-κB activity could therefore prove to be an interesting therapeutic target in the prevention and management of HCC. In light of POE ability to reduce de novo lipogenesis and regulate the NF-κB signaling pathway, we suggest the marine plant P. oceanica as a potential dual weapon against HCC progression. To date, some inhibitors of the NF-κB pathway are in various stages of clinical trials [56]. 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--- title: 'Arsenite Exposure to Human RPCs (HRTPT) Produces a Reversible Epithelial Mesenchymal Transition (EMT): In-Vitro and In-Silico Study' authors: - Sonalika Singhal - Scott H. Garrett - Seema Somji - Kalli Schaefer - Benu Bansal - Jappreet Singh Gill - Sandeep K. Singhal - Donald A. Sens journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048886 doi: 10.3390/ijms24065092 license: CC BY 4.0 --- # Arsenite Exposure to Human RPCs (HRTPT) Produces a Reversible Epithelial Mesenchymal Transition (EMT): In-Vitro and In-Silico Study ## Abstract The human kidney is known to possess renal progenitor cells (RPCs) that can assist in the repair of acute tubular injury. The RPCs are sparsely located as single cells throughout the kidney. We recently generated an immortalized human renal progenitor cell line (HRTPT) that co-expresses PROM1/CD24 and expresses features expected on RPCs. This included the ability to form nephrospheres, differentiate on the surface of Matrigel, and undergo adipogenic, neurogenic, and osteogenic differentiation. These cells were used in the present study to determine how the cells would respond when exposed to nephrotoxin. Inorganic arsenite (iAs) was chosen as the nephrotoxin since the kidney is susceptible to this toxin and there is evidence of its involvement in renal disease. Gene expression profiles when the cells were exposed to iAs for 3, 8, and 10 passages (subcultured at 1:3 ratio) identified a shift from the control unexposed cells. The cells exposed to iAs for eight passages were then referred with growth media containing no iAs and within two passages the cells returned to an epithelial morphology with strong agreement in differential gene expression between control and cells recovered from iAs exposure. Results show within three serial passages of the cells exposed to iAs there was a shift in morphology from an epithelial to a mesenchymal phenotype. EMT was suggested based on an increase in known mesenchymal markers. We found RPCs can undergo EMT when exposed to a nephrotoxin and undergo MET when the agent is removed from the growth media. ## 1. Introduction The tubular epithelium of the human kidney has the capacity to regenerate, repair, and re-epithelialize in response to injury by various insults. In the human kidney, a population of resident cells with progenitor characteristics, identified by the PROM1 stem cell marker, were localized to the Bowman’s capsule, proximal tubules, and the inner medullary papilla [1,2,3]. The number of cortical PROM1-expressing tubular cells increased in patients with acute renal injury [4]. Further studies have shown renal epithelial cells co-expressing PROM1 and CD24 have the capacity to participate in the regeneration of renal tubule cells [5,6,7,8,9]. Cultures of human renal epithelial cells that co-express PROM1 and CD24 also display features expected of RPCs; such as spheroid formation, ability to undergo adipogenic, neurogenic, osteogenic differentiation, and form tubule-like structures on Matrigel. These cells provided a potential model to define the mechanisms underlying the progenitor cell’s ability to participate in renal epithelial cell regeneration. However, these cultures were shown to possess two cell types, one co-expressing PROM1 and CD24 and another expressing only CD24 [10]. Subsequently, our laboratory identified an immortalized human renal proximal tubule epithelial cell line, RPTEC/TERT1, that also displays the two cell populations, one that cell sorting was used to isolate two new immortalized cell lines, one HRTPT that co-expresses PROM1 and CD24, and another, HRECT24T that expresses CD24 and no PROM1 [11]. The HRTPT cells expressed the features defined for RPCs while the HRECT24T cells displayed no features of RPCs [11,12,13]. The HRTPT cells provide a human cell culture model to determine if PROM1/CD24 co-expressing RPCs are susceptible to nephrotoxic agents. To the author’s knowledge, this is an unexplored area as regards RPCs. Exposure of the HRTPR cells to inorganic arsenic (iAs) was chosen to test this hypothesis. Inorganic arsenic has an extensive distribution in the environment [14,15,16]. The kidney is the most susceptible of all organ systems to iAs exposure [17,18]. There is evidence that exposure to iAs is associated with renal disease. A study of 6093 participants from arseniasis-endemic areas in northeastern Taiwan showed a temporal relationship between arsenic concentrations ≥ 10 mg/L in drinking water and CKD (chronic kidney disease) [19]. The study also demonstrated a dose-dependent association between well-water arsenic concentration and kidney diseases. Other studies have also shown an association of iAs exposure with alterations in renal function and disease [20,21,22]. Thus, there is evidence from population studies that exposure to iAs is associated with renal disease, however, studies defining the concentration of iAs within the human kidney and specific cells of the nephron are rare. Accumulation is possible due to the presence of metallothionein (MT), a small molecular weight protein that is known to bind and sequester iAs within cells [23,24,25]. ## 2.1. EMT as a Function of Exposure of HRTPT Cells to iAs Examination of the HRTPT cells exposed to iAs by light microscopy demonstrated a change in cell morphology at passage 3 when compared to cells unexposed to iAs (Figure 1A,B). When compared to the control, the iAs exposed cells were less closely packed, more disorganized, and had lost the ability to form “domes”. Domes are raised areas of the monolayer due to fluid accumulation and are a manifestation of vectorial active ion transport [10]. This change in morphology was evident for at least seven more serial passages (Figure 1C). The change in morphology suggested that the cells could have undergone an epithelial-to-mesenchymal transition (EMT). Further evidence for the possibility of EMT was provided in passage 8 by increased expression of ACTA2, TAGLN, VIM, and CDH2 and a modest decrease in expression of CDH1, (Figure 1D–H). The co-expression of PROM1 and CD24 mRNA was retained by the cells exposed to iAs but the expression was clearly reduced (Figure 1I–K). The change in morphology and gene expression by the iAs−exposed cells suggested global gene expression technology might assist in providing additional information if iAs were inducing EMT or a related mesenchymal alteration in the HRTPT cells. Lower concentrations of iAs (1.0 and 2.0 μM) elicited a similar shift in morphology and increased expression but at an extended number of serial passages (Supplementary Figure S1). The HRTPT cells exposed to iAs were assessed for their morphology and expression of the above genes when iAs was removed from the growth media. Light microscopic examination showed that by the 2nd passage the iAs− cells displayed a morphology similar to the HRTPT controls (Figure 2A,B) and by the 11th passage they were indistinguishable from the control (Figure 2C). The iAs− cells regained dome formation at both P2 and P11 following iAs removal from the growth media. The expression of the ACTA2, TAGLN, VIM, N-cdh, and E-cdh genes were also assessed and all except VIM, which was absent from the iAs−cells, showed a trend to return to control values (Figure 2D–H). The change in morphology and gene expression after the removal of iAs suggested that the cells might have undergone a mesenchymal-to-epithelial transition (MET). These results presented the opportunity to examine the global gene expression profile of a toxin-exposed renal progenitor cell that shows evidence of undergoing EMT and, upon toxin removal, the ability to undergo MET and return to an epithelial morphology. The ability of the iAs− HRTPT cells to dome is strong evidence of epithelial differentiation. ## 2.2. Global Gene Expression and Impacted Pathway Analysis The above morphology and gene expression changes suggested that exposure of HRTPT cells to iAs induced EMT, and when iAs was removed, MET back to the morphology and gene expression of the control HRTPT cells. Global gene expression was employed to further explore the ability of HRTPT cells to undergo EMT and MET as a function of exposure to iAs. In this section, triplicates of all the samples, i.e., control cells (P0), iAs exposed cells at P3, P8, and P10 and P8 cells and iAs recovered P2, P11 included to determine the global distribution and to identify all possible relationships among different conditions. The first two components of the PCA plot, PC1, and PC2, carry $66.1\%$ and $13.4\%$ of the variance of the data and the P0 is far removed from P10 and P8 as compared to P2 and P11 (Figure 3A). Correlation analysis supported this relationship and demonstrated that P2 samples were most closely related to the P0 (Figure 3C). Differential gene expression analysis using Post hoc test with ANOVA identified 2478 probes varieties across all possible conditions (Supplementary Table S1). The hierarchical clustering of the top 100 differentially expressed genes was determined from these 2478 probes (Figure 3B). IPA was performed using the 2478 probe varieties across all possible conditions which identified hepatocellular carcinoma as the top hepatotoxicity function and renal damage as the top nephrotoxicity function (Figure 3D). GSEA analysis on 2478 probes identified thirty-six [36] upregulated pathways, and 368 downregulated pathways with nominalized p-value < 0.05 (Supplementary Table S2). ## 2.3. Gene Expression of HRTPT Cells Exposed to iAs Global gene expression was performed between P0 versus P3, P8, and P10 (each group separately) cells and identified 247, 363, and 304 differentially expressed genes, respectively (Supplementary Table S3). For the three sets of differentially expressed genes, $\frac{106}{247}$ genes were down-regulated and $\frac{141}{247}$ up-regulated; $\frac{118}{363}$ were down-regulated and $\frac{245}{363}$ up-regulated; and $\frac{111}{304}$ genes were down-regulated and $\frac{193}{304}$ were up-regulated in expression (Supplementary Table S3). An intersection analysis of the three gene sets for commonality identified 167 common genes with 91 genes being up- and 76 genes down-regulated (Supplementary Table S3, Figure 4A). When analyzing all the exposed samples together, i.e., iAs+ with respect to the P0, the first two components of PCA show $57.8\%$ and $22.2\%$ of the variance, respectively (Figure 4B) among the phenotypes. The variation between P8 and P10 is very narrow as compared to the P3 samples. A total of 280 probes (234 gene symbols) were found differentially expressed between these conditions (Supplementary Table S4) and a subset of the top 25 genes was determined from these differentially expressed genes (Figure 4C). A volcano plot shows the most significant upregulated and downregulated differentially expressed genes (Figure 4D) between iAs+ and P0. ## 2.4. Pathway Analysis of HRTPT Cells Exposed to iAs GSEA was performed on the 167 gene-set selected as an interaction of significant genes identified from P0 versus each of the iAs exposed cells passage (i.e., P3, P8, P10). We found 37 upregulated, and 129 downregulated pathways that had a nominalized p-value < 0.05 (Supplemental Table S5). Again, 167 common gene set was also analyzed using Reactome and we found the down-regulate pathways were associated with signaling pathways, especially those associated with FGF (Supplemental Table S6). Other associated pathways such as PI3K, the RAF/MAP kinase cascade, and ERBB were also identified using Reactome. The analysis of the 76 down-regulated gene set using Reactome also identified IGF signaling as a pathway. The prominent pathways associated with 91 up-regulated gene sets were interleukin signaling (IL4, IL10, IL13, IL18) and chemokine receptors (Supplemental Table S6). An analysis of the 167 gene set by the Panther Classification System also identified signaling pathways as a prominent component (Supplemental Table S6). In addition, we performed pathway analysis on a total of 280 probes (234 gene-symbols, Supplemental Table S4) were found differentially expressed between P0 and iAs+ conditions ($p \leq 0.05$ and FC < 0.5 or >2) (Supplementary Table S4). Of the 234 genes, we found 151 were upregulated pathways and 34 downregulated pathways with nominalized p-value < 0.05 (Supplemental Table S7). One of the upregulated pathways was the Hallmark Epithelial Mesenchymal Transition, a gene set with genes defining the epithelial-mesenchymal transition [26]. IPA analysis on the above 234 genes identified other significant pathways associated with exposure to iAs (Supplemental Table S8). A total of 533 pathways were demonstrated in this list out of which around 200 were under the $p \leq 0.05.$ EIF2, Ferroptosis, and mTOR signaling were at the top of the list. ## 2.5. Progenitor Cell Properties of HRTPT Cells after Recovery from Exposure to iAs The HRTPT cells recovered from iAs exposure were shown to retain the ability to differentiate, form nephrospheres and express PROM1 and CD24 in over $94\%$ of the cells (Figure 5A–F). One noteworthy alteration in tubular differentiation was that the recovered cells demonstrated no significant change in the expression of aquaporin from control cells, but did exhibit a large increase in the expression of calbindin (Figure 5G,H). The osteogenic gene RUNX2 and neurogenic gene ENO2 showed a significant increase (Figure 5I,J); while neurogenic genes MAPT and NES showed no significant change in expression (Figure 5K,L) and adipogenic gene, PPARG showed a decrease in expression when compared to the control HRTPT cells (Figure 5M). The confocal images show the expression of AP, AQP1, and THP as the tubulogenic marker (Figure 5N–P); FN1 and CD10 as osteogenic markers (Figure 5Q,R); NF, β-tub, and GFAP as neurogenic markers (Figure 5S–U); and PPARγ and ADIPOQ as adipogenic markers (Figure 5V,W) expression in recovered cells. ## 2.6. Gene Expression Analysis of HRTPT Cells after Recovery from iAs Exposure The HRTPT cells were assessed for their gene expression at the P2 and P11 passage following the removal of iAs. A comparison between the control HRTPT (P0) cells and the P2 cells demonstrated that 166 genes were differentially expressed between the two groups, with 30 upregulated and 136 down-regulated genes (Supplemental Table S9). A similar comparison between P0 and P11 cells demonstrated that 71 genes were differentially expressed with 39 up-regulated and 32 down-regulated (Supplemental Table S9). The common genes between the two gene sets were determined and 36 genes were common (Supplemental Table S9, Figure 6A), with 22 up and 11 down-regulated genes (Figure 6B,C). Three genes were found to differ in directionality between the P2 and P11, IGFBP3, NMNAT2, and CYFIP2 (Figure 6D). PCA found significant separation between the two recovered samples with PC1-$74.1\%$, as compared to the control with PC2-$18.9\%$ (Figure 6E). Differential gene expression identified 77 probes significantly different between the recovered cells versus the control (Supplementary Table S10). The heat map shows the top 25 differentially expressed genes along with a volcano plot of the results (Figure 6F,G). The gene expression analysis of control versus iAs recovered samples (iAs−) identified 426 probes that were differently expressed with $p \leq 0.05$ (Supplementary Table S11). ## 2.7. Pathway Analysis of HRTPT Cells Following Recovery from iAs Exposure 36 common genes were examined using Reactome and Panther databases. The Reactome database identified elastic fibers and pathways involved in the cell cycle and p53 interactions (Supplementary Table S12). The Panther database identified mostly signaling and regulatory processes. GSEA on 49 genes identified as differentially expressed (Supplementary Table S10) between the control and iAs−. Only two down-regulated pathways were identified at nominalized p-value < 0.05 (Supplementary Table S13). IPA on differentially expressed genes between iAs− vs. control (Supplementary Table S10), confirm p53 signaling as a canonical pathway (Figure 7, Supplementary Table S14). ## 2.8. Comparison of iAs Exposed HRTPT Cells and HRTPT Cells Following Recovery from iAs Exposure An intersection of differentially expressed genes between iAs exposed HRTPT cells and HRTPT cells following recovery from iAs exposure found 9-genes of interest (Figure 8A). These genes included CLDN16, CTSE, PTH1R, CYFIP2, SCD5, LIX1, MFAP5, KCP, and SH2D1B. PC1 and PC2 were showing $51.8\%$ and $27\%$ variance in the data, respectively (Figure 8B). A total of 305 differentially expressed probes (280 gene-symbols) were identified between P0, P3, P8, P10 and P0, P2, P11 with 41 down-regulated and 264 up-regulated genes (Figure 8D, Supplementary Table S15). GSEA on the 280 genes (Supplementary Table S15) found 42 downregulated pathways, and 157 upregulated pathways with nominal p-value < 0.05 (Supplementary Table S16). IPA identified FGFR as a significant upstream regulator (Figure 9, Supplementary Table S17). ## 3. Discussion The HRTPT cell line provides an opportunity to determine how a human renal progenitor cell responds to a nephrotoxic agent and its subsequent removal. The hypothesis is that nephrotoxin might alter the regenerative capacity of the RPCs to repair tubular damage. The results demonstrated that the HRTPT cells exposed to 4.5 µM iAs displayed those characteristics of a cell undergoing EMT as noted by a change to a mesenchymal morphology and an increase in expression of mesenchymal markers such as ACTA2 and TAGLN. It was also shown that the alteration in morphology and increased expression of smooth muscle actin alpha 2 and transgelin also occurred at lower levels of iAs exposure (1.0 and 2.0 µM), albeit at much longer times of exposure, providing evidence that results found with 4.5 µM iAs would translate to lower levels of exposure. Global gene expression was used to further analyze the EMT response when the HRTPT cells were exposed to iAs. GSEA of the common genes expressed from the comparison of P3, P8, and P10 compared to control identified the Hallmark Epithelial Mesenchymal Transition from the MSigDB as an upregulated pathway [26]. The global differently expressed gene set was examined to determine if the iAs treated cells were transitioned to myoepithelial (keratin expressing) or myofibroblast-like (vim expressing) cells. The common gene set did not show the differential expression of any keratin genes or the vimentin gene. To further explore this finding, the P3, P8, and P10 were examined separately for keratin and vimentin expression. This confirmed that vimentin was not identified as differentially expressed for any of the three passages. In contrast, KRT18 was increased in expression when P3 and P8, but not P10, were compared to the control. This provides evidence that the transition favors the myoepithelial cell. KRT18 has been noted to increase during tubular injury and approximately 20-fold in the early stage following human renal transplantation. [ 27,28] To the authors’ knowledge this is the first observation that a human RPC can undergo EMT. Pathway analysis for the 167 gene set identified signaling pathways associated with FGFR2 and chemokine receptors and chemokines as those having strong significance. An analysis of the down-regulated 76 gene set identified strongly with signaling related to the FGFR2 pathway, while the 91 up-regulated gene set was more strongly related to chemokine receptors, chemokines, and related pathways. An interesting feature of the down-regulated 76 gene set is that it was identical for all three time points of iAs exposure. The 76 gene set included FGF 9 and FGF 13 in addition to the FGFR2 receptor. The FGFR2 receptor has been shown to protect against tubular cell death and acute kidney injury involving ERK$\frac{1}{2}$ signaling in models of renal ischemia and reperfusion [29,30]. The expression of FGF9 has been shown to maintain the stemness of renal progenitor/stem cells during renal development [31]. FGF9 also has an essential role in the development of mesenchymal components in cells and tissues [9,32,33]. The FGF13 is elevated in ischemia/reperfusion in concert with the FGFR2 receptor [29]. The FGF18 gene, the only up-regulated FGF gene in the 167 gene set, has seen only limited study in the kidney but has been shown to have increased expression in cisplatin-induced murine AKI36. In breast cancer, FGF18 has been shown to be involved in both cell migration and EMT [34]. Despite these findings, the individual components of the FGF pathway have seen a limited study in renal disease as it relates to agent-induced changes in EMT and MET recovery from those changes. Arguing against any cause-and-effect relationship between the FGF pathway and iAs-induced EMT is the observation that iAs increased the activation of ERK$\frac{1}{2}$ in the HRTPT cells that had been exposed to iAs and undergone EMT. This type of response suggests that the many other ligands that can influence the ERK pathways might be active in the iAs-induced EMT. The important observation is that ERK was activated during the EMT process. The increase in chemokine receptors and their ligands might also play an important role during iAs induced EMT. The increase in expression of IL4, 10, 13, and 14 and the pathway identification of chemokine receptors bind chemokines would appear to have consequences for renal diseases in the human setting due to their role in immune responses and inflammation. Most studies on EMT involve its involvement in cancer progression. However, a role in renal disease was established a decade ago, indicating that renal epithelial cells could switch to a mesenchymal phenotype. [ 35,36,37] The involvement of EMT in inflammation [38], fibrosis [39,40,41], and wound healing [42] suggests a link between chemokines and EMT. The pathway analysis was consistent for the role of FGF and chemokines in the EMT of the HRTPT cells. The only pathway present in Panther, but not Reactome or David, was the WNT7a pathway. Wnt7a was increased in expression and provides some evidence for upregulation of the non-canonical Wnt-signaling pathway. [ 43,44] Both the canonical and non-canonical Wnt pathways have been linked with diabetic nephropathy [45]. Overall, this aspect of the study provides the first demonstration that a renal progenitor cell can undergo EMT when exposed to an environmental toxin. The time course of exposure provides a 167 gene set associated with the iAs induced development of EMT and corresponding 91 and 76 gene sets representing genes up- and down-regulated within the 167 gene set. These three sets of genes will be valuable in determining the expression, druggable targets, and prediction value in a wide variety of human renal diseases and other disease datasets associated with iAs exposure. The second aspect of this study was to determine, once iAs exposure was stopped if the iAs-treated HRTPT cells would retain their mesenchymal properties. The results showed that by the second passage following iAs removal, the cells had regained an epithelial morphology indistinguishable from the control HRTPT cells. This represents the initial observation that RPCs that have undergone EMT due to toxin exposure, can undergo MET back to an epithelial morphology after toxin removal. This ability is consistent with the observation that renal epithelial cells arise during embryogenesis by mesenchymal-to-epithelial transition (MET) [46,47]. It was confirmed that the cells undergoing MET retained the co-expression of PROM1 and CD24 and the ability to form nephron spheres and undergo osteogenic, neurogenic, lipogenic, and tubulogenic differentiation. A difference in tubulogenic differentiation was found for the recovered cells in that they expressed high levels of calbindin and low levels of aquaporin whereas the control unexposed cells had the opposite expression levels. To further explore this finding, global gene expression was performed at 2 and 11 passages following iAs removal. Following the removal of iAs, the cells at both P2 and P11 showed a marked divergence from the iAs-exposed HRTPT cells at P8 and a return to an expression profile more in line with the control HRTPT cells. This was especially noticeable in passage 11. The common genes between the control HRTPT cells compared to both the P2 and P11 cells were 36. Of these 36 genes, 3 had a reverse in expression between the control and recovered cells (CYFIP2, IGFBP3, and NMNAT2). To determine if iAs exposure might have a lasting, or potentially permanent, effect on gene expression, a common gene set was identified for iAs exposed cells at P3, P8, and P10 with those unexposed through P11. One could speculate that epigenetic modification due to iAs exposure might produce long-lasting alterations in the genome after iAs removal. The 33 gene set did identify interactions with p53 and the cell cycle. The possible interactions with p53 and the cell cycle would be consistent with the long-term carcinogenic effects of iAs. The obvious limitation of the study is that it is performed using cells in culture. The results will require validation in the human kidney. ## 4.1. Study Design A flowchart of the study design is shown in the visual abstract (Figure 10). ## 4.2. Cell Culture The isolation and serum-free culture conditions for the HRTPT cells have been previously described [10,11]. Confluent cultures of HRTPT cells were exposed to 4.5 µM iAs for 24 hrs and then subcultured at a 1:3 ratio in the continued presence of iAs until confluent. Following confluence, the cells were serially subcultured again in the presence of iAs until confluent. This was repeated for 10 serial passages. Additional cultures of iAs exposed cells at passage 8 were sub-cultured into iAs free growth media and continued in iAs free media for 11 additional passages. ## 4.3. Microarray Gene Expression The gene expression profile was determined using the Clariom D Human Microarray (platform ID: GPL23126) on triplicate samples of control HRTPT cells (P0) and HRTPT cells exposed to 4.5 µM iAs for 3, 8, and 10 serial passages (named as P3, P8, P10) and after recovery (named as P2, P11) (GSE215904). Each sample has gone through quality control processing before downstream analysis. The total of 138,745 probes were analyzed for each sample under different conditions. Confluent cultures were used for the isolation of RNA. ## 4.4. Individual Gene mRNA and Protein Expression The mRNA and protein expression of individual genes was determined using RT qPCR, western blotting, and flow cytometry as described previously [10,11]. ## 4.5. Statistical Analysis Statistical significance of genes was calculated by running t-tests [48] between pairs of groups such as all subset of passages with respect to P0, iAs+ (combination of P3, P8, and P10 passages) with respect to P0, iAs− (combination of P2, P11 passages) with respect to P0 and iAs+ versus iAs−. When comparing more than two groups, one-way ANOVA (Analysis of Variance) was performed. Scattered volcano plots were used to show the statistically significant genes with p-value < 0.05 and fold-change (FC) greater than two in both directions (up or down-regulation). The foldchange for each gene was calculated based on antilog-expression value between two phenotypic conditions. Most of the genes provided in different tables were selected based on p-value ≤ 0.05 with or without fold change (FC ≤ 0.5 or FC ≥ 2). A principal component analysis (PCA) was performed to test the distribution of replicates of passage samples and Pearson correlation was used to find the relationship between the different passage conditions as well as genes [49,50]. The first two components of PCA i.e., PC1 and PC2 were used to explain the amount of variance in the data according to phenotypic condition(s). Venn diagrams were used to demonstrate the union and intersection of genes in different conditions. The entire analysis was performed using R/Bioconductor. ## 4.6. Pathway Analysis Different significant gene lists were examined using commercially available pathway tools such as QIAGEN Ingenuity Pathway Analysis (IPA) [51], as well as freely available Gene Set Enrichment Analysis (GSEA) [52], Reactome [53], Panther [54], and DAVID [55,56] software databases. ## 4.7. Gene Set Enrichment Analysis Gene set enrichment analysis was performed on different gene sets identified through t-tests or ANOVA using $p \leq 0.05$ +/- FC < 0.5 or >2. In some cases, some genes with fold change > 4-fold were also included, regardless of the p-value to sets the relevance at the functional level. Probes were ranked according to their p-value and/or log2 fold change and all the probes without gene symbols were excluded as they cannot map with the different pathway databases. Ranked lists were used in the Gene Set Enrichment Analysis pre-ranked software for a minimum gene size of 5 with a maximum gene set size of 500 [52]. The MSigDB pathway database was used to identify enriched gene sets including Hallmark, C2, and C3 [57]. ## 5. Conclusions This study shows that human renal progenitor cells, in vitro, undergo EMT when exposed to a nephrotoxin and undergo MET upon toxin removal. 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--- title: Are Football Players More Prone to Muscle Injury after COVID-19 Infection? The “Italian Injury Study” during the Serie a Championship authors: - Alessandro Corsini - Andrea Bisciotti - Raffaele Canonico - Andrea Causarano - Riccardo Del Vescovo - Pierluigi Gatto - Paolo Gola - Massimo Iera - Stefano Mazzoni - Paolo Minafra - Gianni Nanni - Giulio Pasta - Ivo Pulcini - Stefano Salvatori - Marco Scorcu - Luca Stefanini - Fabio Tenore - Stefano Palermi - Maurizio Casasco - Stefano Calza journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048896 doi: 10.3390/ijerph20065182 license: CC BY 4.0 --- # Are Football Players More Prone to Muscle Injury after COVID-19 Infection? The “Italian Injury Study” during the Serie a Championship ## Abstract Introduction: Football was the first sport to resume competitions after the coronavirus disease 2019 (COVID-19) lockdown and promptly the hypothesis was raised of a potential relationship between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and musculoskeletal injuries in athletes. This study aimed to confirm the association between SARS-CoV-2 infection and muscle strain injury in a large population of elite football players and to investigate if the COVID-19 severity level could affect the risk of injury. Methods: A retrospective cohort study involving 15 Italian professional male football teams was performed during the Italian Serie A 2020–2021 season. Injuries and SARS-CoV-2 positivity data were collected by team doctors through an online database. Results: Of the 433 included players, we observed 173 SARS-CoV-2 infections and 332 indirect muscle strains. COVID-19 episodes mostly belonged to severity level I and II. The injury risk significantly increased after a COVID-19 event, by $36\%$ (HR = 1.36, CI$95\%$ 1.05; 1.77, p-value = 0.02). The injury burden demonstrated an $86\%$ increase (ratio = 1.86, CI$95\%$ 1.21; 2.86, p-value = 0.005) in the COVID-19 severity level II/III versus players without a previous SARS-CoV-2 infection, while level I (asymptomatic) patients showed a similar average burden (ratio = 0.92, CI$95\%$ 0.54; 1.58, p-value = 0.77). A significantly higher proportion of muscle–tendon junction injuries ($40.6\%$ vs. $27.1\%$, difference = $13.5\%$, CI$95\%$ $0.002\%$; $26.9\%$, p-value = 0.047) was found when comparing level II/III versus Non-COVID-19. Conclusions: This study confirms the correlation between SARS-CoV-2 infection and indirect muscle injuries and highlights how the severity of the infection would represent an additional risk factor. ## 1. Introduction Since the beginning of 2020, the world has been experiencing the coronavirus disease 2019 (COVID-19) pandemic [1], caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to suffering and deaths, changing almost every aspect of our society. Sports also had to kneel and stop all activities for a long time. The fear that sports could represent a risk factor in virus spread [2,3], disease aggravation [4], and long-term health consequences [5,6,7] has prompted sports doctors to proceed based on the principle of maximum prudence [8]. Football was the first sport to resume competitions after the lockdown started in March 2020. During the interruption, sport medicine physicians discussed issues related to training during the lockdown [9], tried to promote the maintenance of physical health, and mitigate the increased risk of injury expected upon resumption. Subsequently, the scientific literature debated the issue of return to play, trying to figure out which screening protocol could best assess the consequences of the infection, especially the cardiological, hematological, and pneumological ones [10,11,12]. In the meantime, football institutions, in collaboration with international medical societies, have proposed screening and organizational protocols to be implemented during national and international competitions [13]. The Fédération Internationale de Football Association (FIFA) changed the rules relating to player substitutions during matches, taking them from three to five for each team per match [14]. The sports season $\frac{2020}{21}$ started at different times in European countries. Furthermore, some teams had a shorter rest time after the end of the previous season, due to the delayed end of the European Champions League and Europa League competitions. The Italian Championship started on 19 September 2020 and quickly had to deal with a resurgence of the pandemic, with a steep increase in positive players a few months after the start. All these factors significantly influenced the performance of the championships in all countries of the world. Several studies tried to investigate how SARS-CoV-2 infection could affect the performance of athletes and, at the same time, put forward the hypothesis that COVID-19 could increase the risk of musculoskeletal injuries [5,9,10,15,16,17,18,19]. Indeed, muscle injuries still represent the first cause of injuries in football, accounting for a huge amount of missed matches for the players [20,21]. However, to the best of our knowledge, only a few studies have focused on the association between SARS-CoV-2 infection and muscle strain injuries in elite football players [22,23,24,25,26,27,28,29], often with questionable and conflicting results [30]. Furthermore, none of these studies investigated the influence of the COVID-19 level on the risk of injury. Therefore, the purpose of our study was to confirm whether SARS-CoV-2 infection was associated with an increase in the number of muscle injuries in a large population of professional athletes, and to investigate if the COVID-19 severity level could affect the risk of injury. ## 2.1. Study Design The Italian Injury Study is a retrospective observational cohort study promoted by the Italian Sports Medicine Federation (FMSI). At the end of the $\frac{2020}{2021}$ football season, all teams belonging to the Italian Serie A (the highest professional football league in Italy) were invited to participate in the study, collecting the required data retrospectively. Teams that agreed to participate in the Italian Injury Study were asked to individuate a member of the medical staff who would be responsible for collecting the data, named Team Data Manager (TDM). All the TDMs participated in a preliminary meeting in which the methodology of the study and the terms of data acquisition were described and discussed in detail. The collecting procedure was performed according to the international consensus statements for epidemiological study of injuries to professional football players [31,32]. TDMs of each football club compiled an online database provided with a standardized form that would gather all the data necessary for the study. All the information collected was fully anonymized: the teams, players and TDM identities were coded using computer-generated random hash strings. The platform was compliant with Italian laws relating to privacy and data protection. All the players involved were informed about the purpose of the study and were required to provide written informed consent. The study was conducted in accordance with the Declaration of Helsinki principles and its later amendments. The 4 June 2021 has been identified as the last date for the delivery of the final data requested. The injury data collection period stretched from 22 August 2020 to 23 May 2021. The data relating to positivity for COVID-19 were collected, starting on 1 January 2020. During that period, a SARS-CoV-2SARS-CoV-2 infection was tested using a PCR nasal swab performed and analyzed by an independent contractor. Each player was tested at least 48 h before each official game during the whole season, in case of symptoms or teammates’ positivity. Isolation of 14 days at home was mandatory. A negative test was necessary to return to training. ## 2.2. Patient and Public Involvement We aimed to collect an exhaustive sample of Italian Serie A. Unfortunately, five teams did not agree to participate. The 15 medical staff who agreed to participate were involved in the injury registration and the SARS-CoV-2 infection data collection. This study was carried out without patient involvement. Athletes were not invited to actively contribute to the study design or to write or edit the present document. However, each TDM and all team players provided consent to analyze the COVID-19-related data and they were all informed of the study results. ## 2.3. Setting The study group consisted of all the players belonging to the Italian Serie A who had agreed to participate in the study and who had regularly taken part in the sporting activity throughout the observation period. ## 2.4. Inclusion Criteria The study included all the players for whom the following data were available:SARS-CoV-2 infection characteristics, defined as [33]: Polymerase Chase Reaction (PCR) COVID-19 test positivity and subsequent PCR negativization date;Return to play (RTP) date;Clinical classification of COVID-19 severity. 2.Injuries: Injury occurrence and RTP date;Anatomical location of the injury;Situation in which the injury occurred (i.e., training or competition);Type of injury (i.e., first injury or re-injury). 3.Exposure: Day-by-day players’ exposure time in training or match. ## 2.5. Exclusion Criteria Those players for whom all the aforelisted data were not available for the entire observation period were excluded from the study. ## 2.6. Subjects Fifteen clubs out of twenty belonging to the Italian Serie A agreed to participate in the study. Players who left or joined the team during that season were included during their time spent on the team. ## 2.7. Data Collection The following data were recorded:The number of subjects who contracted COVID-19 during the observation period. For each subject affected by SARS-CoV-2 infection, the days lost due to infection (RTP time) and the severity of the disease were also collected. The severity was classified based on five levels according to the Coronavirus Disease 2019 (COVID-19) Treatment Guidelines [34] (Table 1). RTP was defined as the moment when a player made a full return to training and competition, without any restrictions [35,36]. ## 2.8. Statistical Analysis Quantitative data were reported as mean values and standard deviation (SD), while categorical data were summarized using counts and percentages. Injury risk was modeled as a repeated event, while COVID-19 status was entered as a time-varying exposure, using a Prentice, Williams, and Peterson (PWP) model. The injury incidence rate was modeled using a Generalized Linear Mixed Model (GLMM) [38] assuming a Poisson distribution for injury counts on the player and exposure time entered as an offset term. Injury burden was defined as the total amount of days lost due to injuries for each player, scaled to 1000 h/player of exposure. Injury burden was estimated by modeling injury time for each patient using a GLMM with exposure time entered as an offset term. Due to the highly skewed distribution and zero inflation, we used a Tweedie distribution family [39] that allows us to seemingly model skewed data with a point mass at zero. The usage of GLMM models allows one to account for the potential within player event correlation, as players infected by SARS-CoV-2 contribute both to Non-COVID-19 (before infection) and COVID-19 groups. The proportions of injuries according to the anatomical location were estimated using a multinomial regression model. The comparison of average COVID-19 duration between gravity levels (II–III vs. I) was performed using a robust linear model, to account for some extreme values [40]. Results were reported as estimates and corresponding $95\%$ confidence intervals, with p-value adjusted for multiple comparisons when needed. All statistical tests were two-sided and assumed a $5\%$ significance level. All analyses were performed using R (version 4.2.1) [41]. ## 3. Results Fifteen out of twenty teams belonging to the Italian Serie A championship $\frac{2020}{2021}$ participated in the study. According to the eligibility criteria, 433 subjects (mean age ± SD of 26.08 ± 5.03 years) were included in the study. During the follow-up period, we observed 173 subjects ($39.95\%$) with SARS-CoV-2 infections, a total of 332 indirect muscular injuries affecting 204 players, and 214 other events such as sickness that was not COVID-19 related or injuries other than the one of interest. Among the indirect muscular injuries, 104 ($31.3\%$) occurred after a SARS-CoV-2 infection, while 228 ($68.7\%$) were not preceded by COVID-19 episodes. COVID-19 episodes were mostly level I (78, $45.1\%$) and II (84, $48.6\%$), with a few level III (11, $6.4\%$). Due to the low number of moderate illness cases, we collapsed levels II and III as Mild-Moderate illnesses. The risk of injury following a COVID-19 episode was estimated using a Cox model for repeated events (injuries) and time-varying exposure (COVID-19 disease). The risk of injury significantly increased after a COVID-19 event by $36\%$ (HR = 1.36, CI$95\%$ 1.05; 1.77, p-value = 0.02) (Table 2). When considering the injury incidence, expressed as the number of injuries per 1000 h of exposure (playing time), we estimated a $69\%$ increase in the injury incidence rate (RR = 1.69, CI$95\%$ 1.21; 2.38, p-value = 0.0023) in the Mild-Moderate level versus Non-COVID-19, while level I COVID-19 (asymptomatic) showed a rate comparable to the Non-COVID-19 group (RR = 0.95, CI$95\%$ 0.61; 1.46, p-value = 0.80) (Table 3). The injury burden showed a similar pattern, with a substantial and significant increase in Mild to Moderate COVID-19 compared to Non-COVID-19, with an $86\%$ increase (ratio = 1.86, CI$95\%$ 1.21; 2.86, p-value = 0.005), while asymptomatic subjects showed a similar average burden (ratio = 0.92, CI$95\%$ 0.54; 1.58, p-value = 0.77) (Table 4). To evaluate if the proportion of injuries in the muscle–tendon junction might vary depending on the injury being after a SARS-CoV-2 infection or not, and on the level of the COVID-19 symptoms, we tested if the proportion of muscle–tendon junction events, estimated using a multinomial model, was significantly different according to COVID-19 status. We found a significant, although marginally, higher proportion of muscle–tendon junction injuries ($40.6\%$ vs. $27.1\%$, difference = $13.5\%$, CI$95\%$ $0.002\%$; $26.9\%$, p-value = 0.047), when comparing level II/III versus Non-COVID-19. Again, level I COVID-19 was not significantly different from Non-COVID-19 ($35.3\%$ vs. $27.1\%$, difference = $8.2\%$ CI$95\%$ −$8.9\%$; $25.3\%$, p-value = 0.35) (Table 5). According to the Italian law during the study period, the minimum isolation period after infection was 14 days regardless of the severity of the symptoms; we computed that the average number of days of absence due to SARS-CoV-2 infection was equal to 19.0 (robust mean = 16.1) days (range 14–63), with no statistical difference among COVID-19 levels (robust t-test. Average difference II/III vs. I: −0.023, CI$95\%$ −1.16; 1.11, $$p \leq 0.97$$) (Table 6). ## 4. Discussion The results of this study show that the football players affected by SARS-CoV-2 infection had a $36\%$ higher risk of having indirect muscle injuries than players who never contracted COVID-19 or whose indirect muscle injuries occurred before contracting the infection. Furthermore, the injury incidence increases in the COVID-19 severity level: despite the COVID-19-unrelated group having a similar muscular injury incidence to the players that suffered level I COVID-19, the players having II and III COVID-19 severity levels had a $69\%$ incidence increase in indirect muscle injuries. These data are confirmed by the injury burden parameter, the cross-product of severity and incidence, that recently was suggested to provide a more complete picture of risk assessment in sports injuries [42,43]. Indeed, the data of our study show an injury burden increase in the II and III COVID-19 severity levels compared to the COVID-19-unrelated group. Despite COVID-19 primarily affecting the cardio-respiratory system [44], the SARS-CoV-2 infection may affect muscle function, compromising peripheral capillarization and consequently decreasing muscle oxygen uptake, limiting oxidative metabolic pathways [45,46,47]. This limitation in the aerobic metabolic system may, in an intermittent high-intensity sports activity such as football, prematurely induce a state of fatigue, which represents a predisposing factor for indirect muscle injury [48,49]. Indeed, fatigue can alter the muscle recruitment pattern and force production, thereby increasing muscle injury risk [48]. Furthermore, the reduction in the oxidative capacity of the muscle [50] increases the involvement of the anaerobic lactacid metabolism with the same effort intensity, giving rise to subsequent premature muscle acidosis [51]. An increase in muscle acidosis leads to the greater fragility of the muscle fibers and subsequently to a higher risk of muscle injury [52,53]. Moreover, aerobic system perturbation may reduce the overall endurance capacity of the athlete. Since a lower VO2 max is an independent risk factor for indirect muscle injuries [54], we may suppose that an athlete who has contracted COVID-19 is subject to an increased risk of muscle injury. A further risk factor that could play a role in the increased injury incidence was the isolation period after the SARS-CoV-2 infection. As a matter of fact, during the 2020–2021 season, Italian law mandated the 14-day isolation to be spent at home. Indeed, the detraining induced by such an isolation period may represent another important risk factor for indirect muscle injuries [55,56]. This hypothesis is consistent with other studies showing that muscular detraining may induce some important physiological adaptations [57]. Some authors [58] demonstrated how, after a muscle activity suspension of 14 and 23 days, the knee extensor torque, the cross-sectional, the vastus lateralis fascicle length and the tendon stiffness decreased. Furthermore, a significant deterioration in tendon mechanical properties occurring within two weeks, exacerbating in the third week of suspension of muscle activity, was noted. The authors conclude that rehabilitation aimed to limit muscle and tendon deterioration should probably start within 2 weeks of unloading. These effects on muscle structure could be the cause of a muscular force and structural imbalance that produced the shift in the incidence of injury site location within the muscle structure, despite the type of muscle not differing in the COVID-19 and Non-COVID-19 groups. Indeed, we found increased susceptibility to injury of the muscle–tendon junction in the COVID-19 level II and III than in the COVID-19-unrelated group. These findings are in line with some other studies that demonstrate how immobilization and detraining can reduce the MTJ structure and endurance in rats [59,60,61] and humans [62] Yet, the data in our study do not allow us to assert that a longer RTP duration is related to the level of severity of SARS-CoV-2 infection. Probably the need to have the players back on the team as soon as possible leveled the RTP duration regardless of the COVID-19 illness severity. Furthermore, it is important to remember that after COVID-19, a period of rest until the complete resolution of symptoms [56,63], followed by a specific athletic reconditioning period [55], is strongly recommended. Unfortunately, due to team needs, this is often impossible in the middle of a professional sporting season [51,64]. Indeed, most of the players, once recovered from COVID-19, were generally reintegrated into the team as soon as possible, to respond to team performance needs. During the post-COVID-19 period, it is probable that the players not only did not go through a suitable physical reconditioning period but were forced to train and compete with players who, not having contracted COVID-19, had never stopped training. Consequently, they were not in suitable physiological conditions for being exposed to the same training volumes and intensity as the other players, which increased the risk of injury. This hypothesis is consistent with a study conducted on football players belonging to Bundesliga during the 2019–2020 season, showing a proportionally higher number of injuries in athletes striving to play their first match too early after the COVID-19 disease [65]. The effect of a quick RTP could explain the susceptibility of injury in matches compared to training recorded in the level II and III groups. We can argue that the higher intensity level and the strong muscle demand of matches could reveal the low level of training due to the COVID-19 illness. The data recorded in our study show that the effect of the severity of COVID-19 level on the time elapsed between the RTP period and the first recorded indirect muscle injury was not statistically significant. Furthermore, the athletes who suffered a COVID-19-related injury (severity levels II and III) had a more severe injury burden in comparison to the athletes who suffered a COVID-19-unrelated injury. In addition to this, the injury burden value of athletes affected by COVID-19 progressively increased according to the three levels of infection severity recorded. The higher value of injuries incidence and injury burden, regardless of the RTP time, for athletes affected by COVID-19 level II and III as compared to those affected by COVID-19 level I, may be at least partially argued by the musculoskeletal susceptibility to SARS-CoV-2 infection. This infection seems to influence muscular cell activity by a direct mechanism, with the virus binding to the ACE2 receptor on the skeletal muscle cell surface, and by an indirect mechanism named “cytokine storm”, a deregulated release of numerous cytokines by the immune system after a lung infection [47]. The process triggered by the virus could induce muscle fiber proteolysis, promoting a decrease in protein synthesis, interfering in the myogenic process, and disrupting the body’s homeostasis [66,67]. However, the lack of scientific research focusing on the musculoskeletal system of athletes, and the fact that few studies included functional or morphological techniques in their methodologies to demonstrate the direct muscle functional damage, prompt the authors to remain cautious in conferring a causal link. Our results confirm the findings of other studies [23,51,65], even if our study has more exhaustive collecting modalities, a bigger sample size, and a higher number of events recorded. Other similar research explores this situation in other major football European championships, demonstrating how this is a new, hot, and widespread topic in the scientific literature: Wezenbeek et al. [ 26] reported a five times higher risk of developing a muscle strain after COVID-19 in Belgian professional footballers; Mannino et al. [ 22] showed an increase in injuries in the pandemic-related English Premier Leagues season; Maestro et al. [ 19] highlighted a doubled risk of muscle injuries after COVID-19 in Spanish football players; and Seshadiri et al. [ 25] shared a similar worrying scenario in German Bundesliga. The clinical consequences of the situations described in our study would recommend extreme caution in the process of reintegration into training and competitive activity of a player who has contracted the SARS-CoV-2 infection [68], especially after moderate and severe ones. Aerobic, resistance, and speed training should follow specific phases, based on the progressiveness of the training load and the consequent physiological adaptation response [55]. Moreover, muscle injury prevention exercises should be introduced and/or increased as part of the training program’s response [55]. Finally, a gradual and cautious return to training and return to play protocol, under medical supervision, should be adopted [68]. This study suffers from some limitations. The most important is represented by the fact that our study design did not allow us to make direct causal inferences concerning the effects of COVID-19 pathology. In addition, the associations found in this study should be interpreted considering that many interdependent factors are involved in causing muscle injuries. The study does not consider, for example, that pharmacological therapies (such as steroids and antibiotics) taken for the treatment of COVID-19 may have played a role in making the player more susceptible to muscle injury. Moreover, no follow-up was performed to confirm this trend in the following seasons, with different variants of the virus, different pharmacological and diagnostic approaches to the pandemic, and new rules about isolation. Finally, we must clarify that injuries can be a repeating event within subjects, and the same subject might have one or more injuries and these can occur both before and after COVID-19. Therefore, a subject contributes to both groups, without and with COVID-19, depending on the time point considered. Thus, this set of players at risk of injuries is a dynamic riskset. Therefore, the results of the study must be interpreted with caution. ## 5. Conclusions Our results show the correlation between COVID-19 infection and an increase in indirect muscle injuries in professional football players. Furthermore, this study highlights how the severity of the infection would represent an additional risk factor. Moreover, we demonstrated how COVID-19 level I infection does not seem to affect the risk of muscle injury more than normal football activity. Considering that no difference in time to RTP after infections was found, these data suggest that the short-term detraining effects due to the time loss, but probably also a direct action of the virus and the inflammatory process triggered by the virus on muscle tissue, could be associated with a greater risk of indirect muscle lesions. However, the continuous evolution of the virus and the lack of studies focused on musculoskeletal system damage is preventing us from drawing definitive conclusions. 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--- title: Predicting Athlete Intentions for Using Sports Complexes in the Post-Pandemic Era authors: - Tsung-Yu Chou - Peng-Yeh Lee journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048897 doi: 10.3390/ijerph20064864 license: CC BY 4.0 --- # Predicting Athlete Intentions for Using Sports Complexes in the Post-Pandemic Era ## Abstract In recent years, the concept of health has gradually fit into people’s lives through the government’s promotion. The indoor sports complex is becoming more and more popular, offering people the opportunity to engage in physical and recreational activities regardless of weather conditions. Psychological and social abundance is the key to improving happiness, and the most important thing is to treat and care for yourself. Many fitness venues have emerged to provide athletes with a wide range of choices. However, the advent of the COVID-19 pandemic, which is caused by a virus mainly transmitted through direct contact or air droplets, has had a severe impact on indoor gym users. Therefore, based on the Theory of Planned Behavior (TPB) and Health-Promoting Lifestyle (HPL), this research investigated athletes’ behavioral intentions regarding sports halls and perceived risks as interfering variables. For data collection, we collected data samples from sports complexes athletes in Taiwan. A total of 263 responses were analyzed via SPSS 20.0 (IBM Corporation, New York, NY, USA) and AMOS 20.0 (IBM Corporation, New York, NY, USA) seis tests. The study’s results indicate that health-promoting lifestyle cognition has a positive and significant effect on behavioral intention; athletes’ attitudes, subjective norms, and perceived behavioral control significantly affects the behavioral intention of using the facilities in a sports complex. Athletes’ risk perceptions have an interference effect between HPL, attitude, subjective norm, perceived behavioral control, and behavioral intentions of using the facilities in a sports complex. Sports venue managers can refer to the results of this project to develop marketing strategies and promoting. ## 1. Introduction With the growing domestic economy and the change of living habits and concepts, people have gradually paid more attention to sports and leisure life (Tsorbatzoudis et al. [ 1]). Recreational sport refers to physical activity that occurs during leisure time and focuses typically on participation, as opposed to winning material [2]. Recently, under the government’s policy of developing a public fitness program, the rate of people’s participation in sports was $80.2\%$ in 2021, maintaining a high value of over $80\%$ for 8 consecutive years since 2014, according to the results of the 2021 sports survey released by the Sports Administration Ministry of Education. The population participating in regular sports reached $33.9\%$, remaining stable at over $30.0\%$ for eight consecutive years since 2012 [3]. However, due to the limited space for urban leisure in Taiwan, people have fewer recreational activities, leading to their lack of exercise. However, people’s health consciousness has gradually increased recently. In addition to making significant dietary adjustments, they also want to have fitness facilities for recreational activities. The Sports Complex represents a physical space for human health and fitness-centric industries that serve various societal dimensions such as cultural, economic, and social aspects at individual and national levels [4]. According to Roychowdhury [5], during the COVID-19 pandemic, the deleterious effects of physical inactivity coupled with sedentarism can concoct a dangerous recipe for a range of adverse psychophysiological health issues for individuals. This is particularly critical for the current confined circumstances, as physical inactivity and sedentarism have already been declared as global crises [6,7]. In response to this demand, sports centers began to build their indoor sports facilities to enable people to enjoy exercise without being affected by weather conditions. Many fitness complexes also emerged in communities, providing athletes with various sports and recreational activities. According to Irawan, Bastarianto, and Priyanto [8], good exercise program implementation and social distancing measures can effectively inform people’s willingness to engage in sports. Therefore, there is a need to explore athletes’ intentions for using multisport fields in the post-pandemic era. Maglacas [9] suggested that stability and health potential are the two major dimensions of health; stability refers to a stable physical, psychological, and social balance, and health potential indicates a group or an individual’s ability to cope with environmental and psychosocial demands and stresses. Based on Richter et al. [ 10], the workplace can affect people’s health promotion and well-being. For people working in the sports complex, low self-perception will affect their health promotion [11]. Health promotion is not a specific disease or health-problem-prevention method, but a positive self-actualization-oriented approach that guides individuals to maintain or increase their positive attitudes toward health, self-actualization, and well-being. It represents the individual’s proactive approach to establishing new positive behaviors. Walker et al. [ 12] suggested that a health-promoting lifestyle is “a multifaceted, spontaneous behavior and awareness of the individual to maintain or enhance health, self-actualization, and self-fulfillment”. According to the WHO [13], health promotion enables people to increase control over and improve their health. It affects individual behaviors, environmental factors, and lifestyles. Moreover, during the COVID-19 pandemic, people are vulnerable to psychological issues, sleep disorders, fear, anxiety, depression, somatization, and obsessive-compulsive disorders [14]. This concept of a health-promoting lifestyle contributes to athletes’ attitudes towards self-health promotion, which is included in the factors that affect athletes’ behaviors towards the use of sports complexes and is one of the issues in this study. During the consumption process, the amount of perceived risk affects the consumer’s behavior, which leads them to take certain actions that can reduce the risk [15]. According to Brown and Gentry [16], the main actions athletes take in the face of risk are buying directly and delaying or abstaining from buying to reduce the potential loss of the purchase and increase the certainty of the purchase outcome. Previous studies have discovered that athletes’ risks (corresponding to purchase intention) and perceived risks are key factors in influencing athletes’ purchase intentions, while perceived risk can significantly affect athletes’ attitudes and purchase intentions [17]. The COVID-19 pandemic emerged around the world in 2019. The virus is transmitted mainly through direct contact with viral secretions or air droplets. Up to now, tens of millions of cases have been confirmed worldwide, and the number of deaths continues to rise. This has seriously impacted the global economy, society, and the psychological well-being of people [17]. The rapid spread of COVID-19 has been attributed to the long environmental survival of the virus. It lasts in the air for 3 h, on copper surfaces for 4 h, and on cardboard surfaces for 24 h [18]. The plastic and stainless steel materials of common fitness equipment in sports centers, such as handrails, handles, and exercise equipment, are all potential vectors of the virus. Through respiratory droplets, indoor sports spaces and environments could become contaminated and cause serious impact on the facility and its clients. Therefore, the use of all gyms was suspended during the height of the pandemic. Although the pandemic is slowing down, it does not mean that people are completely at ease, as patients with no symptoms appear one after another, and the risks associated with fitness still affect indoor sports venue users. Therefore, understanding the athletes’ risk perceptions and behavioral intentions is an urgent issue for sport complex operators. The discussion of intention and attitude began with Fishbein and Ajzen’s [19] assessment of the relationship between the two. They proposed the Theory of Reasoned Action (TRA) after considering the relevant factors affecting attitude. The authors hypothesized that individuals’ behavioral attitudes and subjective norms influence their behavioral intentions. Later, Ajzen [20] extended the (TRA) theory by suggesting that human behavior follows the principle of rational thinking, and the decision-making process goes through information gathering and comparative solution evaluation to choose the best solution. The Theory of Planned Behavior (TPB) was developed, which suggested that the intention of that behavior influences a particular behavior, and that three variables, namely, attitude, subjective norm, and perceptual behavior control, are the main factors affecting the intention of the behavior. This model is more accurate in predicting consumer behavior and has been widely used in predicting behavioral intention [17,21]. In this study, the TPB was used as a basic framework to investigate the behavior of athletes in the sports complex by combining a health-promoting lifestyle and athletes’ perceptions of risk. As described above, the objectives of this study are as follows: [1] To investigate the influence of sports complex athletes’ behavioral attitudes, subjective norms, and perceived behavioral controls on their behavioral intentions of use. [2] To investigate the effect of sports complex athletes’ health-promoting lifestyle awareness on their behavioral intentions of use. [3] To investigate whether the sports complex athletes’ behavioral intentions of use are interfered with by perceived risks. ## 2.1. Awareness of Health-Promoting Lifestyles Health promotion has become a hot topic in medicine, public health, and nursing. Pender et al. [ 22] argued that health promotion is not about disease prevention, but rather a progression that individuals or groups use to maintain and improve health, achieve self-actualization, and obtain self-fulfillment. A rather new perspective in health promotion is the capability approach, that enable people to increase control over their own lives and health [23]. It represents an individual’s active initiative to develop new behavior patterns with an approach behavior and an actualizing tendency of self-actualization toward positive growth, rather than a behavior solely directed toward disease or health problem prevention. In addition, the coaches’ efforts to promote health increase young athletes’ enjoyment, self-esteem, and health [24]. The earliest scale developed to measure HPL was the Lifestyle and Health Habits Assessment (LHHA), developed by Walker et al. [ 12]. They proposed that HPL is a multifaceted form of spontaneous behavior and perceptions that individuals use to enhance or maintain self-fulfillment and self-actualization. Exercise can improve physical fitness and cause energy expenditure to reduce excess fat. Therefore, correct and continuous exercise helps improve one’s body shape and keeps one physically fit [25]. The WHO [26] believes that doing sports regularly is extremely important for personal health and well-being. In a study by Huang and Chang [27], the correlation between HPL and perceived health status was significantly positive. Moreover, exercise is the most important factor in lifestyle. It increases people’s participation in physical activities and helps them achieve better physical health through physical training [28]. Thus, it is important to exercise regularly to maintain physiological stability [29]. Heaps [30] also pointed out that exercise can improve self-image, self-satisfaction, and social adaptation while reducing anxiety, depression, and self-centeredness. Regular exercise can not only benefit people’s health but also positively affect an individual’s psychological status [31]. Regular exercise has physiological and psychological benefits; aside from promoting personal health [32], it also increases self-satisfaction and social adaptation and reduces anxiety, depression, and self-centeredness [30]. Based on past studies, regular exercise can help remove anxiety and reduce the risk of developing depression [33]. Referring to Mottola et al. [ 34], exercise will make people feel relaxed and comfortable as it can improve people’s body and brain temperatures. ## 2.2. Theory of Planned Behavior The Theory of Planned Behavior (TPB) provides a theoretical framework for understanding athletes’ behavioral intentions. Ajzen [35] proposed TPB, who argued that individual behavior emerges from behavior intention. BI (behavior intention) refers to one’s intention to perform a particular behavior, i.e., the consideration that occurs before starting or preparing to engage in something [35,36]. TPB is one of the most used theoretical models for predicting people’s behaviors or behavioral intentions [37]. TPB proposes that their behavioral intention directly determines a person’s actual behavior. It is assumed that the individual’s intention to perform the behavior will be stronger if they have more positive attitudes toward a behavior, greater perception of the surrounding norm, or greater perception of control over the behavior. Therefore, the three major factors influencing athletes’ behavioral intentions are attitude, subjective norm, and perceived behavioral control, and are major predictors of behavioral intention [38,39]. Attitudes can be obtained as a psychological emotion through behavioral appraisals. BI (behavior intention) tends to be more positive if attitudes are positive [38]. The subjective norm is the sense of social pressure to perform the behavior or not, that is, the influence of other people, such as friends, relatives, or colleagues, and the feeling of social pressure on the individual to perform a particular behavior [20]. Therefore, the norm or pressure emerges when individuals consider whether others around them maintain a certain opinion about behavior before performing it, generating subjective norms [21]. Perceived behavioral control emphasizes controlling external and general factors, such as time and opportunity [40]. Therefore, based on the above, it is appropriate for this research to explore athletes’ behavioral intentions for using the facilities in a sports complex based on TPB. When an individual’s attitude towards sports promotion is more positive, their behavioral intention will be more positive, and vice versa. In terms of subjective norms, the consumer’s behavioral intentions can be predicted based on the social pressure they experience about their physical appearance. The performance of important references (e.g., family members, friends, or coworkers) would also be a source of pressure on an individual’s behavioral intentions. Perceived behavioral control reflects one’s perception of the ease of behavior [40]. The cost and time of work and the ability to access the venue’s location are also factors that influence the consumer’s behavioral intention. ## 2.3. Risk Perception Risk is a key element in purchase behavior [41]. Perceived risk is often described as various types of possible losses or perceived uncertainty related to product selection or consumption [42]. It is a subjective perception of possible loss when seeking a desired outcome from a product or service [43]. Perceived risk has long been recognized as a key factor that influences consumer decisions and behavior [36]. While investigating how perceived risk would influence purchase intention, Cabeza-Ramírez et al. [ 44] discovered that perceived risk can impose a significantly negative effect on product attitude and purchase intention. Yu et al. [ 45] described the perceived risk as the various perceptions of uncertainty and negative outcomes related to an athlete’s purchase or choice of a product or service. Han et al. [ 36] defined perceived risk as the various possible or potential losses the customer perceives in purchasing or choosing a product or service. Perceived risk in consumer behavior can be divided into multiple components depending on the nature of the loss caused by the transaction between firms and athletes [46]. Roselius [47] suggested that athletes may suffer losses in the form of time, safety, and money. Among all the concepts of perceived risk, this study defined perceived risk as the possibility of physical health problems or that people will suffer from diseases while doing exercises in sports complex during the COVID-19 pandemic. ## 2.4. Hypotheses Development Among the diversified recreational activities, choosing the activity that suits oneself and shows one’s leisure characteristics and the ability for group cooperation can achieve the best exercise adjustment during leisure time. Exercise also mediates negative stress and generates more positive energy, restoring people’s physical and mental health, indirectly relieving stress at work, and improving interpersonal relationships and quality of life in general. Recreational activities play a neutralizing and soothing role in stress elimination [48]. Participation in sports and recreational activities is also essential for social interaction. By joining a sports community and engaging in recreational activities with others, one can build and expand their social network and enhance one’s sociability [49]. From the above literature, the cognition of HPL has a positive and significant effect on exercise and fitness behavioral intentions. Therefore, this study proposes the following hypothesis: H1. An athlete’s health-promoting lifestyle cognition has a positive and significant effect on their behavioral intention to use the facilities in a sports complex. Several studies confirmed that attitudes, perceived behavioral control, and subjective norms indirectly influence intentions while directly affecting behavior [50,51]. Cabeza-Ramírez et al. [ 44] suggested that when an individual’s attitude is more positive, their behavioral intentions are also more positive, and vice versa. In addition, Massoud et al. [ 52] suggested that individuals with positive attitudes toward a particular behavior are more likely to perform the said behavior. Based on the above studies, this paper proposes the following: H2. An athlete’s attitude significantly affects their behavioral intention of using the facilities in a sports complex. On the inference of subjective norms influencing athletes’ intentions, previous research focused on the extent of the impact that a significant person’s approval made on people’s intentions to perform a particular behavior [53]. Individuals’ behaviors are often based on their perceptions of others, and their intentions to accept potential behaviors are heavily influenced by those with whom they have close relationships [54]. In addition, the greater the pressure exerted by a significant person is, the greater the individual’s intention to engage in the behavior will be [55]. Based on the above studies, this paper proposes the following: H3. An athlete’s subjective norm significantly affects their behavioral intention of using the facilities in a sports complex. People may not have full control over the opportunities, resources, time, knowledge, and skills available to them, but these factors influence their behavioral intentions [53], that is, the more resources and opportunities an individual believes they have and the fewer the obstacles they anticipate, the more control they will have over the behavior. Davies et al. [ 56] suggested that both internal factors (e.g., skills, abilities, knowledge, and proper planning) and external factors (e.g., time, opportunities, and dependence on others to cooperate) may interfere with control over expected behavior. Kautish et al. [ 40] also argued that behavioral control (e.g., competence) determines behavioral intentions. Thus, perceived behavioral control refers to a person’s perception of the ease of performing a particular behavior [57]. From the above, this paper proposes the following hypothesis: H4. An athlete’s perceived behavioral control has a significant positive effect on their behavioral intention of using the facilities in a sports complex. Clientages perceive various types of risks due to the uncertainty associated with buying products and services. Therefore, the level of perceived risk affects their decision-making process and purchasing behavior [58]. According to Rosillo-Díaz et al. [ 59] perceived risk can impose a significantly negative effect on athletes’ purchase intentions. The influence of perceived risk on athletes’ purchase behavioral intentions is important in this study. Athletes perceive a higher risk in facing the outbreak of an infectious disease (e.g., COVID-19) with no clear treatment. A high level of perception leads athletes to have an intention to avoid such risks [60]. Yu et al. [ 61] showed that perceived risk for COVID-19 has a significant negative impact on revisiting hotels. Joo et al. [ 62] suggested that new infectious diseases lead to various perceived risks for athletes, which may create a psychological burden resulting in extremely passive and restrictive consumption behaviors. Therefore, the following hypotheses are proposed: H5. An athlete’s risk perception has an interference effect between HPL and behavioral intentions of using the facilities in a sports complex. H6. An athlete’s risk perception has an interference effect between attitude and behavioral intentions of using the facilities in a sports complex. H7. An athlete’s risk perception has an interference effect between subjective norms and behavioral intentions of using the facilities in a sports complex. H8. An athlete’s risk perception has an interference effect between perceived behavioral control and behavioral intentions of using the facilities in a sports complex. ## 3.1. Research Model As mentioned, this research employed the expanded TPB model to verify people’s intentions to use the sports complex. Figure 1 shows the research framework, including three TPB variables: attitude, subjective norm, perceived behavioral control, and HPL’s influence on behavior intention. Among all those variables, the perceived risk was the moderator of this study. The eight hypotheses proposed in this research are shown above. ## 3.2. Research Participants The COVID-19 pandemic has severely hit the sport industry, and it is known that limiting physical contact is important to reduce the spread of COVID-19. In order to avoid excessive contact, users were asked to use their mobile phones to scan codes to fill out electronic forms. The random sampling method was to approach potential subjects onsite, outside of the sports complexes of three metropolitan areas in Taiwan (Taipei City, New Taipei City, and Taichung City) that were seriously affected by the epidemic from 1 September to 10 October 2022. A total of 285 questionnaires were distributed, and 263 valid questionnaires were obtained after deducting 22 invalid ones, with a recovery rate of $92\%$. The average time spent filling in the questionnaires was approximately 15 min, and the appropriate written consent to participant in this study was also signed in a voluntary manner by the athletes. We pointed out in Announcement No. 1010265075 of the Health Department of the Taiwan Executive Yuan that if the researchers have fully informed the subjects of the investigation of the method and purpose, the non-human test measurement method will use the questionnaire survey method. ## 3.3. Research Instruments To measure the variables in this study, we quote effective research items from existing studies. A Likert 5-point scale measured all the items, the points on which range from 1 to 5 (strongly disagree, disagree, neutral, agree, strongly agree). The questionnaire of this research is based on the planned behavior scale compiled by Ajzen [35], the perceived risk scale by Schuett [63], and the health-promoting lifestyle (HPL) scale by Walker et al. [ 12]. As for the demographic analysis, gender, age, educational background, and annual income were included. ## 3.4. Data Processing and Analysis After collecting all valid questionnaires, this study employed SPSS 20.0 to analyze the samples. Then AMOS 20.0 was used in the offending estimates, normal distribution test, confirmatory factor analysis, and structural relationship model analysis to verify the hypotheses. SEM is a statistical method used to verify assumptions and models based on the direct or indirect relationship between multiple observed and non-observed variables [64]. Moreover, it is the most appropriate way to specify measurement errors, which traditional methods cannot realize. ## 4.1. Demographic Profiles Of the 263 respondents, $46\%$ were male and $54\%$ were female. A total of 29 % of the participants were 41–50 years old. When asked about their educational background, $50.2\%$ said they were university (professional), followed by postgraduate ($35.7\%$) and high school (vocational) ($14.0\%$). Of the participants, about $33.5\%$ reported their annual income was between TWD 310,000–500,000, followed by TWD 510,000–700,000 ($22.4\%$), as shown in Table 1. ## 4.2. Test of Offending Estimates “Offending estimates” refers to a type of non-structural or measurement model. The model is improperly interpreted if the statistical coefficient exceeds the acceptable range [65]. Hence, before the overall model goodness, we would conduct the test of offending estimates in this study. Results showed that for the error variance of measured, the estimated values were between 0.02 and 0.82, all of which conformed to the standard value of 0.95 put forward by [64]. In summary, there were no offending estimates in the overall model of this study, and the overall model goodness test can be conducted later, as suggested in Table 2. ## 4.3. Normal Distribution Test To avoid incorrect conclusions caused by the expansion of the chi-square statistic, this study referred to Kline [66] to verify standard normal distribution. In this study, no skewness of variables exceeded the absolute value of 1, and no kurtosis of variables exceeded the absolute value of 7, which means that the normal distribution has been formed in this study, as shown in Table 3. ## 4.4. Confirmatory Factor Analysis The confirmatory factor analysis was employed in this study to verify the reliability and validity of scales [67]. ## 4.4.1. Analysis of Reliability Reliability analytical results of this study indicate the accuracy of tools, or whether the measurement tools can be retested and have internal consistency. Cronbach’s alpha was used to check the internal consistency of questionnaire variables and the correlations between modified items and questionnaire variables. According to the results, the Cronbach’s alpha of attitude was 0.873, and its relevant modified coefficient ranged between 0.732 and 0.782; hence, all of the items were retained. The Cronbach’s alpha of subjective norm was 0.881, and its relevant modified coefficient ranged between 0.749 and 0.781; hence, all of the items remained. The Cronbach’s alpha of perceived behavioral control was 0.881, and its relevant modified coefficient was 0.761–0.780; hence, all of the items were retained. The Cronbach’s alpha of self-actualization was 0.870, and its relevant modified coefficient was between 0.592 and 0.798. Hence, all of the items remained. The Cronbach’s alpha of health responsibility was 0.815, and its relevant modified coefficient was 0.511 to 0.740; hence, all of the items were retained. The Cronbach’s alpha of sports was 0.754, and its relevant modified coefficient was within the range of 0.470 to 0.613. All of the items remained. The Cronbach’s alpha of nutrition was 0.858, and its relevant modified coefficient ranged between 0.596 and 0.804, so all the items were retained. The Cronbach’s alpha of interpersonal support was 0.815, and its relevant modified coefficient was within the range of 0.544 to 0.705. Hence, all of the items were retained. The Cronbach’s alpha of pressure treatment was 0.845, and its relevant modified coefficient ranged between 0.604 and 0.765, so all the items remained. The Cronbach’s alpha of perceived risk was 0.903, and its relevant modified coefficient was 0.795 to 0.823; hence, all of the items were retained. The Cronbach’s alpha of behavior intention was 0.735, and its relevant modified coefficient ranged between 0.496 and 0.604, so all the items were kept [67]. ## 4.4.2. Analysis of Validity The validity analysis was based on the composite reliability (CR) of dimensions and average variance extract (AVE). Previous studies pointed out that when the value of CR is above 0.7 and the value of AVE is over 0.5, the research questionnaire has convergent validity. After conducting the convergence validity tests on the planned behavior scale, perceived risk scale, and health-promoting lifestyle scale, the test results showed that all factor loads of the dimensions were between 0.54 and 0.90, the value of CR was within the range of 0.74 to 0.88, and the value of AVE was between 0.50 and 0.72, showing good validity [67,68,69], as suggested in Table 4. ## 4.5. Analysis of Structural Relationship Model To ensure the effectiveness of the constructed model, this study carried out the goodness test following the studies of [65,70,71,72,73,74]. Results suggested that CMIN/DF = 4.93, $$p \leq 0.000$$, GFI = 0.82, CFI = 0.82, AGFI = 0.80, RMSEA = 0.1, TLI = 0.80, and NFI = 0.80. Hence, the overall goodness of the constructed measurement model is acceptable. ## 4.5.1. Path Analysis The path coefficients of each dimension in this study are illustrated in Table 5. Based on the results, the standardized path coefficient of HPL on behavior intention was 0.14 ($$p \leq 0.026$$), which supported H1. The standardized path coefficient of attitude on behavior intention was 0.64 ($p \leq 0.000$), which verified H2. The standardized path coefficient of subjective norm on behavior intention was 0.29 ($$p \leq 0.027$$), which proved H3. The standardized path coefficient of perceived behavioral control on behavior intention was 0.35 ($$p \leq 0.009$$), which proved H4. ## 4.5.2. Analysis of Moderating Effects of Perceived Risk As for the moderating effects of perceived risk adjustment, compared with other research methods such as ANOVA or regression analysis, SEM can measure the influence of errors and estimate the main moderating effects with higher explanatory power [75]. Although this study only focused on the moderating effect of potential continuous variables, if the moderator is an explicit category variable, the data can be divided into sub-groups and processed by Structural Equation Modeling [76]. The analysis of moderating effects of perceived risk was suggested in Table 5. The standardized path coefficient of the effect of perceived risk between HPL and behavior intention was −0.78 ($$p \leq 0.009$$), proving H5. The standardized path coefficient of the effect of perceived risk between attitude and behavior intention was 0.20 ($$p \leq 0.026$$), which proved H6. The standardized path coefficient of the effect of perceived risk between subjective norm and behavior intention was 0.34 ($$p \leq 0.022$$), which proved H7. The standardized path coefficient of the effect of perceived risk between perceived behavioral control and behavior intention was 0.32 ($$p \leq 0.019$$), which proved H8. ## 5. Discussion and Implications The widespread of COVID-19 highlighted the importance of human behaviors in controlling disease transmission. At the start of the COVID-19 pandemic, people were not vaccinated. Nondrug preventive measures, such as wearing masks, washing hands, and keeping social distance, are important and cost-effective ways to control the pandemic. During the COVID-19 pandemic, the government of Taiwan closed indoor sports halls, bringing considerable difficulties to the practitioners. Even though the situation has improved during the post-pandemic era, the number of returned athletes is not as much as before. Hammerschmidt, Durst, Kraus, and Puumalainen’s [77] research shows that the biggest problem for clubs during COVID-19 is liquidity, and the crisis brought by COVID-19 will quickly threaten their operations [78]. Therefore, it is urgent to discuss clubs and athletes. This study was employed to strengthen the concept of behavior role exploration previously conducted by [35,79], enabling sports complex and post-pandemic era athletes to develop and build favorable interactive relationships. In this research, a complete TPB model was developed to explain the key structure for athletes in deciding the intention of using sports complexes during COVID-19, and the model was further improved to be more complete through HPL and perceived risk. Based on the results, athletes’ attitudes have the greatest impact on behavior intention, and the hypotheses relating to athletes’ intentions of using sports complexes have been verified. Most importantly, referring to this study, while an athlete’s attitude, subjective norm, perceived behavioral control, and HPL affect their willingness to use sports complexes, perceived risk can moderate an athlete’s intention to use sports complexes. ## 5.1. Theoretical Implications First, this study effectively added HPL into the TPB model to explore the athletes’ behavior intentions of using sports complexes. Past studies mainly investigated the direct effects of attitude, subjective norm, and perceived behavioral control on behavior intention. As the public usually has expectations and requirements for their health before using the sports complex, it is an important innovation for this study to add athletes’ perceptions of HPL on behavior intentions. Secondly, according to the results, perceived risk is crucial to an athlete’s behavior intention. Moreover, perceived risk significantly modifies health-promoting lifestyle cognition, attitude, subjective norm, perceived behavioral control, and behavior intention. Among them, the most influential is the moderating effect of health-promoting lifestyle cognition on behavior intention. We discovered that athletes’ use of sports complexes can be moderated by COVID-19. The results showed that COVID-19 has greatly affected and changed the lifestyle of athletes and has caused a considerable impact on the use of sports complexes. Hence, this study can also be a reference for operators of sports complexes. Finally, this study acted as an important reference to identify factors affecting athletes’ intentions to use sports complexes. Based on the results, attitude can significantly impact athletes’ visits to the sports complex. Moreover, past studies have revealed the influence of attitude on behavior intention [41], which indicated that when the feelings of athletes about using sports venues are more valuable, interesting, or beneficial, they will be able to stimulate a focus variable to use sports complexes. As for health-promoting lifestyle cognition, its impact on behavior intention is not the biggest, but with the moderator of risk cognition, its impact can be negative, as indicated in previous research reports [36,80]. Hence, it can be concluded that, due to COVID-19, the original positive impacts of athletes’ self-actualization, health responsibility, sports activities, interpersonal support, and pressure on athletes’ intentions of using sports complexes can be transformed into negative results. This noteworthy finding indicated that, although these athletes have the correct awareness of health-promoting lifestyle and are ready to enter sports complexes for exercise, they are reluctant to do so as COVID-19 weakens their universal intention [48,49]. ## 5.2. Practical Implications The results affected the management of sports complexes. First, it will be valuable to improve athletes’ attitudes about sports complexes or to promote sports education. In addition, improving athletes’ sports values and attitudes can be an important factor in triggering behavior intention. Even so, managers of sports complexes or centers should be quite cautious to avoid leaving bad impressions by exaggerating their words. The challenges faced by those managers is to provide athletes with an information balance during the marketing, and allow athletes to experience activities honestly and transparently. Moreover, this study discovered that perceived risk can affect users’ behavior intentions, which can be an important reminder for complex sports managers. In other words, athletes’ attitudes, subjective norms, perceived behavioral control, and health-promoting lifestyle cognition positively affects the sports complex’s behavioral intentions. However, these variables will adjust athletes’ behavior intentions based on their perceived risks caused by virus infection. For example, according to Zhang et al. [ 81], through high perceived risks, even high control levels, high subjective norms, or original behaviors can be altered due to fear. Furthermore, since our study has revealed the important role of perceived risk, the government and enterprises should act to reduce athletes’ perceived risks in the use of sports complexes during the post-pandemic era. The government should advocate the right anti-pandemic measures to lower the risk of exercise for athletes, while practitioners should perform various cleaning and disinfection policies to improve the athletes’ sense of safety. More specifically, the government can use mass media to spread related norms that should be followed during the exercise process, making athletes feel certain about not acquiring an obtaining infection by using correct prevention concepts. For enterprises, they should strictly enforce government regulations and continue to provide adequate guarantees for the safety of sports complexes and facilities to attract more customers. Thirdly, according to the results, managers can formulate effective marketing strategies based on athletes’ health-promoting lifestyle cognition to improve athletes’ behavior intentions. For instance, Nimri et al. [ 82] emphasized the key role of environmental protection in affecting individuals’ decisions to choose green hotels. Therefore, marketing personnel of sports centers should carry out advertising activities to advocate the importance of a health-promoting lifestyle, which will make athletes believe that they should exercise, considering their demands for a healthy lifestyle. ## 5.3. Limitations and Future Research As this study only targeted the athletes of Taiwanese sports complexes, future studies can make a cross-comparison to focus on cross-cultural athletes. Additionally, each operating model of a sports complex is special, and there are various kinds of sports complexes in Taiwan. This study only touched on the behavior intention of athletes towards sports complexes, but how the athletes react when located in different operating models can also be worthy of future discussion. Moreover, under the impact of COVID-19, whether the moods of athletes and operators can adapt to the changes, and whether athletes’ requirements for operators can be changed into the consumption mode before the COVID-19 when COVID-19 is tamed, all deserve investigation. It is suggested that the follow-up research can conduct multiple comparisons on how different sports complexes can affect athletes’ behavior intentions. Additionally, in the future, studies can evaluate whether the results of this research can still be effective if the external environment changes, which can provide more in-depth business insights into the management of sports complexes. Since athletes’ behavioral intentions for sports complexes were revealed, future research can re-examine what products or services are essential for athletes and the price rationality they agree with to provide athletes with acceptable leisure and entertainment products and sports services. ## 6. Conclusions In this research, the following research conclusions are obtained Theory of Planned Behavior (TPB) and Health-Promoting Lifestyle (HPL), investigating athletes’ behavioral intentions regarding sports halls and perceived risks as interfering variables. The Health-Promoting Lifestyle cognition, attitudes, subjective norms, and perceived behavioral control has a positive and significant effect on behavioral intention; The risk perceptions have an interference effect between HPL, attitude, subjective norm, perceived behavioral control, and behavioral intentions. ## References 1. Tsorbatzoudis H., Alexandres K., Zahariadis P., Grouios G.. 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--- title: The Effect of Serum Leptin Concentration and Leptin Receptor Expression on Colorectal Cancer authors: - Sylwia Chludzińska-Kasperuk - Jolanta Lewko - Regina Sierżantowicz - Elżbieta Krajewska-Kułak - Joanna Reszeć-Giełażyn journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048899 doi: 10.3390/ijerph20064951 license: CC BY 4.0 --- # The Effect of Serum Leptin Concentration and Leptin Receptor Expression on Colorectal Cancer ## Abstract Introduction: The level of leptin in the blood shows a positive, strong correlation with the mass of adipose tissue. Being overweight and having metabolic disorders increase the risk of developing colorectal cancer. Aim of the Paper: The aim of the study was to assess the concentration of leptin in the blood serum as well as the expression of the leptin receptor in colorectal cancer cells. In addition, the effect of serum leptin concentration and leptin receptor expression on clinical and pathological parameters such as BMI, obesity, TNM, and tumor size was assessed. Methods: The study included 61 patients diagnosed with colorectal cancer and treated with surgery. Results: Strong leptin receptor expression and the prevalence of overweight and obesity are factors influencing the occurrence of excessive leptin concentrations. Conclusion: Leptin may be involved in the development and progression of colorectal cancer. More research is needed to better elucidate the role of leptin in the development and progression of the disease. ## 1. Introduction Colorectal cancer (CRC) is the third most common cancer and the fourth most common cause of death in the world. In addition to the aging of the population and eating habits, adverse risk factors, such as obesity, a lack of physical activity, and smoking, increase the risk of CRC [1,2,3]. Advances in the pathophysiology of CRC have increased the number of treatment options for CRC and led to the creation of individual treatment plans for patients [1,3,4]. Although the new treatment options have doubled the overall survival for advanced disease to 3 years, survival for CRC patients is still the best for those with non-metastatic disease. Since the disease only shows symptoms at an advanced stage, worldwide screening programs are being implemented to increase early detection and reduce the incidence and mortality rate of CRC [1,3,4,5]. The early diagnosis of CRC is very important, as it allows the cancer to be detected at an early stage, when there is a very good chance of a cure. Colonoscopy is the gold standard in the diagnosis of CRC. It has high diagnostic accuracy and can determine the exact location of the tumor. Importantly, this technique allows the simultaneous collection of tumor tissue material and, thus, the histological confirmation of the diagnosis and the collection of material for molecular tests [6]. New treatment options for colorectal cancer include immunohistochemical and histoenzymatic techniques (immunohistochemical identification of cytosolic cytochrome, histoenzymatic analysis of the activities of energy metabolism enzymes) [7]. In the diagnosis of CRC, radiology has various applications, such as ultrasound, computed tomography, positron emission tomography, and magnetic resonance imaging. Depending on the stage of CRC, a combination of treatments can be used: surgery, chemoimmunotherapy, and radiotherapy. Therefore, it is advisable not only to coordinate multidisciplinary treatment but also to quickly implement diagnostic procedures [8,9]. It is the second most common cancer diagnosed in women and the third among men. In women, the incidence and mortality of CRC are approximately $25\%$ lower than in men. These indicators vary geographically, with the highest rates observed in the most developed countries. Stabilizing tendencies and decreasing incidence of CRC are observed only in highly developed countries [3,9]. Environmental factors are associated with the occurrence of cancer and account for approximately $83\%$ of all colorectal cancer cases. Among these patients, the only environmental impact is detected in $30\%$. In $28\%$ of them, the cancer is associated with epigenetic mutations, such as DNA repair genes; the remaining $25\%$ of patients have a positive family history of CRC [10,11,12]. Being overweight and having metabolic disorders increase the risk of developing CRC. Studies indicate a clear correlation between body mass index (BMI) and the incidence of CRC. The risk of CRC increases with an increase in BMI in the range of 23 to 30 kg/m2. In adults with a BMI above 30 kg/m2 compared with people with a BMI of less than 23 kg/m2, this risk increases by 50–$100\%$. An increase in BMI of 5 kg/m2 significantly increases the risk of CRC, and in people with a BMI above 30 kg/m2, it is already approximately $40\%$ [5,13,14,15]. The exact mechanism by which obesity increases the risk of developing CRC is not known. Scientific research takes into account the mitogenic effects of insulin, insulin resistance, and hyperinsulinemia. The role of insulin in the carcinogenesis of CRC may result from its direct or indirect effect on increasing the concentration of IGF-1 in the blood serum, which stimulates proliferation by inhibiting the apoptosis of mutant cells [15,16,17]. Many epidemiological data prove that obesity increases the risk of CRC. Although the molecular mechanisms underlying this compound are not known, data from in vitro experiments suggest a direct contribution of adipose tissue to the development of CRC. The main factor affecting serum leptin levels is body fat mass. The level of leptin in the blood shows a positive, strong correlation with body fat mass. In people with obesity, the level of leptin in the blood is almost 10 times higher than in people with a normal body weight. The results of research in recent years indicate that leptin is also produced by cancer cells of the large intestine and breast [18,19,20,21,22]. Numerous epidemiological studies indicate obesity as a significant risk factor conducive to the initiation of the cancer process as well as a prognostic factor associated with prognosis in people affected by cancer. Obesity has been proven to be an important prognostic factor for cancers of the mammary gland, uterine body, colon, and prostate. Given the positive correlation of leptin levels with body fat mass, more research should be conducted to investigate the contribution of leptin to the tumorigenesis process [18,19,20,21,22]. The aim of the study was to assess the concentration of leptin in the blood serum as well as the expression of the leptin receptor in colorectal cancer cells. In addition, the effect of serum leptin concentration and leptin receptor expression on clinical and pathological parameters such as BMI, obesity, TNM and tumor size was assessed. The study attempted to clarify the role of leptin as a new biomarker for CRC. ## 2. Material and Methods The study involved a total of 61 patients successively admitted to the II Clinic of General and Gastroenterological Surgery of the University Clinical Hospital in Bialystok with a diagnosis of CRC treated surgically in the period 2018–2020. The study was conducted after obtaining written consent from patients regarding biological material (blood and tumor tissue) and the clinical data of patients with CRC. Patients diagnosed with CRC were included in the study, whereas patients with a previous history of cancer and patients using hormonal treatments were excluded. ## 2.1. Experimental Designs For the research, 1.6 mL of serum was stored at −80 °C till the tests were performed, and peripheral cancer tissue stored in paraffin blocks was used. The comparison group for serum leptin values consisted of a total of 60 patients (33 females and 27 males aged 35–60 years): 30 patients without cancer with a normal BMI who were not using hormonal treatment, and 30 patients without cancer with obesity I° BMI > 35, who were also not using hormonal therapy. The control to assess the expression of the leptin receptor in the tissue consisted of slices of normal intestinal mucosa taken from 20 patients (8 females and 12 males aged 45–65 years with a normal BMI) with diverticulosis of the large intestine stored in paraffin blocks. Clinical–pathological data were also analyzed. Serum leptin concentrations were tested using the Human Leptin ELISA Kit. Immunohistochemical methods were used to evaluate leptin expression in peripheral tumor tissues. After resection of the large intestine, the collected tissue samples were fixed in a $10\%$ buffered formaldehyde solution, then immersed in paraffin blocks and stained with hematoxylin and eosin. Immunohistochemical studies were performed to evaluate the expression of the leptin receptor in colorectal cancer samples and normal colorectal mucosa samples. The tissue thickness was 4 μm. Leptin was investigated in representative tissue sections using specific antibodies for leptin and polyclonal antibodies A-20. All primary antibodies were diluted in phosphate-buffered saline with $1.5\%$ normal blocking serum. The antibody–antigen reaction was revealed using an avidin–biotin–peroxidase complex for leptin, and then the slides were counterstained with hematoxylin. The assessment of leptin receptor expression in cancer tissue was carried out by two separate pathologists. The expression of leptin was analyzed using light microscopy. The immunostaining for leptin was analyzed in 10 different fields. The mean percentage of tumor cells with positive staining was calculated. Positively colored cells were counted in 10 representative high-power fields and classified as follows: negative (−) with ≤ $10\%$ positive cells, positive (+) with 11–$49\%$ positive cells (focal and moderate expression), and highly positive (++) with ≥$50\%$ positive cells (strong and diffuse expression). The counts were conducted in a set of 10 random fields at ×20 magnification. ## 2.2. Statistical Analysis The Mann-Whitney test, Kruskal-Wallis test, Student’s t-test, chi-square test of independence, Spearman’s rank correlation coefficient, and multivariate variance analysis were used for statistical analysis [23]. The statistical package STATISTICA 13 by Statsoft was used for the calculations. ## 3.1. Characteristics of Study Group The study included 61 patients diagnosed with CRC; in the study group, there were $63.9\%$ male patients and $36.1\%$ female patients. The average age of all subjects was 70.5 ± 10.3 years, and the average BMI was 27.7. It was observed that $29.5\%$ (18 people) of the study group were obese, $34.4\%$ (21 people) were overweight, and $36.1\%$ (22 people) had a normal body mass index (BMI). In the study group, more than half of the surveyed patients ($57.4\%$: 35 people) smoked cigarettes; alcohol was consumed by just over a fifth of patients ($21.3\%$: 13 people). More than half of the people in the study group had hypertension, one in three had type II diabetes, about one in six people had cardiac arrhythmia and coronary heart disease, and one in ten men had benign prostatic hyperplasia. Approximately one in six people was found to have no comorbidity. ## 3.2. Leptin Receptor Expression The study group was dominated by patients whose tumor size was over 3 cm. This size of the tumor was found in $49.2\%$ of the subjects in the study group. All patients in the study group had positive leptin receptor expression in the tumor tissue. Almost $60\%$ of patients had strong (++) leptin prescription expression. Leptin receptor expression was undetectable in the study samples of patients in the comparison group. No leptin receptor expression was detected in the normal intestinal mucosa in all 20 tested scraps of normal intestinal mucosa. However, no significant correlation was found between leptin receptor expression and clinical–pathological parameters (Table 1). Figure 1 shows the observed incidences of each leptin receptor expression level observed in both groups, along with $95\%$ confidence intervals. In the 20-person comparison group, no case of moderate or strong leptin receptor expression was observed. It does not necessarily result in the absence of such cases in a larger population, as can be seen from the chart below. With $95\%$ confidence, it may be said that the percentage of such cases in the entire population of people with other diseases of the large intestine is no more than approximately $18\%$. Similarly, with $95\%$ confidence, it can be concluded that in the group of patients with CRC, the occurrence of a low expression of the leptin receptor concerns a percentage of less than approximately $7\%$ ($$p \leq 0.00$$). For factors of a numerical (e.g., age and BMI) or ordinal (TNM stage) nature, Spearman’s rank correlation coefficient was used to analyze their effect on leptin levels. In the case of comparing the concentration of leptin with the levels of the grouping factor (e.g., expression of the leptin receptor), the analysis consisted of determining descriptive statistics in the compared groups and assessing the differences between the groups using the Mann-Whitney test (for two groups) or its generalization—the Kruskal-Wallis test (for more than two groups). Among the cancerous tissues, all of the 61 ($100\%$) samples were stained positive or highly positive via the immunohistochemical technique. In contrast, all of the 20 normal colorectal tissue were negatively stained. The difference in the leptin receptor expressions between the cancerous and normal colorectal mucosa were highly significant ($p \leq 0.01$, chi-squared). The results of this study suggest that leptin may be involved in the development of colorectal cancer. ## 3.3. Serum Leptin Concentrations Table 2 provides information on the serum leptin concentrations; since the level of this hormone depends on sex, detailed descriptive statistics are stratified for sex. The average leptin concentration among women is more than 2.5 times higher than that among men. The above summary shows that $42.6\%$ of patients had above average leptin concentrations. For men, in the case of a higher expression of the leptin receptor, there is also a much higher concentration of leptin (the median leptin concentration value for a moderate expression of the leptin receptor is 3.40 ng/mL, and for a strong one, 6.23 ng/mL). This difference is statistically significant ($$p \leq 0.0032$$) (Figure 2). Figure 3 shows the relationship between the age of patients and serum leptin concentration; the analysis was carried out taking into account the sex variable. A statistically significant correlation was found between age and leptin concentration among men: with age, leptin concentration decreases. However, this is not a very strong dependency (rs = −0.33). Similarly, the results of the correlation analysis between BMI and the concentration of leptin are presented in Figure 4 and Table 3. Statistically significant and relatively strong (and for women, even very strong rs = −0.83) relationships between BMI and leptin concentration were obtained. The higher the BMI, the higher the concentration of leptin in the blood serum. The analyses show that the concentration of leptin did not correlate statistically significantly with the degree of histological differentiation, lymph node metastases, the stage of TNM, tumor size, the occurrence of cancer in the family, or lifestyle. Type II diabetes is also not a statistically significant factor affecting leptin levels. It was also examined whether the percentage of people who experienced above-average leptin concentrations depended on selected factors. Since the average leptin concentration takes into account the specificity of sex, the analysis can be carried out at the level of the whole community, including both women and men. To assess the significance of differences between groups in the participation of individuals with an oversized concentration of leptin, a chi-squared independence test was used. Strong leptin receptor expression and the manifestation of overweight and obesity are factors influencing the occurrence of excessive leptin concentrations. Both of these relationships are statistically significant (test probability value $p \leq 0.05$) (Table 4). First, a comparison of leptin concentrations in the study and comparative groups was performed based on the Mann-Whitney test. The analysis was carried out separately for men and women. In this analysis, it was not possible to find statistically significant differences in the level of leptin concentration in the study and comparison groups (Table 5). In the proposed approach, the difference between the groups was also statistically significant. Based on the value of descriptive statistics, it can be concluded that the concentration of leptin is significantly higher in the comparison group than among people with CRC (Figure 5). The factor associated with leptin concentration is BMI. The study used a regression model with three variables: being a member of the study or comparative group, sex, and BMI. The calculation was carried out for the concentration of leptin subjected to a logarithmic transformation (Table 6). The results of the analysis are presented below. It is evident that the concentration of leptin is higher for women and increases with BMI. On the other hand, the negative coefficient and high significance of the difference between the study and comparative groups confirm previous analyzes, which resulted in a lower concentration of leptin in the study group. The regression model formula for the log leptin concentration is as follows:LOG (leptin concentrations) = −1.42 − 0.318·Group (study vs. comparative) + 1.075 Gender (Female vs. Male) + 0.112·BMI However, after performing the inverse logarithmic transformation, the following form of this equation is obtained:Leptin concentration = 0.24 (Study Group) 0.728 (Female) 2.930·(BMI)1.119 The above results can be interpreted as follows: in women, the concentration of leptin is 2.93 times higher than that in men; an increase in BMI of 1 causes an elevation of 1.12 times in leptin concentration; and in the study group, the concentration of leptin is 0.728 (i.e., by $27.2\%$) less than in the comparison group. An illustration of the results of the above analyses can be found in the following figures, which show the relationship between BMI and leptin concentration (in logarithmic form) in the study and comparative groups—separately for women and men. The graphs below confirm the fact that there is a lower concentration of leptin in the study group, although they also allow us to see other interesting regularities, i.e., that the concentration of leptin is lower in the group of women studied for low BMI values, while in the case of a high BMI, such a difference does not occur. However, for men, the situation is the opposite—for low BMI values, the difference in leptin concentration between the test and comparison groups is small, while the lower level of leptin concentration in the study group appears with increasing BMI values (Figure 6). ## 4. Discussion Epidemiological data indicate that obesity is a risk factor for the development of CRC [5,10,24]. However, the possible molecular mechanisms responsible for this phenomenon are unclear. In this context, the correlation between the obesity hormone leptin and CRC has been studied in recent years. Studies using animal models and epidemiological data have shown controversial results, with a positive or negative correlation between serum leptin concentrations and CRC. In addition, some research has indicated that serum leptin levels are not linked with CRC [24,25]. Leptin is a peptide hormone with a molecular weight of 16 kD, which in adults is produced mainly in adipose tissue. At the same time, leptin, in much smaller amounts, is also secreted in numerous extrafatual tissues: the lungs, breast, gastric mucosa, brain, placenta, prostate, testicles, ovaries, and endometrium. In the study of Zaha et al. describe the potential role of leptin in inducing pro-inflammatory markers expression. C reactive protein is associated only with obesity, not with the metabolic syndrome. Therefore, assessment of adiponectin in the population could help identify patients with high risk of diabetes mellitus and cardiovascular disease [26,27,28,29,30,31]. Leptin is released cyclically, usually 2–3 h after a meal, and its serum concentration is directly correlated with the amount of body fat. It has been discovered that when there is an increase in the number and size of adipocytes, the leptin gene begins its production, which is then secreted into the bloodstream. Numerous reports from the literature show that leptin plays a crucial role in the progression and pathogenesis of CRC [32,33,34], while the research of Tutino et al. [ 34] has shown that high serum leptin levels are an independent risk factor for the development of CRC. Leptin receptor expression occurs in many cancer cells, including colorectal cancer cells. Literature reports in recent years show that leptin receptor expression is positive in approximately 77 to $95.5\%$ of patients with CRC [19,35,36,37,38]. Obesity increases the risk of cancer formation through molecular mechanisms resulting from excessive amounts of adipose tissue and through the coexistence of hyperinsulinemia and hyperlipidemia directly related to lifestyle. Adipose tissue is composed of subcutaneous fat secreting large amounts of leptin and visceral fat, which are more hormonally active and secrete biologically active compounds, i.e., adiponectin, IL-6, and resistin [39,40], TNF-α (tumor necrosis factor), visfatin, or PAI-11 (plasma activator inhibitor). Under the physiological conditions of normal body weight, the appropriate proportions between individual substances are maintained, which is beneficial for human health and enables the proper functioning of the body. In obesity, there is an imbalance between them, which may result, among other complications, in the development of a chronic inflammatory process and insulin resistance [39,40,41]. Many pro-inflammatory cytokines increase the concentration of TNFα and IL-6, which are the key cytokines in cancer progression. Tumor necrosis factor activates the nuclear factor (NF-kB nuclear factor) by binding to the TNF receptor, blocking apoptosis, and increasing the proliferation of neoplastic cells. Interleukin 6 sends signals to the cell nucleus via a signal transducer to Transcription Activator 3 (STAT3), an oncoprotein activated in many cancerous tumors. The excess of adipose tissue is one of the factors determining carcinogenesis in obesity [39,40,41]. The second factor is the lifestyle of overweight and obese people—a lack of physical activity and excess energy supplied from food in relation to the demand and quality of meals consumed. A study [42] of nearly 3500 adults showed that obese people lead a less active lifestyle compared with people with a normal body weight. Physical activity is an indispensable element of cancer prevention as it influences the immune system by alleviating inflammation, lowering the concentration of sex hormones, reducing body weight, and improving intestinal peristalsis [42]. In the study of S Vuletic et al. [ 26], the intracytoplasmic and intramembrane expression of the leptin receptor was verified in a significant number of cases ($77.3\%$), with pronounced leptin receptor expression in approximately one-third of the cases ($33.3\%$). The study [26] showed that leptin receptor expression was significantly associated with lymph node metastases and distant metastases. There was no significant association [26] of leptin receptor expression with the patient’s demographic characteristics, which was consistent with the results of Wang et al. [ 34], who studied leptin receptor expression in colorectal cancer with regard to demographic parameters. The researchers Koda et al. [ 43] showed a statistically significant positive correlation of leptin receptor expression with female sex and an age of over 60 years. The study of Al-Shibli et al. [ 44] further highlights the possible role of leptin receptor expression in CRC, as well as the prospect of using leptin receptor expression as a possible therapeutic target. Research by Al-Shibli et al. [ 44] is consistent with the research of Koda et al. [ 45]. To date, there is no other report that has found such a frequent occurrence of leptin receptor expression in CRC or any other cancer. The closest numerical incidence of leptin receptor expression was reported by Al-Maghrabi et al. [ 46], where positive leptin receptor expression was observed in $93.5\%$ of cases of CRC in the Western Province of Saudi Arabia. Koda et al. [ 45] reported that leptin cells are overexpressed in CRC relative to normal colon mucosa in their study. Leptin is associated with carcinogenesis and the progression of various cancers. However, changes in serum leptin levels in patients with CRC and their relationship to the treatment response in these patients have rarely been studied [47,48]. In the study of Wang et al. [ 49] on CRC, a correlation was found between the serum leptin concentration and focal leptin receptor expression in tumor tissue. The serum leptin concentrations of patients with CRC were significantly higher compared with those of the control group (22.67 ± 12.56 vs. 12.68 ± 7.8 ng/mL; $p \leq 0.05$, respectively). In addition, leptin levels after surgery decreased compared with preoperative levels (18.67 ± 8.54 vs. 22.67 ± 12.56 ng/mL; $p \leq 0.05$, respectively). In summary, leptin levels were elevated in overweight and colorectal cancer patients. Leptin concentration decreased after colectomy, indicating that leptin may be associated with colon carcinogenesis. Research by Wang et al. [ 49] argues that serum leptin levels can be used for early diagnosis and the monitoring of colorectal cancer treatment response. Our studies did not show a positive correlation between leptin concentrations in patients with CRC and those in the comparison group. There were also no statistically significant differences between the leptin concentration and leptin receptor expression. The purpose of the study of Salagean et al. [ 50] was to investigate the relationship between several levels of adipocytokines in the blood and the clinical–pathological characteristics of colorectal cancer patients undergoing surgery. Resistin levels were significantly higher in patients with colon cancer, while serum leptin levels were significantly lower compared with the controls. Leptin levels dropped gradually as the tumor progressed. In conclusion, the results of this study suggest that adipokines, in particular, resistin and leptin, may be involved in the development and progression of CRC. The relationship between serum leptin levels and the risk of CRC remains controversial. In the study of Wang et al. [ 49], significantly lower serum leptin concentrations were found in patients with CRC in contrast to the control group. In addition, a significant correlation was observed between serum leptin concentration and cancer staging based on TNM classification ($$p \leq 0.021$$). ## The Limitation of the Study Obesity increases the risk of many types of cancer, including colorectal cancer. Although the molecular mechanisms underlying this compound are not known. The results of this study also do not demonstrate the molecular mechanism. The issue is that many things are altered by obesity. But it is very hard to know if the changes that are being observed are due to changes in leptin and leptin receptors, or something else entirely. For instance, other factors produced by adipose tissue are also likely to be increased and could also be driving the changes that are observed independently of leptin. Thus, in vitro experiments on colorectal cancer cells would be necessary to observe the effect of leptin on cancer development and progression. Above studies is the small size of the study group and the comparison group. Perhaps a statistical analysis of a larger group of colorectal cancer patients would lead to more precise conclusions. ## 5. Conclusions In conclusion, the results suggest that leptin may be involved in the development and progression of colorectal cancer. In order to better clarify the role of leptin in the development and progression of the disease, more research is needed. ## References 1. Brenner H., Kloor M., Pox C.P.. Colorectal cancer. Lancet (2014) 383 1490-1502. DOI: 10.1016/S0140-6736(13)61649-9 2. Witold K., Anna K., Maciej T., Jakub J.. 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--- title: How Did People with Prediabetes Who Attended the Diabetes Prevention Education Program (DiPEP) Experience Making Lifestyle Changes? A Qualitative Study in Nepal authors: - Pushpanjali Shakya - Monish Bajracharya - Eva Skovlund - Abha Shrestha - Biraj Man Karmacharya - Bård Eirik Kulseng - Abhijit Sen - Aslak Steinsbekk - Archana Shrestha journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048900 doi: 10.3390/ijerph20065054 license: CC BY 4.0 --- # How Did People with Prediabetes Who Attended the Diabetes Prevention Education Program (DiPEP) Experience Making Lifestyle Changes? A Qualitative Study in Nepal ## Abstract Diabetes can be prevented through lifestyle modification in the prediabetic phase. A group-based lifestyle intervention called ‘Diabetes Prevention Education Program’ (DiPEP) was tested recently in Nepal. The present study aimed to explore experiences of making lifestyle changes among people with prediabetes participating in the DiPEP. This qualitative study, with semi-structured interviews of 20 participants, was conducted 4–7 months following DiPEP intervention. Data analysis was performed by thematic analysis. The results included four themes, understanding that diabetes could be prevented, lifestyle changes made, hurdles to overcome, and experiencing benefits leading to sustained change. Some participants said they felt relieved to know that they had a chance to prevent diabetes. The participants talked mostly about making changes in diet (reducing carbohydrate intake) and physical activity (starting exercises). Obstacles mentioned included a lack of motivation and a lack of family support to implement changes. Experiencing benefits such as weight loss and reduced blood sugar levels were reported to lead them to maintain the changes they had made. Understanding that diabetes could be prevented was a key motivator for implementing changes. The benefits and hurdles experienced by the participants of the present study can be taken into consideration while designing lifestyle intervention programs in similar settings. ## 1. Introduction Prediabetes is a health condition with higher than normal blood glucose level [fasting plasma glucose 100–125 mg/dL or 2-h plasma glucose 140–199 mg/dL or glycated hemoglobin (HbA1c) 5.7–$6.4\%$], but not yet meeting the threshold of diabetes diagnosis [1]. The prevalence of prediabetes is increasing worldwide, with the highest age-adjusted prevalence in low-income countries [2]. In Nepal, the prevalence of Impaired Fasting Glucose (IFG) and Impaired Glucose Tolerance (IGT) were found to be $7.8\%$ and $5.4\%$, respectively, as per the International Diabetes Federation Atlas [2], while other previous studies showed the prevalence of prediabetes determined by IFG and IGT ranged from 1.3–$19.4\%$ [3,4,5,6,7]. A meta-analysis has shown that the prevalence of prediabetes in Nepal was $9.2\%$ in 2020 [8]. In the urban settings of Nepal, the prevalence of prediabetes determined by HbA1c was $5\%$ [9]. The annual risk of development of type 2 diabetes (T2D) among individuals with prediabetes has been estimated to be 5–$10\%$ [10,11]. One of the strongest determinants of prediabetes is obesity [12], which is also associated with metabolic syndrome [13]. The associated comorbidities of prediabetes increase health care expenditure and deteriorate the quality of life [14] as well as increase mortalities [15,16]. T2D is one of the fastest-growing global health emergencies of the 21st century, causing an estimated 6.7 million deaths in 2021 [2]. The prevalence of T2D was $10.3\%$ in Nepal as per meta-analysis and systematic review [17]. In the context of developing countries with out-of-pocket healthcare expenditure systems, such as Nepal [18], the economic burden associated with the disease is carried by the individuals and their families [19,20]. Considering all the drawbacks of T2D, it is urgent to prevent or delay it. Prevention or delay of T2D is possible with non-pharmacological interventions, including a healthy diet and adequate physical activity targeting weight reduction during the prediabetes phase [21]. One such evidence-based intervention is the Diabetes Prevention Program (DPP) developed in the US, consisting of 16 sessions mostly conducted in clinical settings with follow-up by lifestyle coaches [22]. This program can lead to a 30–$60\%$ reduction in the incidence of T2D among adults with high risk [23] and, in some cases, conversion back to normoglycemia [24]. However, most of the studies included in a systematic review of lifestyle intervention programs were undertaken in developed nations [11]. There is a dearth of evidence on the effectiveness of community-level interventions in the context of Nepal. Though some national programs deliver diabetes management interventions, the prevention of diabetes has not been prioritized in Nepal [25,26]. Globally, qualitative studies have investigated perspectives on prediabetes education intervention among different stakeholders, including user groups [27,28,29]. There are also qualitative studies exploring perspectives of individuals with prediabetes making dietary changes and physical activity changes following the diagnosis of prediabetes and participation in lifestyle intervention programs [30,31,32,33,34,35]. These studies have revealed facilitators and barriers for lifestyle changes among people with prediabetes in different parts of the world [30,35]. However, such studies have mostly been conducted in high-income countries. Exploring the experiences and perceptions of the stakeholders, especially the users group, is substantial to emphasize the effectiveness of the interventions and also is important in improving the content and form of interventions. Such studies are scarce in countries with low-resource settings. To the researchers’ knowledge, no studies in Nepal have yet explored or reported experiences of people with prediabetes who underwent diabetes prevention intervention programs at the community level; such experiences can be helpful to reach people better and/or improve certain aspects of the interventions. Recently, a community-based cluster randomized controlled trial (RCT) investigating the effect of an intervention called “Diabetes Prevention Education Program (DiPEP)” was conducted in Nepal [36]. It was a group-based lifestyle intervention program designed with inspiration from the National Diabetes Education Program [37] and the DPP [22] from the USA. The aim of the DiPEP intervention was to prevent diabetes among people with prediabetes in the community settings of Nepal. The intervention targeted people with prediabetes and consisted of four one-hour weekly educational sessions and follow-ups for the next five months. The trial provided an opportunity to explore participants’ experiences of making lifestyle changes. Hence, the aim of the present qualitative study was to explore the experiences of making lifestyle changes among individuals with prediabetes who participated in the DiPEP intervention. ## 2.1. Study Design This was a qualitative study following a phenomenological approach with semi-structured interviews conducted at the individual-level from August 2020 to January 2021 as part of a larger project, including screening campaigns in the community to screen for individuals with prediabetes and a cluster RCT on the effect of the DiPEP intervention. The study protocol for the larger project is published elsewhere [36]. The consolidated criteria for reporting qualitative studies (COREQ): a 32-item checklist [38] was consulted to report this study. ## 2.2. Setting and Intervention The RCT took place in two urban community settings in Nepal (Patan and Dhulikhel) [9,36]. However, due to the practical consequences of the COVID-19 lockdown, the qualitative study included participants in the intervention arm of the RCT from Patan only. Patan is a core part of Lalitpur Metropolitan City located 5 km southeast of the capital Kathmandu, densely populated with a population of 284,922, a literacy rate of $80\%$, and an increasing trend of westernization [39]. As part of the larger project, screening campaigns were organized in the communities among the general public with the eligibility criteria: (i) a permanent resident of the study sites, (ii) age 18–64 years, and (iii) no self-reported history of diabetes [36]. Banners and verbal announcements using speakers about the screening campaigns in the Nepali language were used to let the general public know about the campaigns. The screening campaigns for the RCT were aimed at detecting persons with prediabetes, defined by HbA1c ranging from 5.7–$6.4\%$ [36]. Those identified as having prediabetes were informed that their blood sugar level was higher than normal, but they had not yet reached the threshold of diabetes. They were also provided with information about the RCT. The DiPEP is a 6-month, group-based intervention program in the community setting comprising four one-hour (30 min theory + 30 min practical) weekly educational sessions held during the first month of the intervention and follow-up for the next five months. The educational sessions were conducted physically in Patan prior to the COVID-19 pandemic and digitally during the lockdown phase. The topics of the four educational sessions were: (i) introduction to diabetes and prediabetes, (ii) healthy eating and physical activity, (iii) stress management, and (iv) management of social cues. The local and cultural context of Nepali users was considered during the development of the intervention, and the intervention was delivered in the Nepali language. Examples of considering local and cultural context included an emphasis on adjusting the sizes of portions of available food instead of asking the participants to introduce new types of food under the topic of ‘healthy diet’ and including different types of simple exercises that could be performed at home under the topic of ‘physical activity’. Five goals of the intervention were clearly conveyed at the beginning of the educational sessions. The goals were: [1] to prevent diabetes, [2] to reduce body weight by 5–$7\%$, [3] to perform at least 150 min of moderate exercise per week, [4] to gain support from family and friends, and [5] to overcome the obstacles during lifestyle changes. The researcher (PS) and study nurses conducted the educational sessions. Participants were provided with written materials consisting of a diabetes prevention education brochure (including a summary of the four topics of DiPEP educational sessions), an exercise calendar, and a food logbook [36]. The five months of follow-up of the participants prior to the pandemic included group-based weekly physical sessions conducted by community health care workers/volunteers (CHCW/Vs) and group-based monthly physical meetings with both the CHCW/Vs and the study nurses. The physical follow-up included measurement of weight and blood pressure, assessment of the food logbook and exercise calendar, doing four types of exercises, and a question–answer (Q&A) session. During the COVID-19 lockdown, these physical follow-ups were replaced with individual biweekly telephone calls by the CHCW/Vs and group-based monthly digital meetings conducted by the researcher (PS). In the first telephone call of the month, CHCW/Vs reminded participants about the DiPEP lessons and answered any questions asked by the participants; in the second telephone call, they also invited participants to the monthly digital meeting. The digital meeting included the revision of four topics, asking about the food logbook and the exercise calendar, doing four types of exercises (by displaying a video of the same exercises), and a Q&A session. ## 2.3. Study Participants Individuals included in the present qualitative study met the following inclusion criteria: prediabetes detected by HbA1c 5.7–$6.4\%$; age of 18–64 years; participation in the intervention arm of the RCT [9,36]; attendance at a minimum of one of the four educational sessions; access to the internet, as the interviews were performed digitally due to COVID-19 lockdown. To ensure diversity in the study sample, participants were purposively selected considering gender, education level, ethnic group, physical or digital participation in the intervention, and the number of educational sessions attended. The recruitment was performed by selecting the participants from the top of the attendance lists of the educational sessions. The order of participants on the lists was based either on ascending order of the code number that they had from the screening program or their seat position in the first educational session. Written consent was obtained before enrollment in the RCT and after the participants were informed about the interview and the study. Those participants selected for the interviews from the intervention clusters were contacted by phone or digitally. Once a participant agreed to take part in the interview, an information sheet with detailed information about the interview in the Nepali language was sent digitally, and an appointment for the digital interview was made. Participants were aware of the purpose of the interview and the person (PS) who would conduct the interview since the researcher (PS) had dual roles as an educator and as an interviewer. On the day of the interview, as physical contact was avoided due to the pandemic, the interviewer read the information sheet and consented again to the participants on a digital platform. Verbal consent was then obtained from the participants before the interview commenced. A total of 6222 participants participated in the screening campaigns, and 308 had prediabetes. Out of these, 291 were enrolled in the RCT, and 159 were recruited in the intervention arm. Only 73 participants attended at least one DiPEP educational session and were thus eligible for the present qualitative study. Among these, 31 participants were approached for the interview; these were selected from the list of participants in the attendance sheet of the educational sessions and also fit the inclusion criteria. Recruitment was stopped when 20 persons had been interviewed, and data saturation had been achieved [40], where no new information relevant to the study was found. The reasons for the non-participation of 11 participants were that they were not reachable ($$n = 5$$) or not available for the interview ($$n = 6$$). ## 2.4. Data Collection Data were collected through individual semi-structured face-to-face interviews held in the Nepali language. Due to the restrictions during the pandemic, all interviews were held digitally. For practical reasons, participants who attended digital educational sessions were interviewed four months after the start, while those taking part in physical sessions were interviewed seven months after the start, i.e., three months before and one month after the end of the program, respectively. One researcher (PS, a female Ph.D. scholar trained in qualitative studies at two universities in Norway) conducted all of the interviews. A note keeper made notes of significant verbal or non-verbal actions during the interview. The interviews lasted from 40 to 82 min, with an average of 62 min. A semi-structured interview guide, based on the literature [41,42,43] thoroughly discussed among the research group and reviewed by the other three external experts, was developed and pre-tested among the study nurses. The main questions were: What was your experience with DiPEP? What were the lifestyle changes implemented afterwards? How easy or difficult was it to follow the DiPEP lessons and maintain those changes? The guide also included several probing questions, and probes were also asked based on the responses of the participants. No changes were made to the main questions during the data collection. ## 2.5. Data Analysis All interviews were audio-recorded and transcribed verbatim in Nepali by native Nepali speakers (study nurses and research staff). To analyze the data, a thematic cross-case analysis called ‘systematic text condensation’ was used for this paper [44]. This consists of four steps: “[1] total impression—from chaos to themes; [2] identifying and sorting meaning units—from themes to codes; [3] condensation—from code to meaning; [4] synthesizing—from condensation to descriptions and concepts” [44]. This was performed iteratively. The transcripts were initially read and reviewed by two researchers (PS and MB) against the audio recording for quality assurance and to gain a total impression. First, three transcripts were read independently by PS from a bird’s eye perspective to identify preliminary themes. PS suggested three themes: (i) perception, (ii) knowledge, and (iii) implication. After reading seven other transcripts, PS recategorized and renamed the preliminary themes as: (i) participants’ understanding, (ii) acceptability of having prediabetes, (iii) participants’ perception, (iv) impact of the intervention, and (v) effects of the COVID-19 pandemic. MB read ten transcripts independently and suggested four preliminary themes called (i) understanding, (ii) level of acceptance, (iii) DiPEP and its effectiveness, and (iv) effect of COVID-19. PS and MB identified ‘meaning units’ (the smallest text fragment containing information about the research question) [44] independently and sorted them into their separate preliminary themes. This material was discussed thoroughly by PS and MB. A senior researcher (ArSh) checked the coding and resolved disagreements where necessary. Based on this, for this paper, three main themes called (i) understanding, (ii) acceptance of having prediabetes, and (iii) behavior changes were made. Then PS and MB coded all remaining interviews independently based on these three themes. Sub-themes were identified and defined under the given themes throughout the process of coding. This was further discussed with two senior researchers (ArSh and AsSt), and the findings were recategorized into four new themes with more focus on the participants’ experience and less on the actual changes they said they had made. The final themes were: (i) understanding that T2D can be prevented, (ii) lifestyle changes made, (iii) hurdles to overcome, and (iv) experiencing benefits leading to sustained change. A new round of sorting of meaning units was then conducted by PS and AsSt, with subsequent development of sub-themes and condensation of these. Finally, the contents of the condensations were rewritten into analytic text [44]. Quotations from each sub-theme that supported the themes were selected from the transcript and translated into English. The quotations were marked with PhyX and DigX, respectively, for participants attending the interventions physically or digitally. Nvivo [version 20.6.1.1137] was used to support the coding process. ## 3.1. Participants’ Characteristics The mean age of participants ($$n = 20$$) was 51 (SD = 9) years; $50\%$ were female. Fifty percent of the participants had at least a higher secondary level of education and half of the participants ($50\%$) were self-employed (Table 1). Table 2 presents baseline lifestyle characteristics, anthropometric measurements, and clinical characteristics of the participants. The majority were non-smokers ($95\%$), and half had never consumed alcohol. Physical activity was determined by Global Physical Activity Questionnaire using metabolic equivalent (METs) minutes per week [45]. It included physical activities of various levels, such as mild, moderate, and vigorous activities [45]. More than half ($65\%$) had physical activity (METs) (≥600 min per week), which is recommended by the WHO [45]. The mean total grain intake and mean total fruits/vegetables intake were 637 gm/day (SD = 143) and 264 gm/day (SD = 106), respectively. All had central obesity measured by waist circumference. Mean RBS and HbA1c were 167.6 mg/dL (SD = 33.9) and $5.9\%$ (SD = 0.2), respectively. The findings were categorized into four themes: (i) understanding that T2D can be prevented, (ii) lifestyle changes made, (iii) hurdles to overcome, and (iv) experiencing benefits leading to sustained change. ## 3.2. Understanding That T2D Can Be Prevented All participants were familiar with the term ‘diabetes’ for T2D, but no one had heard about the term ‘prediabetes’, although some had heard about ‘borderline diabetes’. Almost all participants verbalized that they had been unaware that diabetes could be prevented. It was said that this was a key motivator. A few participants spontaneously mentioned that they were happy to have been detected with ‘prediabetes’, because they understood that they had a chance to prevent T2D. For some, this was said to change their views on lifestyle modifications, as they had previously thought that T2D was inevitable. Learning in the DiPEP educational sessions about what can be performed to prevent T2D was said to be an eye-opener. The participants talked about how changes in diet, physical activity, and stress management could prevent T2D. One example was obtaining new knowledge about different types of exercises taught in the sessions; these types were referred to as contributing to blood glucose regulation. ## 3.3. Lifestyle Changes Made When participants were asked about lifestyle changes they had made, the topic most frequently mentioned was the reduction in food intake. Some said they used to eat large portions of rice with little vegetables in a single meal or drink lots of tea with sugar. After learning about the consequences of such a diet in the educational sessions, they said that they were motivated to change their diet. It was also reported that during the follow-up meetings, they found support in other participants talking about their own attempts to change their way of eating. Physical activity was also an area where there was frequent mention of changes made. This included both increasing the level of physical activity and adding different types of exercises. The reasons given were that the lessons in the sessions changed their perception of physical activity and exercises as important to prevent diabetes. One example was doing different types of exercise taught in the session, such as flexibility exercise, resistance exercise, and balance exercise, along with going for a morning walk every day as a part of moderate aerobic exercise. It was reported that they learned that instead of heavy exercises, they could do simple exercises at home to prevent diabetes. A few participants mentioned that before the DiPEP intervention, they had a regular habit of doing activities such as meditation and yoga to keep their minds at peace. They said that they would continue to do so as they had learned at DiPEP educational sessions that this was a good practice. For most of the participants, stress management was not an area where they said they had made changes; however, some said that they were surprised to learn about the link between stress and high glucose level and were motivated to implement some of the activities that were taught in the DiPEP educational session such as making a list of work for the next day before they went to bed to sleep. ## 3.4. Hurdles to Overcome Different participants stated different reasons for not being able to do what they were taught in the DiPEP educational sessions. Examples given were that they were too occupied with their household work and consequently were neither able to consider their diet pattern nor physical activity. One male participant stated that he did not have control over cooking food at home, so he had to eat whatever he was offered. Some reported that they were discouraged from doing exercises due to the cold weather (winter season). Others had started to make changes but met different types of challenges, which made them return to their previous lifestyle. One participant talked about how he felt weak after reducing his daily food intake and doing vigorous exercise. In addition, he said that his mother was concerned for his health and therefore insisted that he increase his food intake again. He recommended that knowledge should be provided to the family in addition to the individual. Some experienced peer pressure and social cues during gatherings and parties, making it difficult to stick to the diet recommendations in such situations. Others talked about the struggle they had with themselves. When we asked specifically about the effects of COVID-19 on their lifestyle changes, some participants did report that they increased their food intake, and some said they stopped doing exercises just to make sure they would not become weak during the times of the pandemic crisis. Two participants expressed that mobility restrictions hindered them from doing their regular exercises. ## 3.5. Experiencing Benefits Leading to Sustained Change It was readily seen in the interviews that participants who experienced some positive outcomes from the changes they had made due to what they learned in the DiPEP lessons were motivated to continue. A frequently mentioned example was becoming aware of being overweight and starting to monitor it regularly on their own. When they noticed the reduction in weight, reduction in abdominal girth, or blood sugar down to normal level, they were even more motivated to continue the change. Finding that the exercises taught in DiPEP educational sessions were easy, safe, and efficient to do at home was another example of what they said motivated them to continue with the change. One of the benefits reported was having more energy with increased physical activity. This experience was also reported for some of the stress management strategies. ## 4. Discussion The present study revealed that learning about the possibility of diabetes prevention at the prediabetic phase strongly motivated lifestyle modifications. It was reported that most changes were made in diet (reducing carbohydrate intake) and in physical activity (more active lifestyle), while fewer changes were made in stress management and in the handling of social situations. Lack of motivation, time, and family support meant that some did not change or reverted to their old habits. Experiencing benefits from lifestyle changes strengthened the motivation for maintaining the change. ‘Diabetes can be prevented’ was said to be an insightful message for the participants of the present study because they had thought that diabetes was inevitable due to heredity and thus not possible to prevent, similar to previous study findings [27,34,46]. Even though half of the participants in the present study had higher education, the information about diabetes prevention was still new to them. Evidence suggest that participants with a higher level of education had better participation in the lifestyle intervention [47] and had better results in terms of diabetes incidence reduction as a result of the intervention [48]. Studies also suggest that a diagnosis of prediabetes itself could motivate one to make lifestyle changes [30,34]. This is in congruence with the present study, where some participants expressed being happy to be detected with ‘prediabetes’. This new knowledge motivated participants of the present study to bring changes in their diet patterns and physical activity. The reported positive changes from the present study are in line with several other studies conducted worldwide [30,32,33,49,50]. Despite being aware of the possibility of preventing diabetes, people do not necessarily change their behavior [51]. Even if someone attempted to change behavior, lack of sustained and consistent effort at the maintenance stage of behavior change can lead to ‘relapse’ as described by the transtheoretical model [52]. Some of the participants of the present study reported having relapsed into their old habits due to different perceived social and psychological barriers, such as family and peers, similar to another study [30]. The barriers revealed by the participants of the present study, such as lack of personal motivation and the presence of external resistance, were similar to the challenges demonstrated in several past studies [35,53,54,55,56,57,58]. One male participant of the present study said that he ate what he was served, similar to the result of a study from the US [49], indicating that some male participants placed the responsibility of behavior change on the other members of the family. This might be due to the family cooking practices in Nepalese culture, where female members of the family cook and serve the food, and the other family members are expected to eat what is served. Traditional diet practices can also be a barrier, such as in Nepal, where it is common to start feeding rice to infants every day from 5–6 months of age and eating big meals with rice twice a day. On the other hand, changes in food culture, for instance, change from traditional food to ultra-processed food, may be the cause of obesity leading to prediabetes among genetically vulnerable individuals in the population [59,60]. In addition, participants neither knew about the adverse effect of obesity in developing prediabetes nor had any idea about required diet recommendations for good health and/or diabetes prevention [61,62]. Some of the participants of the present study were aware of the importance of exercise for good health, but not particularly for diabetes management or prevention. They also did not know about the minimum requirements of physical activity for good health [63]. Several male and female participants reported that morning walk was popular even before the DiPEP intervention. This was in contrast with studies in Bangladesh, where walking was perceived as an embarrassment and invited social criticism [33], and in Cameroon, where the morning walk was taken as a sign of poverty [46]. The intervention of the present study encouraged participants to do different types of simple exercises at home along with their morning walks. The introduction of simple exercises might have motivated participants to implement them. This is an example of the benefit of developing a curriculum addressing the local and cultural context. Some participants in the present study reported having benefited from the intervention. This motivated them to maintain the changes they had made. This supports the notion that positive health outcomes and feedback encourage individuals to change their behavior [54,64,65] and sustain their changed behavior. It was also reported that some participants did not notice the immediate benefits of the intervention, such as a change in weight, despite reducing the amount of food. This means that some people with obesity might need long-term support to achieve a change. ## 4.1. Strengths and Limitations There are several limitations to this study that should be considered while interpreting the results. This study was limited to relatively well-educated participants with prediabetes who were from 37–64 years old from one urban settlement in a developing country who participated in DiPEP intervention; therefore, the results cannot be generalized to other settings. Although the Nepali language was used in the interview, some participants could not express their thoughts well in this language as they normally used a different local language. This study did not explore the perception of participants who did not attend the educational sessions. The data were collected during the intervention period for participants who participated digitally and at the end of the intervention for participants who participated physically. Participants might not have talked about much of their negative experiences as the interviews were conducted by the main educator (PS). This might also have created confirmation bias. However, the interviewer (PS) made sure that the interviewing ambiance was neutral, and the participants also shared negative experiences. Before the interviews were concluded, participants were given the opportunity to say anything they wanted to say that had not been brought up during the interview. Furthermore, other authors took part in the analysis and found that although there were some examples of confirmation bias, this was not prevalent. Strengths of this study include the use of semi-structured interview guides, which allowed for iterative and flexible probing of the questions to explore participants’ experiences and understanding. All interviews were individual, conducted in the Nepali language, and transcribed anonymously by native Nepali speakers to minimize loss of meaning that could have occurred during translation. The conduction of the interviews by the same person (PS) responsible for the educational sessions ensured prolonged engagement with the participants from the start of the intervention. The involvement of several researchers in the analysis of the data helped reduce subjectivity and increase trustworthiness [66]. ## 4.2. Implication for Practice and Research The findings of the present study indicate that individuals with prediabetes should be made aware of their current status and risks in the future by regular community-based screening in the context of low-resource settings such as Nepal. Awareness programs on diabetes prevention and its strategies in the community could be an aid to motivate individuals to prevent diabetes. The present study also demonstrated some of the aspects of lifestyle changes that people might experience during or after the intervention. Some experiences included positive changes, while other experiences included difficulty in implementing or sustaining those changes, as indicated by the transtheoretical model [52]. The findings can be used in developing new interventions in similar settings in low-resource countries incorporating the benefits and the hurdles mentioned by the participants, for instance, by emphasizing simple strategies of lifestyle changes such as adjusting the size of portions of a meal without introducing new foods, doing easy and safe exercises, etc. Future interventions could also be designed that include family members to ensure family support for lifestyle changes. Whether the changes reported and perceived barriers are still present in the long term is worthy of future investigation. ## 5. Conclusions This qualitative study sheds light on the experiences of individuals with prediabetes who underwent community-based diabetes prevention intervention, which is one of the new areas of investigation for a low-resource country such as Nepal. The present study reveals the importance of the detection of prediabetes status among individuals. It also shows that understanding that diabetes can be prevented can be a key motivator for implementing lifestyle changes among persons with prediabetes in low-resource countries. It also highlights that consistent effort is a must to maintain the lifestyle changes made. The benefits and hurdles experienced by the participants of the present study can be taken into consideration while designing lifestyle intervention programs in similar settings. ## References 1. 2. 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--- title: 'Effect of Pulsed Electromagnetic Fields (PEMFs) on Muscular Activation during Cycling: A Single-Blind Controlled Pilot Study' authors: - Aurelio Trofè - Alessandro Piras - David Muehsam - Andrea Meoni - Francesco Campa - Stefania Toselli - Milena Raffi journal: Healthcare year: 2023 pmcid: PMC10048902 doi: 10.3390/healthcare11060922 license: CC BY 4.0 --- # Effect of Pulsed Electromagnetic Fields (PEMFs) on Muscular Activation during Cycling: A Single-Blind Controlled Pilot Study ## Abstract Purpose: PEMF stimulation results in a higher O2 muscle supply during exercise through increased O2 release and uptake. Given the importance of oxygen uptake in sport activity, especially in aerobic disciplines such as cycling, we sought to investigate the influence of PEMF on muscle activity when subjects cycled at an intensity between low and severe. Methods: Twenty semi-professional cyclists performed a constant-load exercise with randomized active (ON) or inactive (OFF) PEMF stimulation. Each subject started the recording session with 1 min of cycling without load (warm-up), followed by an instantaneous increase in power, as the individualized workload (constant-load physical effort). PEMF loops were applied on the vastus medialis and biceps femoris of the right leg. We recorded the electromyographic activity from each muscle and measured blood lactate prior the exercise and during the constant-load physical effort. Results: PEMF stimulation caused a significant increase in muscle activity in the warm-up condition when subjects cycled without load ($p \leq 0.001$). The blood lactate concentration was higher during PEMF stimulation ($p \leq 0.001$), a possible consequence of PEMF’s influence on glycolytic metabolism. Conclusion: PEMF stimulation augmented the activity and the metabolism of muscular fibers during the execution of physical exercise. PEMF stimulation could be used to raise the amplitude of muscular responses to physical activity, especially during low-intensity exercise. ## 1. Introduction Pulsed electromagnetic fields (PEMFs) are a non-invasive medical therapy used for clinical treatments. PEMFs for non-union fracture repair were approved for human use in 1979 by the Food and Drug Administration (FDA). Despite the long time of use, the effects of PEMF are still discussed, as well as the therapeutic benefit for human subjects and further studies are still needed, to confirm the positive influence of a pulsed electromagnetic field [1,2,3]. PEMF therapy is now in use for the treatment of bone conditions such as osteoporosis [4] and fracture [5,6,7]. Due to the piezoelectric effect, PEMFs improve bone mass and density, through the stimulation of osteoblastogenesis with modulation of calcium storage and mineral metabolism. Other studies have shown that PEMFs can improve the tissue oxygenation, microcirculation and angiogenesis in rats, in human erythrocytes and in cell-free assays [8,9]. Such responses could be caused by a modulation of nitric oxide signaling [10] and by the interaction between PEMFs and Ca2+/NO/cGMP/PKG signaling [11,12]. In humans, the effects of a pulsed electromagnetic field on blood circulation appear unclear. Rikk at al. [ 13] showed that PEMF treatment reduced the systolic blood pressure in aging adults, but not the diastolic pressure or arterial stiffness, suggesting that PEMFs could influence the peripheral resistance and microcirculation. Kwan et al. [ 14] found PEMF therapy helpful in patients with diabetes, due to the increased microcirculation by enhancing the capillary blood velocity and diameter. Sun et al. [ 15] showed that PEMFs improved the blood flow velocity of the smallest veins without changing their diameter. Nevertheless, further investigation is needed for an accurate description of the interactions between pulsed electromagnetic fields and human cells and tissue and to better understand the effects of the stimulation parameters such as time and frequency. It has been hypothesized that the different responses to PEMF therapy depends on the biological tissue or dosage of stimulation of a specific electromagnetic signal [16]. Despite much research and several medical applications, few studies have investigated the effects of PEMFs during physical activity. Galace de Freitas et al. [ 17] suggested that the combination of exercise training and PEMF stimulation could be used to improve the function, muscle strength and decrease in pain of patients with shoulder impingement syndrome. However, the benefits deriving from the association of PEMF and training are still controversial and more evidence is necessary to confirm the positive influence of pulsed electromagnetic fields. Parhampour et al. [ 18] applied PEMFs in association with six weeks of a resistance training program in patients with severe hemophilia A and osteoporosis in order to improve their muscle strength, bone formation and joint function. The results showed that PEMF stimulation, in association with resistance training, could be more efficient than PEMF therapy alone in improving bone formation due to the increased level of serum bone-specific alkaline phosphatase. However, further investigation is needed to clarify the benefits deriving from the association of PEMFs and training. Grote et al. [ 19] investigated short-term PEMF stimulation on heart rate variability in the recovery phase after physical exercise, suggesting a possible influence on the autonomic system. In this study, twenty minutes of exposure to low-frequency PEMFs accelerated the recovery of heart rate variability, especially in the very-low-frequency range, with a more rapid return to the initial sympathetic tone. Despite that, the basal autonomic tone seems to play a crucial role as well as the power of the electromagnetic signal. Further studies are necessary to determine the influence of pulsed electromagnetic fields on the autonomic system and recovery. Jeon et al. [ 20] investigated the effects of PEMF therapy on pain, soreness or muscle force generation associated with delayed-onset muscle soreness (DOMS) during recovery after isometric exercise. PEMF treatment on the brachii biceps for ten minutes after training reduced the severity of perceived symptoms of DOMS in the following days, enhancing the quality of recovery. PEMF treatment also increased the median frequency of muscle activation and reduced the electromechanical delay during isometric contraction in the day after exercise, suggesting a shortened recovery time. Despite this, no effect was found on the peak of isometric force generation; thus, more studies are necessary to confirm the positive influence of PEMF to improve and accelerate the recovery phase. Furthermore, the effect of PEMF therapy for pain in the shoulder [21] and neck [22] require additional studies in order to better clarify the benefit of stimulation. The aim of this study was to help better clarify the influence of PEMFs in humans and investigate the effect during physical activity. Until now, very few studies have investigated the influence of PEMF stimulation during exercise or sport activity. Given the importance of oxygen uptake in sport activity [23], especially in aerobic disciplines (e.g., cycling), we sought to investigate the PEMF’s effect during exercise to assess its influence on muscular activity. We know from our previous study that PEMF stimulation results in a higher O2 muscle supply during exercise through increased O2 release and uptake [24]. We thus hypothesize that PEMF stimulation could improve the muscular response due to a higher amplitude of muscular activity generated by the enhancement of muscular contraction mechanisms. ## 2.1. Subjects and Design The study design was a single-blind, randomized controlled trial. The experiments were performed in 20 male semi-professional cyclists (mean ± SD: age 22.3 ± 5.7 years; body mass index 22.5 ± 2.7; VO2 max 54.7 ± 10.4 mL/min/kg; weight 71.5 ± 10.3 kg; height 178.1 ± 6.5 cm). All subjects were volunteers, healthy, non-smokers and none of them were taking medications or supplements. None of the subjects reported a physical deficit or muscular injury at the time of the study. All participants received a verbal explanation of the experimental procedures, and informed consent was obtained before the beginning of recordings. The experimental protocol was approved by the Institutional Bioethic Committee of the University of Bologna. The experiments were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. Table 1 shows the features of each participant of the study. ## 2.2. Methodology For the realization of this study, we recorded the electromyography (EMG) activity from the vastus medialis (RVM) and biceps femoris caput longum (RBF) of the right leg. The EMG data were recorded with a data sampling rate of 1000 Hz with a Free-EMG 1000 (BTS Bioengineering, Inc.). Electrodes were placed on the muscular belly of each muscle. To improve contact, the skin was shaved and cleaned with ethanol before placing the Ag/AgC1 disposable electrodes 32 × 32 mm with active area of 0.8 cm2 and inter-electrode distance of 2 cm used in bipolar configuration (RAM, s.r.l, Italy). The athletes visited our laboratory three times, with three days between each visit, in which we performed different recordings. We first recorded the maximum voluntary contraction (MVC) used to normalize the electromyographic data. We normalized the EMG activity to the peak of the MVC following the same protocol used in previous studies [25,26]. Then, through an incremental test on a cycle-ergometer (H-300-R Lode), we determined the ventilatory threshold (VT), maximal oxygen consumption (VO2 max) and individualized workload for the succeeding recording sessions. The protocol for the incremental test was the following: each subject cycled at 50 Watt for 5 min, followed by a workload at 80 watt that increased by 20 Watt every 1 min, at a cadence of 70 RPM, until the volitional exhaustion [27]. Expired gas was analyzed using a Quark b2 breath-by-breath metabolic system (Cosmed srl, Rome, Italy). The individualized workload for each athlete (mean ± SD: 307.1 ± 60.1 watt) corresponded to ~$50\%$ of the difference between the power (watt) reached at ventilatory threshold (VT) and at VO2 max (~$50\%$ Δ VT−VO2 max) in order to provide a heavy intensity of exercise [28]. After the initial session, the subjects came to the laboratory for two more sessions on separate days, in which they performed a constant-load exercise with randomized active (ON) or inactive (OFF) PEMF stimulation. In order to stimulate the entire thigh, two PEMF loop-antenna devices (Torino II, Rio Grande Neurosciences, USA) were positioned on the right leg at the beginning and at the ending of the thigh. The subjects were blinded to the ON/OFF stimulations (single-blind trial). The PEMF waveform consisted of a pulse-burst modulated 27.12 MHz sinusoidal carrier, with 2 ms burst width repeated at 2 HZ, with a peak magnetic field at the center of the loop 5 ± 1 µT. The measurements were performed on the same cycle-ergometer in a quiet room with a stable and comfortable temperature (22 °C), at the same time of the day (9:00–12:00 AM) to avoid circadian influence. The subjects were instructed to avoid strenuous activity and alcohol in the 24 h preceding the test. The athletes were asked to avoid drinking caffeinated beverages before the experimental procedures. Each subject started the recording session with 1 min cycling without load (0 Watt), which was called the phase of warm-up, followed by an instantaneous increase in power, which was called the phase of constant-load physical effort. Each trial was ended intentionally, as time to exhaustion, when athletes were unable to keep the constant-load physical effort. We also measured the blood lactate concentration before the beginning of each trial (lactate baseline) and at the third minute of the constant-load exercise. ## 2.3. Data Analysis The software EMG easy report 6.03.8 (Merlo Bioengineering, Italy) was used for EMG traces on data process and artifact removal [29,30,31]. First, we used a wavelet-based denoising filter, in order to reduce the background noise and automatically remove large and frequent artifacts [32]. After detecting and removing specific PEMF artifacts on the EMG traces, a consolidation process described below was applied [32,33,34]. Starting from the raw signal, a peak emphasis operator, called Smoothed Non-Linear Energy Operator (SNEO) [33], was applied. SNEO is similar to the Taeger–Kaiser, the other operator frequently used with EMG signals [34]. The peak positions and amplitudes were found using thresholds of the minimum amplitude and distance between PEMF peaks. The position of unrecognized stimulus was found with linear interpolation of the values obtained in the previous point. When the artifact positions were found, the parts of the signal 20 ms before and 80 ms after the peaks were forced to zero (Figure 1). After that, the algorithm calculated the amplitude of the RMS limited at the signal of the muscle activity for each detected onset interval. The activation intervals were calculated through a specific algorithm [32] using a mean background noise level of 10 uV RMS. Then, the values were normalized to the peak of the MVC (Figure 1). For all the variables recorded, we averaged the values of all subjects in the PEMF ON and PEMF OFF stimulation. Figure 2 shows the raw and clean EMG traces for a typical subject, recorded during warm-up and during physical effort, for a typical investigated muscle (RVM), in both OFF/ON PEMF stimulation. We compared the mean values of muscular activity for unloaded cycling (warm-up), constant-load exercise (constant-load physical effort) and muscular activation related to the exercise duration (activity/time to exhaustion). A 2 (muscles, RVM and RBF) × 2 (stimulation, PEMF ON and PEMF OFF) repeated-measures ANOVA was performed on each condition (warm-up, constant-load physical effort, activity/time to exhaustion) separately. The effect sizes were calculated using partial eta squared (η2p), and the means were considered significantly different at $p \leq 0.05.$ The blood lactate levels were analyzed with Student’s t-test for paired data with the means considered significantly different at $p \leq 0.05.$ The effect sizes (ES) were calculated as the mean difference standardized by the between-subject standard deviation and interpreted according to the following thresholds: <0.20; small, >0.20–0.60; moderate, >0.60–1.20; large, >1.20–2.00; very large, >2.00–4.00; extremely large, >4.00 [35]. Data were analyzed with SPSS v22.0 (IBM, New York, NY, USA). ## 3. Results Figure 3 shows the effect of PEMF stimulation. We compared PEMF ON and PEMF OFF within the same muscle and the results showed significant differences: PEMF ON exhibited a higher significant RMS value of both muscles with respect to PEMF OFF during warm-up (RVM t[16] = −5.61; $p \leq 0.001$; ES 0.57—moderate; RBF t[16] = −6.29; $p \leq 0.001$; ES 0.69—large) (Student t-test: RVM $p \leq 0.001$; RBF $p \leq 0.001$) (Figure 3A). We also found a greater amplitude on RVM in comparison to RBF in both stimulations during warm-up (Figure 3A), during constant-load physical effort (Figure 3B) and in the relationship between muscular activity and exercise duration (Figure 3C). The ANOVA results showed a significant main effect for muscle (F1,17 = 16.452; $p \leq 0.001$; η2p = 0.141) and condition (F1,2 = 381.942; $p \leq 0.001$; η2p = 0.884). The analysis also showed an interaction effect of muscle x condition (F2,16 = 20.133; $p \leq 0.001$; η2p = 0.287). We compared the mean duration of each trial (time to exhaustion) in both ON/OFF PEMF stimulation and we did not find any significant difference (ON = 383 ± 15 s; OFF = 413 ± 16 s) in a one-way ANOVA with PEMF (ON/OFF) as a factor (F1,1 = 1.939; $$p \leq 0.171807$$). We measured the blood lactate concentration (mmol/L) before the beginning of the exercise session (baseline) and at the third minute of the constant-load physical effort (Figure 4). The analysis showed a significant difference between PEMF ON (10.05 ± 0.65 mmol/L) and PEMF OFF (7.48 ± 0.42 mmol/L) for the lactate concentration recorded during the constant-load exercise (t[19] = −4.78; $p \leq 0.001$; ES 0.46—moderate). ## 4. Discussion The main result of this study is that PEMF stimulation increases the activity of muscle fibers during warm-up but not during high-intensity constant load. ## 4.1. Effect of PEMF Stimulation on Warm-Up (Low Intensity) During warm-up, when athletes cycled at a very light aerobic intensity, PEMF stimulation enhanced the activity of both the vastus medialis and biceps femoris (Figure 3A). One possible explanation for this effect arises from the change in the membrane permeability and Ca2+ channel conduction enhancing the ion flux and cellular concentration [36,37]. The increase in the amplitude of the muscular response was probably caused by the effect of stimulation on type-I and type-II muscular fibers. Likely, PEMF stimulation increased the activity of type-II fibers, normally poorly activated during light physical effort, suggesting a possible application of PEMFs during the preparatory phase before competition, in order to raise the magnitude of muscular response. During the constant-load phase of effort, PEMF stimulation did not affect the amplitude of muscle activity (RMS). The analysis showed significantly increased activity for the vastus medialis with respect to the biceps femoris (Figure 3B). This result is not surprising given that the effective role of the vastus medialis during cycling is well-known, but the role of the biceps femoris is still under discussion: the magnitude of the biceps femoris is more affected by fatigue, pedaling rate, coordination/activation timing (angle), training status, shoe–pedal interface and body position. The biceps femoris is a bi-articular muscle involved in knee flexion and hip extension. According to Hug and Dorel, the biceps femoris seems to be more important for energy transfer between joints during cycling rather than to supply the main force [38]. One of the largest activities and an earliest activation of biceps femoris seem to be related to increased fatigue in both the vastus lateralis and medialis as a consequence of modified coordination and activation patterns [38]. In the present study protocol, the workload was instantaneous and strongly near to maximal, causing an immediate and large response of the main muscles of cycling, such as the vastus medialis, causing a rapid increment of its muscular activity. Thus, the biceps femoris increased its activity later, upon the arrival of fatigue in the vastus medialis. PEMF stimulation has an effect on muscle activity during low-intensity exercises but does not seem to affect muscle during heavy-load exercises. Possibly, the higher muscle activation covered the effect of stimulation. This is reasonable because our subjects performed a strenuous exercise that required a very high muscle activity. Our dosage of stimulation may not have been sufficient to increase even over the amplitude of muscle activity during exercise. Despite this, the higher blood lactate concentration recorded on exercise during stimulation indicates an effect of PEMF on muscle activity, especially on the contraction mechanism and glycolytic metabolism of type-II muscular fibers strongly involved during exercise. ## 4.2. Effect of PEMF Stimulation on Lactate Concentration The results showed that the PEMF stimulation caused an increase in the blood lactate, suggesting a potential mechanism of microstimulation in enhancing the activity of type-II muscular fibers, typically recruited when the intensity of exercise exceeds the ventilatory threshold. Moreover, lactate production is essential to delay muscle fatigue during heavy physical exercise. According to Robergs et al. [ 39], lactate production delayed metabolic acidosis and muscle fatigue, preventing the impairment of exercise performance. Lactate prevents pyruvate accumulation and supplies muscles’ production of NAD+, based on ATP regeneration from glycolysis [39]. The high-intensity exercise used in this study, with high increases in power during physical effort, led to a faster reduction in the intramuscular pH, suggesting that PEMF stimulation promoted type-II fiber metabolism and lactate production to delay metabolic acidosis. These results suggest a possible application of stimulation during exercise to enhance the amplitude of muscular fibers in response to physical activity. The results of the present study clearly show an effect of PEMF stimulation on low exercise intensity and on the amplitude of muscular responses (Figure 3A). The higher magnitude of muscular activity seems to suggest that PEMF stimulation could enhance muscular activation during preparatory activity. In addition to contraction mechanisms, it is possible to hypothesize that PEMF stimulation affected the energetic system inside the muscular fiber, especially glucose utilization. In rats with streptozotocin-induced diabetic muscle atrophy [40], chronic PEMF treatment affected metabolic enzymes in the quadriceps, with increased succinate dehydrogenase (SDH) and malate dehydrogenase activity (MDH), thus suggesting an increase in the metabolic capacity of muscle. Further, it has been found that PEMF treatment reduced blood glucose and increased serum insulin levels. In insulinoma cells, exposure to PEMF attenuated insulin secretion, suggesting effects on the calcium channels and ion flux [41]. In the present study, the high values of lactate recorded during PEMF stimulation were probably due to the increased overall activity of type-II fibers and boosting of their glycolytic metabolism. ## 4.3. Practical Applications The results of this study show that PEMFs can have an effect on muscular activity, suggesting potential applications in sport disciplines. Based on the present results, PEMF stimulation could be used during light physical effort in order to enhance the amplitude of muscular responses to exercise. PEMFs might be used at a high intensity of physical effort or during hard work-out sessions in order to boost the glycolytic metabolism of type-II fibers in response to heavy workloads and increase the benefits of an exercise program such as peripheral heart action training [42]. PEMF stimulation could also be applied during warm-up to raise the amplitude of muscular responses during the preparatory activity of different performances such as jumps, shots or sprints. PEMF stimulation could also be applied during light exercise or low aerobic intensity in order to increase the overall muscular response. Finally, due to the effect of PEMFs on succinate and malate dehydrogenase in rat quadriceps, it could be a possible effect of microstimulation on the aerobic activity over short and long distances. ## 4.4. Limitation of the Lactate Measure The main critical issue of this study regards the lactate measurement. In the methodological preparation of this study, we chose to take the sample before the beginning of the exercise session (baseline) and at the third minute of the constant-load exercise. We chose this moment because it represents the common time of the end of VO2 kinetics phase II and the start of the slow component [43,44] in order to obtain a more standardized value compared to that of the end of the exercise, given that the time of exercise differed for each athlete. The results showed a strong PEMF effect on the lactate concentration. The authors are aware that taking more lactate samples during the entire phase of both exercise and recovery, building the entire lactate curve, would better clarify the influence of PEMF on the glycolytic metabolism of type-II muscular fibers during exercise. Future experiments should be aimed to measure lactate every minute during the entire phase of exercise and recovery to better clarify the influence of PEMF on the energetic system. This would allow us to uncover the effects of PEMF stimulation on the glycolytic metabolism of type-II muscular fibers during exercise. ## 5. Conclusions To the best of our knowledge, this is the first study to investigate PEMF stimulation during exercise performed between low and severe intensity. This study shows an influence of PEMF stimulation on muscle activity, as well as on the energetic system during exercise. Despite this, more studies are necessary to confirm the influence of pulsed electromagnetic fields in human subjects during physical activity. We believe that these first observations could open new horizons in the field of sport performance. Further studies are necessary to elucidate the stimulation parameters necessary to elicit the most useful physiological response. ## References 1. 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--- title: 'Mitochondrial Genome Sequence of Salvia officinalis (Lamiales: Lamiaceae) Suggests Diverse Genome Structures in Cogeneric Species and Finds the Stop Gain of Genes through RNA Editing Events' authors: - Heyu Yang - Haimei Chen - Yang Ni - Jingling Li - Yisha Cai - Jiehua Wang - Chang Liu journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048906 doi: 10.3390/ijms24065372 license: CC BY 4.0 --- # Mitochondrial Genome Sequence of Salvia officinalis (Lamiales: Lamiaceae) Suggests Diverse Genome Structures in Cogeneric Species and Finds the Stop Gain of Genes through RNA Editing Events ## Abstract Our previous study was the first to confirm that the predominant conformation of mitochondrial genome (mitogenome) sequence of Salvia species contains two circular chromosomes. To further understand the organization, variation, and evolution of Salvia mitogenomes, we characterized the mitogenome of Salvia officinalis. The mitogenome of S. officinalis was sequenced using Illumina short reads and Nanopore long reads and assembled using a hybrid assembly strategy. We found that the predominant conformation of the S. officinalis mitogenome also had two circular chromosomes that were 268,341 bp (MC1) and 39,827 bp (MC2) in length. The S. officinalis mitogenome encoded an angiosperm-typical set of 24 core genes, 9 variable genes, 3 rRNA genes, and 16 tRNA genes. We found many rearrangements of the Salvia mitogenome through inter- and intra-specific comparisons. A phylogenetic analysis of the coding sequences (CDs) of 26 common protein-coding genes (PCGs) of 11 Lamiales species and 2 outgroup taxa strongly indicated that the S. officinalis was a sister taxon to S. miltiorrhiza, consistent with the results obtained using concatenated CDs of common plastid genes. The mapping of RNA-seq data to the CDs of PCGs led to the identification of 451 C-to-U RNA editing sites from 31 PCGs of the S. officinalis mitogenome. Using PCR amplification and Sanger sequencing methods, we successfully validated 113 of the 126 RNA editing sites from 11 PCGs. The results of this study suggest that the predominant conformation of the S. officinalis mitogenome are two circular chromosomes, and the stop gain of rpl5 was found through RNA editing events of the Salvia mitogenome. ## 1. Introduction Salvia officinalis L., also known commonly as sage, is an economically important aromatic and medicinal plant. It belongs to the mint family of Lamiaceae within Lamiales [1]. Salvia means “to heal or save” in Latin, and its species name “officinalis” means “medicinal” [2,3]. Sage has a historical reputation for preventing and curing illnesses and was regarded as a sacred plant in Ancient Rome [3,4]. It has been extensively investigated for its antioxidant, antibacterial, hypoglycemic, and anti-inflammatory properties [5,6]. Moreover, sage can prevent and cure cardiovascular diseases, brain and nervous disorders, and diabetes [7]. The aerial parts of sage were used mostly for the extraction of active constituents [5,8]. Two principal types of secondary metabolites were found in S. officinalis: terpenoids and phenolics [6]. The most active constituents of S. officinalis are essential oils (1–$2.8\%$), including 1,8-cineole, camphor, α-thujone, β-thujone, borneol, and viridiflorol [9]. The nuclear genome of S. officinalis was reported recently and revealed a genomic size of 480 Mb and seven chromosomes [10]. Two expression cascades have been identified as the regulating factors of the biosynthesis of shoot and root diterpenoids, particularly the diterpene biosynthesis gene cluster [10]. Furthermore, genetic and metabolomic studies have focused on discerning the genes and pathways that underpin volatile terpenoid biosynthesis [11,12] and the genetic diversity of seven S. officinalis populations in Greece [13]. Organelle genomes including the genomes of mitochondria and plastids [14,15] are resources for species identification, evolution, and phylogenetic studies [16,17,18]. They all evolved from bacterial endosymbionts [19,20]. Similar evolutionary histories lead to many common characteristics of the mitochondrial and plastid DNAs (mtDNAs and ptDNAs). However, mitochondrial genomes (mitogenome) present more complexity and more pronounced eccentricities than plastid genomes [21]. In particular, the mitogenomes of higher plants show enormous diversity in their genome sizes, structures, and gene contents [14,22]. The mitogenome size ranged from 11 Mb for Silene conica [23], to 66 kb for Viscum scurruloideum [24]. The size variations were reported to be extremely divergent even in the same genus Silene [25]. Different mechanisms contribute to these size variations of plant mitogenome including the proliferation of repeat elements, the incorporation of foreign DNA [22], and the gain or loss of whole chromosomes [26]. The structures of the mitochondrial DNA in angiosperm plants showed enormous diversity at three levels: inter-specific, intra-specific, and within individual levels. At the inter-specific level, different technologies have been used to observe the mitogenome structure diversity. For example, the in-gel fluorescence microscopy of Lactuca sativa mtDNA molecules showed the presence of linear, branched, and circular structures, with branched linear forms representing a prevailing presence [27]. In cultured tobacco cells, linear molecules and multibranched molecules in mtDNA were observed by the pulse-chase experiments with 3H-thymidine [28]. In cultured Chenopodium album (L.) cells, linear, circular, sigma-like, branched, and rosette-like structures were observed by electron microscopy [29]. The plant mitogenomes assembled from high-throughput DNA sequencing data are also found to have complex structures [30]. For example, the cucumber mitogenome was assembled into a single circular master chromosome and a collection of sub-master circular chromosomes [31]. The most prominent mechanism for structure diversity is likely to be the intra- and inter-molecular recombinations mediated by direct repetitive sequences [32,33]. In addition to the inter-specific level, the mitogenome structures were also found to be highly diverse at the intra-species level. For example, seven mitogenomes of sorghum from different cultivars and wild sources showed reticulated structures with multilinked relationships and were grouped into three types [34]. The different types of mitogenome structures could be associated with certain domesticated events. Enormous diversity was also expected at the individual level, considering the fact that one cell has multiple copies of mitogenomes. Indeed, representing the mtDNAs as circular or linear chromosomes might not be appropriate, as these mtDNAs might exist as polymeric complexes. In this case, they are best represented with a graph. Recently, a graph-based sequence assembly toolkit (GSAT) was developed to assemble and obtain high-quality mitochondrial master graphs (MMGs) to represent the full spectrum of structural conformations of plant mitogenomes [30], demonstrating another level of mitogenomic structural diversity. The mitogenome contents, in terms of the number of PCGs and introns, are also found to be highly diverse. The number of PCGs ranges from 19 in Viscum scurruloideum [24] to more than 50 in the liverwort Marchantia polymorpha [35,36]. Additionally, the number of introns varies from 4 in Viscum scurruloideum [24] to more than 40 in the hornwort Anthoceros agrestis [37]. In the angiosperm mitogenomes, 24 core protein-coding genes (PCGs) encode the respiratory proteins, and 17 variable PCGs encode the ribosomal proteins [38,39,40]. The diversity of the mitogenome contents was further increase by a post-transcriptional modification process, called RNA editing [41]. The number of RNA editing sites varies extensively among different mitogenomes, ranging from 8 RNA in the moss Physcomitrella patens to 2139 in Selaginella [42]. Previously, we identified 225 RNA editing sites in the S. miltiorrhiza mitogenomic mRNA. In particular, we found 115 symmetric RNA editing sites, characterized by being on two different strands at the same position [43]. Given the prevalent RNA editing events, the mitogenome sequences alone are insufficient to detect the mitochondrial proteome sequences. The identification of RNA editing sites is important to determine the final output of the functional mitochondrial proteins. The plastid genomes of S. officinalis have been reported [12,44,45]. We also developed novel molecular markers for authenticating three Salvia species [45]. The nuclear genome of S. officinalis has also been reported, but the mitogenome of S. officinalis remains unknown, limiting our ability to understand the interaction of the three genomes in S. officinalis. Our long-term goal is to understand the diversity and evolution of Salvia mitogenomes, thereby ultimately providing valuable information for molecular breeding studies on these economically important Salvia species. Recently, we confirmed for the first time that the predominant conformations of Salvia mitogenomes contain two circular molecules [46]. In the present study, we sequenced the mitogenome of S. officinalis. We ascertained the mitogenomic structure, gene content, repeat sequences, and RNA editing sites. We then compared the mitogenome and plastome to identify the mitochondrial plastid DNA (MTPT) and their evolutionary congruence. Lastly, we compared the mitogenome of S. officinalis with those of related species. The results obtained from this work lay a solid foundation for comprehending the diversity and evolution of Salvia mitogenomes. ## 2.1. Structure of the S. officinalis Mitogenome S. officinalis is a low (40–100 cm) shrub, and its leaves are oblong. The whole plants, aerial parts, leaves, flowers, and fruits of S. officinalis are shown in Figure 1A–D. We obtained 32 Gb of Illumina sequencing data and 22 Gb of Nanopore sequencing data. The statistics of the sequencing results can be found in Table S1. The mitochondrial reads were extended from the raw sequencing reads using the GetOrganelle toolkit and de novo assembled using Unicycler software. The assembly result from GetOrganelle is shown as a unitig graph in Figure 2A. Three double-bifurcation structures (DBS) are designated as DBS01–03 in Figure 2A. Each of the DBS structures contained four alternative conformations—1, 2, 3, and 4—correspondingly named c1–4. Unicycler software was used to resolve these DBS structures, which resulted in two circular sequences (Figure 2B). These two sequences were considered mitogenomic chromosomes, named MC1 and MC2, and were 268,341 bp and 39,827 bp in length and had GC contents of 45.23 and 44.32, respectively. To confirm that these three DBS structures were resolved correctly, we manually mapped all long reads to the sequences corresponding to the four conformations c1, c2, c3, and c4 of each DBS (Figure S1). The percentages of each conformation were quantified by the ratio of the number of long reads mapped to its sequence and those mapped to all four conformations (Table 1). According to the calculation, the relative abundances of the less abundant conformations of DBS01, 02, and 03 were $8.93\%$, $15.00\%$, and $20.69\%$, respectively. The details for the HSP r01, r02, and r47 are provided in Section 2.3. To check the quality of the assemblies, we mapped the Nanopore and Illumina reads back to the MC1 and MC2 sequences. The mitogenome was mostly uniformly covered, with the Nanopore reads having average depths of coverage of 237.31 for MC1 (Figure S2a) and 53.72 for MC2 (Figure S2b). Similarly, the mitogenome was uniformly covered, with the Illumina short reads having average coverage depths of 285.93 for MC1 (Figure S2c) and 161.89 for MC2 (Figure S2d). To determine the inter-specific variations of Salvia mitogenomes, we compared the mitogenome sequences of S. officinalis and S. miltiorrhiza [46] using Mummer (v3) [47] (Figure 3). As shown, there were many rearrangements between the two genomes (Figure 3). The lengths of the alignable regions were 177,342 bp ($57.55\%$) for the S. officinalis mitogenome sequences and 176,363 bp ($42.59\%$) for the S. miltiorrhiza mitogenome sequences, respectively (Figure 3). ## 2.2. Gene and Intron Content of S. officinalis and Other Related Lamiales Mitogenomes The S. officinalis mitogenome encoded 33 PCGs, 3 rRNA genes, and 17 tRNA genes. The distribution of these genes, along with three repetitive sequences (r01, r02, and r47) that might mediate homologous recombinations are shown in Figure 4. Details for the repetitive sequences are provided in Section 2.3. The 33 PCGs included the 24 core PCGs found in most of the angiosperm plants and 9 variable PCGs (Table 2), consistent with prior descriptions for angiosperm mitogenomes [24]. The coding sequences (CDs) covered $12.5\%$ of the mitogenome. Furthermore, we compared the gene contents of S. officinalis with all its Lamiales relatives (Figure 5A). We found that all the core genes were present. The Salvia mitogenomes lost four variable genes: rpl2, rpl23, rps7, and sdh3. In addition, the sdh4 was found to be a pseudogene as only partial sequences remained. The angiosperm mitogenomes contain group I and group II introns [48]. The two types of introns were defined according to their conserved folding structures and splicing mechanisms [49]. These introns were removed by cis- or trans-splicing to form continuous and functional transcripts [50,51]. In the S. officinalis mitogenome, 10 PCGs contained 18 cis-spliced introns (Figure 5B). Among them, nad1, ccmFc, cox1, cox2, rps3, and rps10 contained one cis-spliced intron each. nad5 contained two cis-spliced introns. nad2 and nad4 contained three cis-spliced introns each. nad7 contained four cis-spliced introns. These introns were named according to a scheme described previously [38,52] and the names of the introns are shown in Figure 5B. Three PCGs (nad1, nad2, and nad5) contained six trans-spliced introns in total: nad1i394, nad1i669, nad1i728, nad2i542, nad5i455, and nad5i477 (Figure 5B). We inferred intron gain/loss polymorphism in the S. officinalis mitogenome and the other ten Lamiales mitogenomes, which were introduced in the method of phylogenetic analysis (Section 4.7). The cox1 intron (cox1i729) was detected in eight species, including S. officinalis, but was not detected in the three species: E. guttata, O. fragrans, and H. palmeri (Figure 5B). In the eight Lamiales species, the cox1 intron was highly conserved in terms of its position on the cox1 gene. The presence/absence polymorphism of cis-spliced intron cox2i691 was also observed. Moreover, nad1i728 was cis-spliced in the two Oleaceae species. Its adjacent exons were trans-spliced in the nine Lamiales species (Figure 5B). Figure 5: Heatmaps showing the PCG contents (A) and intron contents (B) in eleven sequenced Lamiales mitogenomes. Each column represents a gene in (A) and an intron in (B). Each row represents a species. The gene and intron names are shown above the heatmaps. The Latin names for each species and the family they belong to are shown to the left of the heatmap. The introns are named according to a prior scheme [52]. The mitogenome generated in this study was labeled with “”. The different colored and patterned squares in the figures are explained below (A) and to the right of (B) the heatmaps.* ## 2.3. Homologous Recombination Mediated by Dispersed Repetitive Sequences Angiosperm mitogenomes contain three types of dispersed repetitive sequences on the basis of the repeat unit length: larger repetitive sequences (LRs), intermediate repetitive sequences (IntRs), and short repetitive sequences (SRs), with repeat unit lengths of “>1 kb”, “100–1000 bp”, and “<100 bp”, respectively [53]. They are vastly distributed among different plants and contribute to homologous recombination and variable mitogenomic structures [54,55]. To identify the repetitive sequences that might mediate homologous recombination in the S. officinalis mitogenome, we conducted a self-to-self sequence comparison using BLASTn with an e-value < 1 × 10−6 and a word size of 7, as described previously [46]. The BLASTn results contained 52 high-scoring sequence pairs (HSPs). Prior research confirmed that the repeat unit sequences of the repetitive sequences mediating homologous recombination could be variable. Consequently, we considered all these HSPs as potential repetitive sequences and named them r01–r52 (Table 1 and Table S2). These 52 repetitive sequences corresponded to 47 SRs and 5 IntRs, ranging from 28 to 892 bp in repeat unit length, with sequence identities ranging from $85.98\%$ to $100\%$ (Table S2). Among these 47 SRs and 5 IntRs, 30 SRs and 2 IntRs were intra-chromosomal, as both repeat units were on MC1 and MC2. By contrast, 20 SRs were inter-chromosomal and the two repeat units were located separately on MC1 and MC2. To investigate if these repetitive sequences can mediate homologous recombination, we extracted the repeat unit sequence along with its 1000 bp long flanking sequences and combined them to form the reference sequences representing the four conformations before and after recombination (Figure 6A). Then, we mapped the Nanopore long reads to the reference sequences representing those before and after recombination. The homologous recombination products of three repetitive sequences (r01, r02, and r47) were supported by the Nanopore long reads. Further examination of these three repetitive sequences showed that they corresponded to DBS01–03, respectively (Figure S1). To confirm the presence of the recombination products of r01, r02, and r47, we designed primers that can amplify DNA sequences corresponding to the c1–4 conformations (Table S3). The primers were then used to amplify the genomic DNA (gDNA), and the products were sequenced with the Sanger method. The gel electrophoresis results of the PCR products are shown in Figure 6B. The products corresponding to the c1–c4 recombination product conformations affiliated with r01, r02, and r47 are shown on lanes 1–4, 5–8, and 9–12, respectively. The PCR products were recovered and subjected to Sanger sequencing. The comparison of the Sanger sequencing results and those expected sequences revealed identical sequences (Figure S3). We then drew the structure of the circular molecules that might result from the recombination mediated by the repetitive sequences r01, r02, and r47 (Figure 7). We define genomic conformation as a set of chromosomes that can contain all the genomic contents. As shown in Figure 7A, we defined the genomic conformation containing both MC1 and MC2 as major conformation 1 (Mac1). Then, four circular molecules, namely, Mic1-1, Mic2-1, Mic2-2, and Mic3-1, can be formed after recombination, mediated by r01, r02, and r47 alone, repeatedly. Assuming recombinations mediated by the three repetitive sequences were independent, then various recombination products can be obtained after recombinations mediated by two and three of the repetitive sequences. In Figure 7B, we showed that Mac1 can form Mic4, Mic5, and Mic6 through recombinations mediated by r02 + r47, r01 + r47, and r01 + r02, respectively. In Figure 7C, we showed that Mac1 can form Mic7 through recombinations mediated by all three repetitive sequences. It should be noted that the genomic conformation Mic3 had only one circular chromosome, which was traditionally regarded as the master circle. Here, we found that its abundance was lower than that of the Mac1. Therefore, Mac1 was considered the predominant conformation. Note that multiple recombinations mediated by different combinations of repetitive sequences could occur and generate numerous molecules with different conformations. ## 2.4. Tandem Repeats in the Lamiales Mitogenomes Tandem repeats, also called tandem repetitive sequences, are repetitive sequences whose repeat units are arranged in tandem. Simple sequence repeats (SSRs), or microsatellites, are short tandem repeat sequences in which the repeat units contain 1–6 nucleotides [56]. Tandem repeats in which the repeat units contain ≥7 nucleotides are designated as long tandem repeats. SSRs are involved in species authentication, genetic variation, construction of linkage maps, population phylogenetics, and evolution [57,58]. The importance of tandem repeats was reported in studies on the transcription, translation, and regulation of promoter activities [59,60]. Using the MISA web service and Linux version of the Tandem Repeats Finder (v4.09), we identified 78 SSRs and 9 long tandem repeats (Table S4). Among the SSRs, 67 and 11 SSRs were located on MC1 and MC2, respectively. The 67 SSRs on MC1 consisted of 5 SSRs in the exonic regions of genes atp6, cob, rps3, and nad1 (SSR17, SSR18, SSR42, SSR58, and SSR59), and 61 SSRs in the non-exonic regions of the mitogenome (Table S5). One SSR in the MC2 was also located in the exonic regions of gene atp6 (SSR73). The S. officinalis mitogenome is rich in tetranucleotide repeats, which represented $43.28\%$ and $36.36\%$ of all SSRs on MC1 and MC2, respectively. By contrast, trinucleotide repeats were less frequent than their tetranucleotide counterparts and represent $19.40\%$ of all SSRs on MC1. Trinucleotide and hexanucleotide repeats were not found in MC2. The S. officinalis mitogenome contained seven (TR1-7) and two (TR8-9) long tandem repetitive sequences on MC1 and MC2 (Table S6), respectively. The length of the repeat units ranged from 12 to 36 nt. ## 2.5. Identification of Mitochondrial Plastid DNA (MTPT) MTPTs are mitochondrial DNA sequences that are derived from plastomes [61]. To identify MTPTs in the S. officinalis mitogenome, we compared the mitogenome and the plastome of S. officinalis using BLASTn with an e-value < 1 × 10−6 and a word size of 7 [46]. We identified 23 MTPTs in the S. officinalis mitogenome, with a total length of 14,495 bp accounting for $4.70\%$ and $9.59\%$ of the S. officinalis mitogenome and plastome sequences, respectively. The list of MTPTs is shown in Table S7. A schematic representation of the homologous sequence pairs from the mitogenome and plastome is shown in Figure S4. The size of the MTPTs ranged from 41 to 4,261 bp. Twenty-one (mtpt01–21) and two MTPTs (mtpt22–23) were found in MC1 and MC2, respectively. The two largest MTPTs were mtpt01 (4261 bp) and mtpt11 (3598 bp), covering the regions of MC1 from positions 32,916 to 37,176 and from 167,076 to 170,673 (Table S7). The mtpt01 contained a fragment of the gene ycf2 and the 198 bp long, complete CDs of gene ycf15. The mtpt11 contained the 468 bp long, complete CDs of the gene rps7. Many complete plastid tRNA genes were also found in the MTPTs, including trnI-CAU, trnN-GUU, trnM-CAU, trnH-GUG, trnD-GUC, trnS-GGA, trnP-UGG, and trnW-CCA (Table S7). To confirm that these sequences homologous to the plastome sequences are indeed on the mitogenome, we extracted the MTPTs and 2 kb long flanking sequences on each side of the MTPTs as reference sequences. We also mapped the Nanopore long reads to these reference sequences. The presence of long reads spanning the homologous sequences would support the notion that these sequences were indeed MTPTs. The mapping results for mtpt01–23 (Figure S5) confirmed the presence of these MTPTs. ## 2.6. Phylogenetic Analysis To understand how mitochondrial genomes evolve, we conducted a phylogenetic analysis of 11 Lamiales species and 2 non-Lamiales species as outgroups. First, we extracted the CDs of 26 genes shared by these 13 species, including the 24 core genes (atp1, atp4, atp6, atp8, atp9, ccmB, ccmC, ccmFc, ccmFn, cob, cox1, cox2, cox3, matR, mttB, nad1, nad2, nad3, nad4, nad4L, nad5, nad6, nad7, and nad9) and 2 variable genes (rps12 and rps13). The CDs were concatenated and subject to multiple sequence alignment with MAFFT. We constructed a maximum likelihood (ML) tree using the RAxML (v8.2.4) and a Bayesian inference (BI) tree using the MrBayes version (3.2.7) [62]. ML and BI analyses revealed that S. officinalis is the sister taxon to S. miltiorrhiza with a bootstrap support of 100 and a posterior probability of 1.00 (Figure 8A). Furthermore, we conducted a parallel phylogenetic analysis of the same taxa using the CDs of 56 common plastid PCGs (Figure 8B). The two trees had identical topology, suggesting that the CDs of mitogenomes and plastomes underwent a similar evolutionary process in these Lamiales species. ## 2.7. RNA Editing Site Analysis RNA editing is a post-transcriptional modification process frequently found in the organelle of high plants [63]. RNA editing plays important roles in protein sequence conservation in the plastids and protein sequence diversification in the mitochondria. RNA editing can affect the composition of the final mitochondrial proteome. Furthermore, RNA editing can create start and stop codons in the mitochondrial mRNA molecules [37] through the so-called start-gain or stop-gain processes [63]. To determine the extent of RNA editing in the S. officinalis mitogenome, we first detected the RNA editing sites using the RNA sequencing reads, and 452 sites in the CDs were obtained within the 31 PCGs (Table S8). To exclude sites that might have originated from polymorphic sites, we identified the single nucleotide polymorphism sites in the CDs of the 31 PCGs (Table S9). Comparing the predicted RNA editing sites and the single nucleotide polymorphism (SNP) sites revealed only one overlap which, in turn, was removed from the downstream analysis of RNA editing events. For the remaining 451 sites, we first ascertained the discrepancies between the DNA and mRNA sequences. We mapped the RNA sequencing reads to the CDs of 31 PCGs and then visualizes the results using the IGV software (v 2.15.1). The results (Figure S6) confirm the RNA editing of all the detected sites. To further confirm the RNA editing of these sites, we randomly selected 11 genes that contained 126 detected RNA editing sites. We designed specific primers (Table S10) to amplify the corresponding gDNA and complementary DNA (cDNA) sequences. The amplicons were then sequenced using the Sanger method. The comparison of the sequences amplified from gDNA and cDNA was presented in Figure S7. In total, 113 ($90\%$) sites were confirmed successfully (Figure S7 and Table S8). These 451 sites contained 404 non-synonymous editing sites, significantly more than the 47 synonymous sites in the S. officinalis mitogenome (Table S8). Moreover, $57.21\%$ (258 sites), $32.37\%$ (146 sites), and $10.42\%$ (47 sites) editing sites occurred at the first, second, and third codon positions, respectively (Table S8). Specifically, we detected two stop-gain editing sites rpl5-529 and rps10-307, which created two new stop codons. The rpl5-529 editing changed the codon from CAA to the stop codon TAA (Table S8, Figure 9, Figure S6x, and Figure S7i). The rps10-307 editing changed the codon CGA to the stop codon TGA (Table S8 and Figure S6C). ## 3. Discussion Recently, we reported on the mitogenome of S. miltiorrhiza on the basis of Pacbio long reads and Illumina short reads [46]. We were the first to confirm that the predominant conformations of the Salvia mitogenome are two circular chromosomes. To understand the diversity and evolution of the Salvia mitogenomes, we sequenced, assembled, and characterized the mitogenome of S. officinalis in detail in the current study. We characterized the gene contents, SSRs, tandem repeats sequences, dispersed repeat sequences, MTPTs, and RNA editing events in the S. officinalis mitogenome. Lastly, we used the common genes of 11 Lamiales species to identify the phylogenetic relationship and variation in gene and intron contents within Lamiales. The results from this work provide indispensable information for comprehending the diversity and evolution of Salvia mitogenomes. ## 3.1. Architecture of Two Major Circular Chromosomes and Multiple Variable forms for the S. officinalis Mitogenome The S. officinalis mitogenome contains two circular chromosomes of 268,341 and 39,827 bps in length. The total length was 308,168 bps (Figure 1). These multichromosomal structures were also found in a close relative, S. miltiorrhiza, which also contained two chromosomes that were 328,915 and 85,199 bps in length. The total length was 414,114 bps [46]. These lines of data suggest that the dominant form of Salvia mitogenomes might be two circular chromosomes. With the availability of more Salvia mitogenomes, we will be able to test if this two-major-chromosome architecture is conserved. The presence of alternative structures of plant mitogenomes created by repeat-mediated homologous recombination has been reported previously [54,55]. In the S. miltiorrhiza and Scutellaria tsinyunensis mitogenomes, repetitive sequences (numbered nine and one) could mediate homologous recombination, which resulted in alternative genomic conformations. We compared the sequences of these repeats and found no sequence similarity between them. Furthermore, the Mimulus guttatus mitogenome has eight alternative genomic conformations (C2–C8) predicted through the homologous recombination of three large repeats (R1, R2, and R3). In this study, we identified three repetitive sequences, r01, r02, and r47, in the S. officinalis mitogenome that mediated homologous recombination. Seven alternative genomic conformations Mic1–7 were predicted based on the recombination mediated by repeats independently. The results indicate that repeat-mediate recombination is a driving force in a plant mitogenome’s structural diversification. It should be noted that there were 52 pairs of repetitive sequences in the S. officinalis mitogenome, but only 3 pairs were identified to be associated with homologous recombination. There are two putative explanations. Firstly, other repeats have low or no recombination activity. Secondly, due to the high sequencing error rate of Nanopore reads, it is difficult to identify the different copies of repeats, resulting in no observation of recombination products using the methods and parameters used in this study. Additional recombination products might be identified with an improvement in the accuracy of long-read sequencing. The specific observation of recombination products for these three repetitive sequences suggested that the recombination events are context-specific and that the recombination frequencies might result from the repeat sequences and their high-dimensional structures at or around the repeat sequences. However, we believe that we do not have sufficient data to draw further conclusions in the current paper. The relative copy number of these alternative subgenomic conformations within plant mitochondria was differential [54] and was controlled by nuclear genes [64]. For instance, one nuclear gene (CHM) in Arabidopsis regulated the genomic shifting process and influenced the abundance levels of subgenomic conformations [64]. The possible involvement of the mechanism of this mitochondrial substoichiometric shifting should be studied in S. officinalis in the future. ## 3.2. Intron Contents of the Lamiales Mitogenomes The intron contents of an angiosperm plant’s mitochondrial genes are conserved in each lineage but are variable among different lineages [38]. The introns’ positions, such as those of nad7 introns of Marchantia, have been used as phylogeny markers [65]. In the present study, we observed the variety of intron contents among different Lamiales lineages. For example, intron loss of the cox1 gene (cox1i729) occurred in two Oleaceae species and one Orobanchaceae species (Figure 5B). By contrast, the cox1i729 introns were present in the other eight species. In these eight species, the positions of the cox1i729 were highly conserved. The origin of the intron cox1i729 has also been examined in the literature. One study proposed that cox1i729 was gained by a single horizontal transfer from fungi and subsequently transferred from one angiosperm to another [66]. Another report suggested that the cox1i729 intron was gained in angiosperms once by largely or entirely vertical transmissions [67]. Five introns (nad1i394, nad1i669, nad2i542, nad5i1455, and nad5i1477) found in the angiosperm mitochondrial trans-spliced genes (nad1, nad2, and nad5) were also identified in the eleven Lamiales species [38]. Furthermore, these trans-spliced introns were postulated to have evolved from one common ancestor by fragmentation of a cis-spliced arrangement [50]. ## 3.3. MTPTs in the S. officinalis Mitogenome Foreign sequences are commonly found in the angiosperm plants’ mitogenome; in particular, plastome sequences are frequently found in the mitogenomes through intracellular gene transfer (IGT) [68]. In this study, we found 23 MTPTs in the S. officinalis mitogenome ranging from 41 to 4261 bp. The total length of MTPTs was 14,495 bp and represented $4.70\%$ of the mitogenome. Previously, 16 MTPTs ranging from 115 to 4987 bp (totaling 12,583 bp) and 19 MTPTs ranging from 45 to 902 bp (totaling 3372 bp) were identified in the S. miltiorrhiza and Scutellaria tsinyunensis mitogenomes, covering $3.04\%$ and $0.95\%$ of the mitogenome, respectively [46,69]. A higher ratio of the MTPT sequences was gained in other plants, such as a total of $6.3\%$ of plastid sequences in the rice mitogenome [70]. In addition, the largest MTPT of the S. officinalis mitogenome (4261 bp) was slightly shorter than the largest MTPT in the S. miltiorrhiza mitogenome (4987 bp) but was more than four times longer than the largest MTPT in the Scutellaria tsinyunensis mitogenome (902 bp). The long fragments that transferred from plastids were not unique to the Salvia mitogenomes as they were also found in the Physochlaina orientalis mitogenome, with the largest MTPT being 6593 bp. Most MTPTs in the S. officinalis, S. miltiorrhiza, and Scutellaria tsinyunensis mitogenomes might be genetically inserted and become functionless as fragments of the coding genes. This presumption was also given in other higher plants [68]. The plastome tRNA genes were always intact and present in the S. officinalis and Scutellaria tsinyunensis mitogenomes. Current data suggest that the MTPTs are quite diverse in terms of length and content within the S. officinalis mitogenome. ## 3.4. RNA Editing in the S. officinalis Mitogenome We also compared the 451 RNA editing sites detected in this work to those in a previous study of S. miltiorrhiza and found 193 sites in the homologous sites of the PCGs (Table S8). The stop codon gain site of rps10-307 was also conserved in the two mitogenomes. In the future, more mitogenome sequences of Salvia are needed to obtain additional information about the conservation of and variation in RNA editing events, and the protein sequences and structures of Salvia mitogenomes. RNA editing events modify transcript site-specific information post-transcriptionally, and mitochondrial RNA editing events have been extensively detected among land plants [63,71,72]. Additionally, RNA editing sites are somewhat conservative in the angiosperm plant mitochondrial genome [73]. In our work, 13 of the 126 RNA editing sites in PCGs were not validated successfully. This outcome might be caused by the following reasons. First, the frequency of RNA editing was low in some of these sites (eight sites had frequencies of RNA editing ranging from 0.13 to 0.44). Second, preferential amplification most likely occurs during PCR. Determining the RNA editing landscape is critical to annotate the mitogenome’s proteome. For example, annotation based on the stop codon on the DNA sequences predicted a CD of the gene rpl5 in S. officinalis that is 786 bp in length. By contrast, the homologous rpl5 CD is 555 bp long in S. miltiorrhiza and 558 bp long in Arabidopsis thaliana. However, considering the stop gain, the CDs of the gene rpl5 in S. officinalis became 529 bp in length and were more similar to their homologous sequences in S. miltiorrhiza and Arabidopsis thaliana. Furthermore, the sequence downstream from the stop gain codon had little similarity to sequences from S. miltiorrhiza and Arabidopsis thaliana. Taken together, DNA sequences alone are not sufficient to annotate PCG sequences, and RNA editing events must be taken into consideration. ## 4.1. Plant Materials and Nucleic Acid Preparation Young leaves were collected from an S. officinalis line (named so-01) grown at the Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing City (E 116°25′, N 39°47′). S. officinalis is not an endangered or protected species, so no specific permissions were required for sample collection. The taxonomic identity of S. officinalis was confirmed by Dr. Yaodong Qi of the Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing. The S. officinalis plant was photographed by Dr. Xinlei Zhao, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing (Figure 1). The voucher specimen of S. officinalis was deposited at the Institute of Medicinal Plant Development and assigned the accession number so-01-yhy. The S. officinalis leaves were kept at −80 °C until use. For DNA sequencing, a plant genomic DNA kit (Tiangen Biotech, Beijing, Co., Ltd., Bejing, China) and an RNAprep Pure Plant Kit (Tiangen Biotech Beijing Co., Ltd) were used to isolate the total DNA and RNA from the fresh S. officinalis leaves, respectively. The DNA and RNA yield and purity were assessed using a Nanodrop spectrophotometer 2000 (Thermo Fisher Scientific, Waltham, MA, USA) and $1.0\%$ agarose gel electrophoresis. ## 4.2. DNA and RNA Sequencing For short read DNA sequencing, the DNA was first fragmented into 350 bp long fragments using the Covaris S2/E210 ultrasonicator (Covaris Inc., Woburn, MA, USA). The sequencing library was constructed with the NEBNext Ultra DNA Library Prep Kit for Illumina (NEB) according to the manufacturer’s instructions. The library was then subjected to pair-end sequencing (2 × 150 bp reads) on an Illumina HiSeq 2500 sequencer using the standard protocol (Illumina, Inc; San Diego, CA, USA) at Grandomics Biotechnology Co., Ltd. (Wuhan, China). For long read DNA sequencing, the large DNA fragments (>10 kb) were selected and enriched by the Short Read Eliminator XS kit (Circulomics, Inc., Baltimore, MD, USA). The sequencing library was constructed using the Ligation Sequencing Kit SQK-LSK109 (Oxford Nanopore Technologies, Cambridge, UK) and sequenced on an R9.4.1 flow cell on the MinION sequencer at Grandomics Biotechnology Co., Ltd. The ONT MinKNOW v19.12.5 software was utilized for base calling. For the total RNA sequencing, the rRNA in the total RNA was removed using a Ribo-ZeroTM Magnetic Kit (Epicenter, Madison, WI, USA). The sequencing library was prepared with a VAHTS Universal V8 RNA-seq Library Prep Kit (Vazyme, Nanjing, China) according to the manufacturer’s instructions. The prepared library was sequenced on an Illumina HiSeq 2500 sequencer (2 × 150 bp reads) at Grandomics Biotechnology Co., Ltd. ## 4.3. Mitogenome Assembly and Annotation To acquire the complete sequence and uncover the possible structures of the mitogenome of S. officinalis (so-01), we performed a hybrid assembly strategy. We extended the mitochondrial short reads using the GetOrganelle (v1.6.4) toolkit. Subsequently, the GetOrganelle-extended reads were de novo assembled into a unitig graph using the SPAdes software packaged in the Unicycler (v0.4.9) [74]. Lastly, the DBSs in the unitig graph were resolved using the Unicycler with the Nanopore long reads. To verify that the DBSs were resolved correctly, we extracted the repeat sequence and its 1000 bp long flanking sequences and combined them to form the reference sequences for the four conformations. The Nanopore long reads were then mapped to the DBSs of the reference sequences using the BWA (v0.7.12-r1039) [75] with default parameters. Subsequently, the Nanopore long reads and the Illumina short reads were mapped back to the complete sequences of mitogenome using the BWA (v0.7.12-r1039) [75] with default parameters. The coverage depth of the long and short reads mapped to the S. officinalis mitogenome sequences was obtained using samtools (v1.3.1) [76]. For mitogenome annotation, we used a new custom program: MGA (http://www.1kmpg.cn/mga (accessed on 10 November 2022)). Finally, we carefully checked the start and stop codon positions and intron/exon boundaries of each gene using the program Apollo [77]. Maps of the circular S. officinalis mitogenome were drawn using PMGView (http://www.1kmpg.cn/pmgview (accessed on 10 November 2022)). The mitogenome sequences of S. officinalis were deposited in GenBank under the accession numbers OQ001564 and OQ001565. For the collinear analysis of the Salvia mitogenomes, we used the Nucmer module in Mummer (v3) [47] with the parameters used in a previous report [78]. Briefly, the identity threshold was set to $85\%$ and the alignment mode was many-to-many. The results of the collinear analysis were visualized using RIdeogram in the R package [79]. To identify the presence/absence of polymorphisms in the introns’ contents, we checked the intron contents of ten Lamiales species using MGA (http://www.1kmpg.cn/mga (accessed on 15 November 2022)). We named the introns following the scheme proposed by a previous report [52], which denoted each intron according to the positions of its orthologous genes in Marchantia polymorpha. ## 4.4. Analysis of the Homologous Recombination To identify the repetitive sequences in the S. officinalis mitogenome that might mediate homologous recombination, we conducted a self-to-self comparison of the mitogenome using BLASTn (v 2.10.1+) with the parameters e-value < 1 × 10−6 and word_size = 7, as described previously [46]. To detect the possible recombination products of these homologous sequences, we extracted the homologous sequences and their 1000 bp long flanking sequences and combined them to form the reference sequences corresponding to the recombination products. Subsequently, we mapped the Nanopore long reads to these reference sequences and counted the numbers of reads spanning the homologous sequences. To validate the presence of the homologous recombination products supported by the Nanopore long reads, we designed primers according to the corresponding reference sequences using Primer-BLAST [80]. PCR amplification was conducted for a total volume of 25 µL that contained 12 µL of 2 × Taq PCR Master Mix (TransGen Biotech, Beijing, China), 11 µL of water, 0.5 µL of each primer, and 1 µL of DNA. We performed the PCR reactions on a Pro-Flex PCR system (Applied Biosystems, Waltham, MA, USA) under the following conditions: denaturation at 94 °C for 2 min; then 35 cycles of 94 °C for 30 s, 57 °C for 30 s, and 72 °C for 60 s; and extension at 72 °C for 2 min. We visualized the PCR amplicons with $1.0\%$ agarose gel electrophoresis and sequenced the PCR amplicons using the Sanger sequencing technology at SinoGenoMax Co., Ltd. (Beijing, China). ## 4.5. SSRs and Tandem Repeats Analysis To analyze the SSRs in the S. officinalis mitogenome, we used the MISA web service [56], with 10 as the number of mononucleotide repeat units; 5 as the number of dinucleotide repeat units; 4 as the number of trinucleotide repeat units; and 3 as the number of tetra-, penta-, and hexanucleotide repeat units. The tandem repeats were determined using Tandem Repeats Finder (v 4.09) [81] with the command line “trf mitogenome.fasta 2 7 7 80 10 50 500”. The parameter settings were 2 for matches; 7 for mismatches and indels; and 50 and 500 for the minimum alignment score and maximum period size, respectively. ## 4.6. Identification of Mitochondrial Plastid DNA (MTPT) We compared the sequences of the mitogenome and plastome (NC_038165.1) using BLASTn (v 2.10.1+) with an e-value < 1 × 10−6 and a word size of 7 [46]. All hits were annotated to check the genes located in the MTPTs. Graphic representations of MTPTs in the mitogenome and plastome were created using TBtools (v 1.076) [82]. To validate the presence of MTPTs, we extracted the MTPTs and their 2000 bp long flanking regions on each side as reference sequences. We then mapped the Nanopore long reads to these reference sequences with BWA-MEM [83]. The number of long reads that spanned the MTPTs was counted. We utilized Integrative Genomics Viewer (IGV) software (v 2.15.1) [84] to visualize the reads mapped to the reference sequences. ## 4.7. Phylogenetic Analysis of the 10 Lamiales Species Based on Common Mitochondrial Protein Sequences For the phylogenetic analysis, we retrieved the mitogenome sequences from S. officinalis (OQ001564 and OQ001565), ten Lamiales species, and two outgroup species. The ten Lamiales mitogenomes include Ajuga reptans (NC_023103.1), Rotheca serrata (NC_049064.1), Scutellaria tsinyunensis (MW553042.1), S. miltiorrhiza (NC_023209.1), Erythranthe lutea (NC_018041.1), Castilleja paramensis (NC_031806.1), Utricularia reniformis (NC_034982.1), Dorcoceras hygrometricum (NC_016741.1), Osmanthus fragrans (NC_060346.1), and Hesperelaea palmeri (NC_031323.1). The two outgroup species were from the order Solanales: Nicotiana tabacum (NC_006581.1) and Solanum lycopersicum (NC_035963.1). The CDs of the 26 PCGs (atp1, atp4, atp6, atp8, atp9, ccmB, ccmC, ccmFc, ccmFn, cob, cox1, cox2, cox3, matR, mttB, nad1, nad2, nad3, nad4, nad4L, nad5, nad6, nad7, nad9, rps12, and rps13) present in all these thirteen mitogenomes were extracted using PhyloSuite (v1.2.1) [85]. The sequences of each gene were aligned with MAFFT (v7.450) [86] and concatenated into a data matrix using PhyloSuite. The ML tree was constructed using RAxML (v8.2.4) [87] with the parameters “raxmlHPC-PTHREADS-SSE3-f a-N 1000-m PROTGAMMACPREV-x 551314260-p 551314260-o Nicotiana_tabacum, Solanum_lycopersicum-T 20.” The bootstrap support values of each branch in the phylogenetic tree were generated based on 1000 replicates. The BI of the phylogeny was performed using MrBayes (v3.2.7) [62] according to the appropriate model (TVM+I+G), and the parameters were determined by jMdoleTest (v2.1.0) [88]. All resulting trees were visualized with iTOL viewer (https://itol.embl.de (accessed on 20 November 2022)). The CDs of the 56 common PCGs (atpI, atpF, rps15, atpE, rpl22, rpl16, psbK, psbF, petD, rps3, petA, psaC, rpl14, clpP, rbcL, rps2, rps16, psbH, petL, atpA, rpoB, psbJ, petN, rpl20, rps11, ycf4, accD, rpl2, psbA, psbM, rps4, psbD, rpoC2, petG, matK, rpoA, petB, ycf1, rpl23, psbL, rps8, ycf3, psbC, psbN, rps18, ycf2, rps14, rpl33, atpH, psbE, rpl36, psaB, psbT, psaA, rps7, and rpoC1) of plastomes of thirteen species—Ajuga reptans (NC_023102.1), Rotheca serrata (MN814867), Scutellaria tsinyunensis (NC_050161.1), S. miltiorrhiza (NC_023431.1), S. officinalis (NC_038165.1), Erythranthe lutea (NC_030212.1), Castilleja paramensis (NC_031805.1), Utricularia reniformis (NC_029719.1), Dorcoceras hygrometricum (NC_016468.1), Osmanthus fragrans (NC_042377.1), Hesperelaea palmeri (NC_025787.1), Nicotiana tabacum (NC_001879.1), and Solanum lycopersicum (NC_007898.1)—were subjected to phylogenetic analysis by utilizing the same methods employed for the mitogenome. ## 4.8. Detection of RNA Editing Sites We extracted the CDs of each PCG with the 100 bp long flanking regions as reference sequences. We then mapped the strand-specific RNA-seq reads to these reference sequences using HISAT2 (v 2.2.1) [89] with the parameters “--rna-strandness RF--sensitive --no-mixed--no-discordant” [90]. We subsequently used REDItools (v 2.0) [91] with the parameters coverage ≥ 5 and frequency ≥ 0.1 to detect the RNA editing sites [43]. Lastly, we employed IGV software (v 2.15.1) [84] to visualize the mapping results at the RNA editing sites with the minor variant frequency ≥ 0.1. To detect SNP sites, we mapped the DNA sequencing reads to the reference sequences extracted above using BWA (v0.7.12-r1039) [75] with default parameters, as described before [52,90]. The SNP sites were identified with REDItools (v 2.0) by employing the following parameters: coverage ≥ 5 and frequency ≥ 0.1. To validate the RNA editing sites detected according to the RNA-seq data, we randomly selected eleven PCGs for PCR amplification and Sanger sequencing of the PCR products. The genomic DNA was the one extracted in the DNA sequencing experiment. To obtain the cDNA, the RNA extracted in the RNA sequencing experiment was subjected to reverse transcription reactions in a 20 μL reaction system containing 1 μg of total RNA, random primers, and 200 U of SuperScript III Reverse Transcriptase (TransGen Biotech, Beijing, China). We utilized the CDs of the selected PCGS as templates and designed primers using the Primer-BLAST. Then, the genomic DNA and cDNA of so-01 were used as templates for PCR amplification under the same conditions described above. The PCR products were subjected to Sanger sequencing at SinoGenoMax Co., Ltd. (Beijing, China). ## 5. Conclusions We found that the predominant conformation of the S. officinalis mitogenome is two circular chromosomes. 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--- title: Exploring First Responders’ Use and Perceptions on Continuous Health and Environmental Monitoring authors: - Jacob Grothe - Sarah Tucker - Anthony Blake - Chandran Achutan - Sharon Medcalf - Troy Suwondo - Ann Fruhling - Aaron Yoder journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048923 doi: 10.3390/ijerph20064787 license: CC BY 4.0 --- # Exploring First Responders’ Use and Perceptions on Continuous Health and Environmental Monitoring ## Abstract First responders lose their lives in the line of duty each year, and many of these deaths result from strenuous physical exertion and exposure to harmful environmental agents. Continuous health monitoring may detect diseases and alert the first responder when vital signs are reaching critical levels. However, continuous monitoring must be acceptable to first responders. The purpose of this study was to discover first responders’ current use of wearable technology, their perceptions of what health and environmental indicators should be monitored, and who should be permitted to monitor them. The survey was sent to 645 first responders employed by 24 local fire department stations. A total of 115 ($17.8\%$) first responders answered the survey and 112 were used for analysis. Results found first responders perceived a need for health and environmental monitoring. The health and environmental indicators that respondents perceived as most important for monitoring in the field were heart rate ($98.2\%$) and carbon monoxide ($100\%$), respectively. Overall, using and wearing monitoring devices was not age-dependent and health and environmental concerns were important for first responders at any stage of their career. However, current wearable technology does not seem to be a viable solution for first responders due to device expense and durability issues. ## 1. Introduction Being a first responder requires strenuous physical exertion in addition to coping with environmental hazards. This occupation is physically, mentally, and emotionally demanding. Each year first responders experience high-injury rates from accidents, musculoskeletal health complications, and sudden premature deaths. Among these occupational health events, coronary heart disease (CHD) is the leading cause of death for first responders, and nearly half of the CHD events occur during first responder activity [1]. There are many different risks for first responders. Between the years 1994 and 2004, nearly 368 United States (U.S.) career first responders lost their lives while on duty, with $39\%$ of those deaths resulting from myocardial infarction (MI) [1]. Cancer has also been identified as a health concern for first responders. A study identifying 3996 male firefighters found them to have significantly elevated risks for melanoma, multiple myeloma, acute myeloid leukemia, and cancer of the esophagus, prostate, brain, and kidney [2]. Compared to the total number of emergencies to which first responders respond, fires are relatively rare and yet most duty-related CHD events happen during fire suppression responses. Fire suppression increases the heart rate and blood pressure levels to maximal age-predicted levels during the event and exposes first responders to extreme temperatures. However, not all MI events result in death; for at least every one fatal MI, seventeen non-fatal MI events occur in the United States [1,3]. Common contributors to the morbidity and mortality from MI and CHD are the long shifts, psychological stressors, the heavy weight of their personal protective equipment (PPE), the amount of exposure to smoke, and chemicals, as well as the high levels of physical exertion [4]. These stressors are some of the leading explanations for mortality and morbidity in first responders with underlying left-ventricle hypertrophy or CHD [4]. Prolonged periods of high strenuous activity can lead to a decrease in myocardial function, causing a decrease in the systolic and diastolic functions of the heart and leading to potentially fatal outcomes (i.e., MI) [5]. Another concern is the increase in the prevalence of obesity and overweight among firefighters, especially in the U.S. population [6]. Poston et al. tested first responders’ body mass index (BMI) in addition to their body fat percentage to determine if they are genuinely classified as overweight or obese [2011] [6]. Evidence has shown obese and overweight individuals have a higher risk of developing cardiovascular disease [6]. From the study, 667 first responders were measured, and the overall average BMI was greater than 25 [6]. Additionally, $32.7\%$ who were classified as overweight using BMI testing were obese based on body fat percentages [6]. Hypertension among first responders is approximately 20–$30\%$, with $50\%$ having prehypertension, and it is expected to increase due to the obesity epidemic in the United States [3,5]. Hypertension causes vascular changes to the cardiovascular system and can lead to complications such as left ventricle hypertrophy and atherosclerosis. Hypertension can also increase the risk of sudden onset events such as MI, stroke, and heart failure [7]. Left-ventricle hypertrophy was documented during autopsies in nearly $56\%$ of first responders who had a fatal occurrence of heart disease, which suggests that uncontrolled hypertension may be present in a majority of first responders [3]. The combination of risk factors mentioned above, and physiological responses, such as increased heart rate, blood pressure, and body temperature that occur during first responder activity, increase the likelihood of cardiovascular disease (CVD). Multiple studies have demonstrated that first responders’ physiological values increase significantly during strenuous first responder physical activities. For instance, a study on career and volunteer firefighters between the age of 18–24 with no history of CVD, smoking, or diabetes tested the effects of PPE weight during physical simulations. The results demonstrated an increase in both heart and blood pressure during activities wearing and not wearing PPE. The study, however, showed a larger increase in heart rate and blood pressure while wearing PPE [8]. Another study observed a 3 h live-fire training exercise with 40 male firefighters with a mean age of 27.4 years and found the heart rate to increase, ranging between 159 to 213 bpm during fire suppression [5]. These findings are consistent with other findings analyzing heart rate or blood pressure. Regardless of age, body weight, type of physical stimulation, or the timing of each activity, each study showed an increase in heart rate to near to max age-predicted values [1,8,9,10,11,12,13]. Only a few studies collected data on the first responders during recovery between short bouts of high physical exertion. These studies found similar results, namely, that recovery in between each session of work is not adequate for a full recovery of the first responder [8,11,13]. For instance, 16 male firefighters with a mean age of 29 years and a mean weight of 91.2 kg had their heart rate, membrane temperature, blood glucose levels, and blood lactate recorded before the drill, after 8 min and 16 min within the task, and 10 min after recovery. In addition, they were tested in two different temperature environments: one being neutral temperature (13.7 °C) and the other being 89.6 °C. The heart rate within the 89.6 °C environment increased the heart rate to age-predicted values and remained high (mean of 137.33 ± 8.19) even after 10 min of recovery [13]. Another study reported on nine firefighters with a mean age of 32.8 years and analyzed a 3 h live-fire training exercise and its effects on core temperature and heart rate. The 3 h event had four work cycles lasting between 15 and 30 min with rest periods lasting between 20 and 40 min. This work-rest ratio is seen in real live firefighting events. The heart rate continued to increase after each activity, peaking at 188 bpm. Additionally, body temperature increased after each event, peaking at 38.72 °C [11]. Even during the 20–40 min recovery time, the body does not fully recover after each event because the heart rate does not return to near resting levels before the next work cycle [11]. Heart rate and blood pressure are not the only physiological changes that can increase the risk of CVD. Hyperthermia, the increase in body temperature, may trigger sudden cardiac events. A systematic review evaluating studies that recorded heart rate and temperature reported that body temperature was shown to increase on average by 0.9 °C during firefighting activities. The increase in body temperature was caused by exposure to heat while wearing a self-contained breathing apparatus (SCBA) and PPE, which do not favor thermoregulation. PPE has been shown to increase skin temperature and internal temperature as well as decrease heat transfer due to the limited permeability of water vapor [4]. An individual who experiences heat stress causes a significant strain on the cardiovascular system. Heat stress has been revealed to cause a significant reduction in blood volume, especially in organs such as the heart, liver, and spleen by nearly $14\%$ or higher [14]. In addition to blood volume reduction, heat stress causes a reduction in ventricular filling pressure and stroke volume, which is accompanied by an increase in heart rate. This results in a significant reduction in cardiac output [14]. In combination with whole-body exercise, heat stress can cause cardiovascular strain due to the reduction in cardiac output, stroke volume, and even blood flow to the muscles and brain [14]. ## 1.1. Wearable Health Monitoring Devices Many of these physiological changes that occur during these events can be measured by continuous wearable health-monitoring devices allowing first responders to assess their occupational health better. These systems can provide real-time feedback on one’s health information. Additionally, they can alert the first responders when acute life-threatening conditions are imminent. Continuously tracking first responders’ health physiological changes may also lead to an early diagnosis of disease, as well as early treatment, ultimately improving their quality of life [15,16]. Continuous monitoring can provide more information on a person’s health, such as arrhythmias, which can be an indicator of heart disease [15]. Additionally, the measurement of multiple vital signs, such as blood pressure, heart rate, respiratory rate, and blood oxygen saturation levels, can bring greater insight into the pathophysiology of disease and new indications of physiological markers of disease status [15]. ## 1.2. Vital Signs Heart rate, blood pressure, respiratory rate, blood oxygen saturation, and body temperature are five vital signs that can be considered essential in providing accurate information about an individual’s health [17]. Electrocardiograms (ECGs) are a widely used biometric instrument that evaluates cardiac rhythm through electrical analysis. ECG can also serve as a diagnostic tool within healthcare and can predict acute MI and other coronary events, such as atherosclerosis, CHD, tachycardia, and bradycardia [17]. Respiratory Rate (RR) can determine cases of distress that can cause hypoxia. Further, respiratory rate can detect other diseases such as chronic pulmonary disease, which are risk factors for the onset of CVD and acute MI. When low percentages are present in blood oxygen saturation levels during an event such as hypoxia, this causes insufficient oxygen (O2) supply to the body. As mentioned previously, an increase in body temperature, which can be affected by extreme heat, places first responders at risk for CVD [4]. ## 1.3. Environmental Factors Measuring the environment also has its importance. Many exposures within the environment can potentially lead to diseases, such as radon, causing lung cancer, arsenic causing, cancer of several organs, and particulate matter, causing cardiorespiratory diseases [18,19]. Furthermore, according to the Agency for Toxic Substances and Disease Registry (ATSDR), ammonia can lead to airway destruction causing respiratory failure [20]. Hydrogen sulfide (H2S) in high concentrations quickly leads to death [21]. Lastly, carbon monoxide (CO) can lead to cardiovascular diseases or death if exposure is high [22]. First responders are potentially exposed to these toxic chemicals during fire suppression because modern building materials and furnishing are becoming more synthetic and, when combusted, release these toxic gas byproducts [23,24]. Other environmental exposures include noise. According to OSHA, high levels of noise can cause permanent hearing loss and loud noise can create physical and psychological stress, reduce productivity, and interfere with communication and concentration which all are important factors to first responders [25]. Each year numerous first responders lose their lives due to MI or CVD; these numbers could be reduced if first responders had more preventive measures in place. Continuous monitoring has the potential to detect diseases early by providing information about the health of the firefighters during emergencies [26]. Furthermore, information regarding the environment can provide firefighters with essential information when arriving at an emergency event. Due to the health conditions mentioned previously, there are apparent reasons for first responders to be monitored; however, there is limited research exploring first responder’s current use of health monitoring technology, which physiological or environmental indicators are perceived as important to them, and/or who should monitor their information during emergencies. Therefore, the study objectives for this project are as follows:Determine how many of the first responders are currently using wearable technology. This gives insight into what health indicators are already monitored and the kind of systems being used. Determine which health and environmental indicators are perceived to be important to first responders. There are many different indicators, and identifying which ones are most important can help a monitoring system focus on those indicators. Determine who should monitor the first responders’ health status and environmental status within the field. Examine if age influences the responses to health monitoring. Examine if the role (e.g., special operations) influences the responses to health monitoring. Examine if age influences the responses to environmental monitoring. Examine if the role (e.g., special operations) influences the responses to environmental monitoring. ## 2.1. Sampling of Participants The survey was administered in 2019 to first responders who worked in the local area fire departments. First responders from all twenty-four fire departments were eligible to participate in the study, and there were no inclusion/exclusion criteria for the study. Before being administered, the survey was approved by the Institutional Review Board (IRB) (IRB # 691-17-EX) of the University of Nebraska Medical Center (Omaha, NE, USA). ## 2.2. Measurements A 16-question survey was developed to answer the research questions. These health and environmental key variables were identified in our previous study [27]. Three demographic questions assessed the location of the participant’s fire station by zip code, whether the participant was part of special operations, and what year that participant was born. In general, special operations first responders often work in emergency medical care, technical rescue, hazardous materials mitigation, etc. Four questions measured whether the participant ever used wearable technology, and if the participant answered yes to ever using wearable technology, then they were asked if they use wearable technology and how confident they were when operating wearable technology. If the participant answered no to ever using wearable technology, then they were asked why they do not use wearable technology. Three questions evaluated which health information would be useful to monitor when working in the field and who should monitor standardized emergency management system (SEMS) operator, which is someone monitoring a system that displays vital information of the first responder, and the first responder themselves. Lastly, two questions asked the participant if any additional types of health or environmental information should be monitored that were not listed as an option within the survey. ## 2.3. Statistical Analysis Questionnaires with incomplete information were excluded from the analysis. Using SAS Software (version 9.4; SAS Institute, Inc., Carly, NC, USA), the association between non-special operations and special operations and current use in technology, the types of health and environmental information that should be monitored, and who should monitor that information were determined using univariate chi-square analysis. A univariate chi-square analysis was also used to assess if the current use of technology, the types of health and environmental information that should be monitored, and who should monitor that information was associated with the age of the individual. Age was categorized into two groups based on the mean age of the respondents. The age was not normally distributed, so we selected 42 based on the distribution where it naturally broke at the mean age of 42. Respondents were either placed in a group below the age of 42, or respondents were placed in a group equal to or greater than the mean age of 42 years. The final sample size for analysis to examine the association between non-special operations and special operations as well as an association between the age of the respondents was $$n = 112$.$ Responses with low frequencies for health information variables were re-coded and categorized dichotomously. The “no” and “don’t” know responses were combined to “no” and the yes responses remained “yes” since we were only interested in who desired a health variable to be monitored. Similarly, we re-coded environmental factor variables dichotomously. We combined the responses for who should monitor that information for the same reason. The new categories for who should monitor that information were “SEMS operator and myself,” “SEMS operator only,” “myself,” and “other”. Finally, for the wearable technology use variable concerning how confident they were in using wearable technology we combined the “very confident” and “somewhat confident” responses into one response category renamed “low confidence” and the extremely confident response was renamed “high confidence”. ## 3.1. Study Subject Characteristics Responses were collected from 115 out of the 645 first responders in the local fire department. From the 115 responses, only two respondents were omitted from the analysis due to missing data. Out of the 113 who completed the questionnaire, 112 answered the question, “Whether they are part of the special operations group?”. Based on that question, there were 78 ($70\%$) who were not part of the special operations group, and 34 ($30\%$) who were part of the special operations group. Individuals had an average age of 42 ± 7.6 years standard deviation (SD). Out of those, 45 ($40.18\%$) participants were under the age of 42, and 67 ($59.82\%$) participants were greater than or equal to 42 years of age. ## 3.2. Current or Past Use of Technology and Confidence Among the 112 responses to the question of whether they have ever used wearable technology (i.e., Fitbit, Smartwatch), 53 ($47.32\%$) selected yes. Among the 53 respondents, 31 ($58.49\%$) of the respondents selected that they were currently using wearable technology. In addition, 16 of the 31 ($51.61\%$) reported high confidence in their ability to operate wearable technology and 15 of the 31 ($48.39\%$) reported low confidence. Table 1 presents the results of a univariate analysis that assessed the association between special operations and non-special operations and wearable technology. Based on Table 1, there was no significant association between ever using wearable technology ($$p \leq 0.11$$), the current use of wearable technology ($$p \leq 0.86$$), and the confidence in using wearable technology ($$p \leq 0.11$$). Table 2 reveals the results of the univariate association between the age groups (Age ≥ 42 or Age < 42) and wearable technology. Similar results were observed: there was no significant difference in age and between ever using wearable technology ($$p \leq 0.30$$), the current use of wearable technology ($$p \leq 0.59$$), and the confidence in using wearable technology ($$p \leq 0.86$$). ## 3.3. Monitoring in the Field A total of 70 out of 110 ($63.64\%$) respondents preferred their health indicators monitored by both a SEMS operator and themselves while working in the field. Twenty-seven [27] ($24.55\%$) preferred to monitor themselves only, nine ($8.18\%$) preferred to have the SEMS operator be solely responsible, and four ($3.64\%$) accounted for all other responses. Table 3 and Table 4 show the results of a univariate analysis that examined the association between the special operations group and age group and who should monitor their health while in the field. Both analyses show no significant difference in responses between the two groups. A total of 71 out of 110 ($63.64\%$) respondents preferred environmental indicators monitored by a SEMS operator and themselves while working in the field. Eighteen [18] ($16.36\%$) preferred to have the SEMS operator be solely responsible, sixteen [16] ($14.55\%$) preferred to monitor themselves only, and six ($5.45\%$) accounted for all other responses. Table 3 and Table 4 show the results of the univariate analysis, and both show no significant difference in responses between the two groups. ## 3.4. Health Monitoring The health indicators that respondents perceived as important for monitoring in the field are arranged in order of highest to lowest: heart rate ($98.2\%$), blood pressure ($93.7\%$), core body temperature ($89.1\%$), hydration level ($87.2\%$), and skin temperature ($67.3\%$). Additionally, $87.4\%$ selected their breathing rate to be monitored, $51.4\%$ selected falls, and $48.6\%$ selected stability. Of the respondents, $91.0\%$ chose blood oxygen levels, $86.0\%$ chose respiration carbon dioxide (CO2) levels, $86.5\%$ selected cortisol levels (stress), $71.6\%$ selected skin resistance, and $64.6\%$ selected breathing depth levels to be monitored while working in the field. Table 5, Table 6, Table 7 and Table 8 reveal the results of the univariate analysis regarding health monitoring within the field. Cortisol levels (stress) ($$p \leq 0.02$$) and skin resistance and hydration levels ($$p \leq 0.05$$) resulted in significant correlations between the age of respondents. No other health indicators revealed any significant association between the special operations analysis and the age analysis. The analysis of special operations groups and heart rate could not determine significance due to low cell counts. Similarly, the analysis of age groups and heart rate as well as blood pressure could not determine significance due to low cell counts. ## 3.5. Environmental Monitoring Respondents selected the following environmental agents/factors/attributes to be monitored: $100\%$ selected CO, $99.1\%$ H2S, $96.0\%$ combustible gas, $95.4\%$ O2, $88.4\%$ particulates, $88.4\%$ biological proteins, $87.3\%$ CO2, $82.7\%$ radiation, $78.6\%$ ammonia, and $59.4\%$ selected pH to be monitored while in the field. Additionally, $97.3\%$ selected a lower exposure limit (LEL), $90.1\%$ selected the temperature inside the suit, and $82.7\%$ chose the temperature outside the suit to be monitored. Further, $69.7\%$ selected humidity inside the suit, $68.8\%$ selected humidity outside the suit, $53.2\%$ chose noise levels inside the suit to be monitored, and $52.8\%$ selected noise levels outside the suit. In addition, $98.2\%$ chose hydrogen cyanide (HCN), $83.8\%$ selected volatile organic compounds (VOCs), and $78.4\%$ selected polyhalogenated compounds (PHCs) to be monitored while working in the field. Inside versus outside of the suit had an overlap of responses as it was the same respondents. Table 9, Table 10, Table 11 and Table 12 present the results of the univariate analysis between the environmental indicators and the special operations analysis and the age analysis. Regarding special operations analysis, only biological proteins showed the significance of the association between the special operations and non-special operation groups ($$p \leq 0.03$$). Regarding age analysis, only carbon dioxide showed the significance of the association between age ≥ 42 and age < 42. Carbon monoxide could not determine significance due to all the respondents responding yes to the monitoring of carbon monoxide, and similar results were observed for the age analysis. The analysis of special operations groups could not determine the significance of hydrogen sulfide, combustible gas, lower explosive limit, and hydrogen cyanide due to low cell counts. The analysis of age groups could not determine the significance of hydrogen sulfide, lower explosive limit, and hydrogen cyanide due to low cell counts. ## 3.6. Additional Comments The survey included three open-ended questions:Why respondents do not use wearable technology. Whether any additional types of health indicators should be monitored. Whether any other types of environmental hazards should be monitored. Regarding wearable technology, sixteen respondents commented that current wearable devices were too expensive, and sixteen respondents said there was no need for them to wear them. One respondent commented, “I used to wear a watch while on duty for the more accurate taking of patients’ pulses. With the constant unknown of what type of call we would be going on, I would wear the watch all day every day. When we would get a call where we would need to put our bunker gear on, I would forget I was wearing the watch, and after so many times of putting the bunker gear on and working with it on, it would eventually break the bands on my watches. So, I quit wearing them as I was tired of replacing them.” Another commented that “… firefighting can easily damage wearable technology if it is not robust.” As for health monitoring, five respondents would prefer blood glucose levels to be added as a physiological indicator. One respondent indicated that radon levels should be monitored at all stations, and one commented that wind direction and speed should be added as an environmental indicator that should be monitored. ## 4. Discussion The overall goal of this exploratory study was to analyze the perceptions of first responders on using wearable technology and attitudes toward health and environmental monitoring. Based on current knowledge, this is the first survey to understand the views of first responders on wearable technology and health and environmental monitoring. Our study identified five essential findings: [1] more than half ($53\%$) of the respondents do not wear wearable technology for such reasons as they are expensive and break too easily, while $47\%$ of respondents said yes to ever using wearable technology; [2] they prefer themselves and the SEMS operator to monitor their status while on shift; [3] the most essential health information to monitor was heart rate, blood pressure, cortisol levels, respiration carbon dioxide, blood oxygen saturation, respiratory rate, hydration level, and body temperature; [4] most of the environmental exposures first responders are exposed to are essential to be monitored, which led $100\%$ of the respondents to select carbon monoxide to be monitored and $99\%$ of the respondents to select hydrogen sulfide; [5] there was no significant difference between first responders that participated in special operations or non-special operations regarding the importance of health monitoring preferences; [6] there was a significant association between age group of the respondent and cortisol levels (stress) ($$p \leq 0.02$$) and a borderline significant association between age group of the respondent and skin resistance ($$p \leq 0.05$$); [7] there was a significant association between special operations and biological proteins ($$p \leq 0.03$$); and [8] there was a significant association between age group of the respondent and carbon dioxide ($$p \leq 0.03$$). Environmental monitoring regardless of special operations vs. non-special operations or the age of the first responder was deemed important except for one variable, biological proteins. Special operations had a higher preference for monitoring biological proteins compared to non-special operations. Environmental monitoring regardless of age group (≥42 or <42) was also deemed important except for one variable, carbon dioxide. The age group < 42 had a higher preference for monitoring carbon dioxide compared to the ≥42 age group. There is no explanation for these observed differences. The sample size was small, which limits the power of the analysis. Age might be a confounding factor. There were also differences observed between the age of the respondent and two of the health variables. Age < 42 had a higher preference for monitoring stress and for monitoring skin resistance compared to those ≥42. A greater sample size would be needed as well as multivariable regression models to determine whether age and special operations were confounding variables. Overall, the high responses to environmental monitoring align with the concerns that first responders are exposed to toxic chemicals during fire suppression due to the increase in synthetic building materials and furnishing [23,24]. According to the Centers for Disease Control and Prevention (CDC), many of these chemicals, such as VOCs, radon, and HCN, are potential human carcinogens, and exposure is quite common among first responders. Even though first responders wear PPE during emergencies, dermal exposure still occurs. Skin exposure can occur through the first responder’s PPE through the hood, turnout jacket, and trousers [28]. Multiple studies have shown evaluated levels of chemical exposures on the skin following firefighting events [29,30,31,32]. Environmental monitoring also aligns with concerns about heat stress during events. Protective clothing worn by firefighters exacerbates heat exposure due to the limited ability to dissipate heat and moisture from the clothing microclimate to the external environment [33]. One study showed that three different prototype turnout suits significantly increased work time and significantly reduced core body temperature, skin temperature, and physiological strain compared to the control turnout suit [33]. Monitoring core temperature, external environment, and other markers of heat stress during an event may provide insight into areas where heat stress reduction can be improved whether it be protective clothing or other aspects of the job. The data from environmental and physiological monitoring can be used for more than health monitoring by examining areas of first responder duties that can be changed to improve health outcomes such as heat stress. Real-time monitoring of the environment will provide first responders with essential information when entering an incident that may have potential toxic gas exposure, such as fire suppression or hazmat emergencies. Further, it will provide additional information on how they can further protect themselves from exposures and improve health outcomes. Cancer due to potential dermal exposure should not be the only worry for first responders. The CDC has stated that high levels of carbon monoxide (CO) in the environment can lead to CO poisoning, eventually leading to loss of consciousness, weakness, shortness of breath, or even death. Carbon dioxide (CO2) can lead to similar symptoms as CO, in addition to asphyxia, coma, and convulsions, according to The National Institution for Occupational Safety and Health (NIOSH). Another concern is hydrogen cyanide (HCN). According to OSHA, symptoms of HCN gas exposure include nausea, breathlessness, and headaches. All these gases mentioned (e.g., CO, CO2, and HCN) have been shown to exceed their respective short-term exposure limits on some emergency events for first responders [34]. Robustness and durability of wearable technology was a concern among respondents. Fitting sensing technology on clothes instead of a watch or band might be less obstructive and more durable [35]. Although biomonitoring sensors usually require accurate positioning of sensors, which may pose a problem for first responders due to the nature of duties encountered during events [35], the type of sensing technology for first responders is an area for further investigation. The cost might be a barrier for first responders as 16 respondents in our study cited current wearable devices as being too expensive as a reason preventing them from using wearable technology. Interestingly, overall noise was not a significant concern for about half of the respondents in this study. However, other studies have shown first responders’ noise exposure limits exceed OSHA’s permissible exposure limits to noise during an eight-hour work shift [36,37]. Some first responders experience hearing loss early in their careers or accelerated hearing loss during their careers compared to other occupations [38,39]. Hearing is such a crucial part for first responders during emergencies since most of their communications and commands are verbally communicated. Additionally, the noise level has been shown to have a negative impact on CVD because it can increase blood pressure. A systematic review observed CVD in U.S. firefighters, which showed that for every 5-decibel increase in acute occupational noise, systolic blood pressure increases by 0.51 mm Hg. First responders can be exposed to nearly 90 decibels on average and may be exposed to as high as 166 decibels [14]. Possibly, the first responder community does not know the long-term effects of noise levels and health hazards that may follow due to hearing loss, and perhaps more outreach is needed to educate first responders on the long-term effects of hearing loss. Other lesser concerns for first responders are falls and stability issues while working in the field. Even though, according to the National Fire Protection Association, in 2018, 22,975 injuries occurred at fire ground operations nearly $18\%$ of these injuries were due to falls, slips, and jumps [40]. Perhaps first responders see these types of incidents as part of the job, or perhaps a false sense of security is occurring because of the PPE first responders wear while in the field. ## Limitations and Future Directions As in any study, there are a couple of limitations. The first limitation is the generalizability of the study. All the participants were from one large metropolitan fire department consisting of 24 first stations. Further exploration should survey first responders in other locales so the study can be generalized. Another limitation is the small sample size; however, the response rate was good. A greater sample size would be able to better reduce type I and type II errors. Additionally, a multivariable analysis would be able to identify whether age or special operations were confounding factors. The observed significant differences from this study should be interpreted with caution. A possible extension of this study is surveying rural volunteer first responders, thus, providing another perspective. Additionally, other unmeasured factors could be explored with an expanded questionnaire. Although the study has limitations, it does provide valuable insights regarding first responders and their views on health and environmental monitoring while in the field. ## 5. Conclusions In summary, no differences were found between special operations and non-special operations or the age of responders and their perceptions of wearable technology and monitoring their health and environment while in the field. While there were a few significant differences found between the age of responders and health and environmental variables. There was a significant difference between special operations groups for one of the environmental variables. This study did not have the power to assess the strength of association between the observed significant results and require further investigation. It is important to monitor the environment in order to prevent possible negative health effects since the environmental conditions have an impact on first responders’ health. Overall, our study illustrates that using and wearing monitoring devices is not age dependent, and that health and environmental concerns are important for first responders in any stage of their career. 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--- title: Comparison of Three Diagnostic Definitions of Metabolic Syndrome and Estimation of Its Prevalence in Mongolia authors: - Enkhtuguldur Myagmar-Ochir - Yasuo Haruyama - Nobuko Takaoka - Kyo Takahashi - Naranjargal Dashdorj - Myagmartseren Dashtseren - Gen Kobashi journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048927 doi: 10.3390/ijerph20064956 license: CC BY 4.0 --- # Comparison of Three Diagnostic Definitions of Metabolic Syndrome and Estimation of Its Prevalence in Mongolia ## Abstract We sought to estimate the prevalence of metabolic syndrome (MS) in the urban population of Mongolia and suggest a preferred definition. This cross-sectional study comprised 2076 representative samples, which were randomly selected to provide blood samples. MS was defined by the National Cholesterol Education Program’s Adults Treatment Panel III (NCEP ATP III), the International Diabetes Federation (IDF), and the Joint Interim Statement (JIS). The Cohen’s kappa coefficient (κ) was analyzed to determine the agreement between the individual MS components using the three definitions. The prevalence of MS in the 2076 samples was $19.4\%$ by NCEP ATP III, $23.6\%$ by IDF, and $25.4\%$ by JIS criteria. For men, moderate agreement was found between the NCEP ATP III and waist circumference (WC) (κ = 0.42), and between the JIS and fasting blood glucose (FBG) (κ = 0.44) and triglycerides (TG) (κ = 0.46). For women, moderate agreement was found between the NCEP ATP III and high-density lipoprotein cholesterol (HDL-C) (κ = 0.43), and between the JIS and HDL-C (κ = 0.43). MS is highly prevalent in the Mongolian urban population. The JIS definition is recommended as the provisional definition. ## 1. Introduction Metabolic syndrome (MS) is a combination of clustering risk factors, including central obesity, dyslipidemia, hyperglycemia, and hypertension, which eventually lead to cardiovascular diseases (CVDs) and diabetes [1,2,3,4]. The prevalence of MS is increasing worldwide, and many prior studies reported a varying prevalence of MS ranging from $20\%$ to $30\%$ in most countries, depending on the ethnicity, aging, sex, and race of the population [3,4,5,6]. In addition, the prevalence of MS has increased in Asia, including in Mongolia [7,8]. Mongolia has faced increased mortality from non-communicable diseases (NCDs) [9,10], such as CVDs, precipitated by MS. However, there is not yet an established diagnostic definition for MS in the Mongolian population. Therefore, in order to establish adequate criteria, this study compares three diagnostic definitions: the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (NCEP ATP III) [1,2]; the International Diabetes Federation (IDF) [3]; and the Joint Interim Statement (JIS) 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 [4], while adapting them to the Mongolian population. Previous studies have reported the prevalence of MS in Mongolia. A previous Mongolian epidemiological study of MS reported the prevalence by IDF criteria ($32.8\%$) in adults aged ≥ 40 years [11]. Another comparative study established the MS prevalence by the NCEP ATP III criteria (12–$16\%$) in adults aged over 30 years, which was found to be higher than that in the Japanese and Korean populations [12]. A recent study on Mongolian national trends in MS reported that MS increased significantly (p for trend 0.023) when the JIS criteria were followed [13]. These studies have reported basic data on the prevalence of MS. To date, there is scarce information regarding the prevalence of MS in urban adults aged more than 20 years and the comparison of diagnostic definitions, increasing the evidence that early detection and prevention are targeted in the Mongolian urban population. This seemingly different prevalence appears to be due to the use of the different thresholds and set of criteria established in different definitions, with varying cut-off values for waist circumference (WC), high-density lipoprotein cholesterol (HDL-C), or fasting blood glucose (FBG), and having different ways of combining and including them in blood pressure (BP), triglycerides (TG), and medications for hypertension, diabetes, and dyslipidemia to define MS. The NCEP ATP III definition does not require any specific risks, and it recognizes that MS is a complex disorder [1,2]. Therefore, IDF and JIS definitions use WC cut-off points based on ethnicity [3,4], and WC is now recognized as an important factor in the IDF definition [3]. However, the adaptability of different definitions to different populations is always arguable [3]. With the importance of early screening, determining, and diagnosing MS to prevent mortality from this condition, it is crucial to establish a personalized definition of MS in Mongolia, an Asian country where westernization is increasing. Therefore, this study compared the differences in MS prevalence among Mongolian urban adults based on three currently used definitions of MS that have their own features to clarify the epidemiological situation of MS, and provide the necessary evidence for preparing diagnostic definitions. Furthermore, we determined the preferred provisional definition of MS for the Mongolians. ## 2.1. Study Design, Sampling, and Population We conducted a cross-sectional survey on the prevalence of MS in an urban population in Mongolia. The survey followed the guidelines of the World Health Organization (WHO) STEPS Surveillance Manual which provides a complete overview, including guidelines and supporting materials, for countries wishing to undertake NCD risk factor surveys using the WHO STEPwise approach [14], and multistage cluster sampling was conducted for Mongolian residents. First, geopolitical units were sampled and then residents were sampled within these units. In Ulaanbaatar, 142 family healthcare centers (FHCCs) provide primary healthcare services to all citizens. According to the WHO STEPS Surveillance Manual, which recommends that at least 50 primary sampling units (PSUs) be selected from over 100, proportional probability sampling was used to select 52 FHCCs from the eight districts in the first stage of cluster sampling. In the next stage, 88 individuals aged ≥ 20 years were randomly selected from the registers of each of the 52 FHCCs. If the participants could not be reached by the research team, they were replaced by the next participant within the same age and sex category. In total, 4515 urban residents were included in this survey (response rate: $98.7\%$), and 2258 residents in Mongolia underwent biochemical measurements. A pilot study was conducted on five randomly selected FHCCs in November 2017. Data collection was conducted between December 2017 and January 2018, and the final study population included 2076 urban residents. We obtained a de-identified dataset of Mongolians from the Onom Foundation, according to the Data Transfer Agreement. Written informed consent was obtained before conducting interviews and physical measurements. The study was approved by the Medical Ethics Committee of the Ministry of Health, Mongolia, and the Ethical Committee of Dokkyo Medical University (Protocol Number, 2021-014). ## 2.2. Measurements The WHO STEPwise approach is comprised of three steps of risk factor assessment: questionnaire, physical measurements, and biochemical measurements. Before data collection, all field members, who were medical researchers, doctors, nurses, and laboratory technicians, successfully completed 5-day training programs regarding how to conduct interviews, measure anthropometry and BP, and take and collect blood samples. These training programs were organized by the ‘Technical Working Group’ in the Public Health Institute in collaboration with the WHO country office and experts from the relevant cooperating organizations. The pilot study was organized covering all steps of the actual survey. The entire data collection procedure was conducted using an electronic tablet (Fire HD 8, Amazon, Seattle, WA, USA). To avoid data loss, an Android application with an offline mode, QuickTapSurvey (TabbleDabble Inc., Toronto, ON, Canada), was used. Interviews were conducted using the Mongolian version [15] of the WHO STEPS instrument for NCD risk factor surveillance. To ensure the adequacy of the Mongolian translation of the questionnaires, the Mongolian versions were separately back-translated and reviewed by two independent translators. The survey questionnaire was further adapted to country specifics with the help of local experts, the survey ‘Technical Working Group’, and with close collaboration and technical assistance from the WHO. Finally, it was reviewed and approved by international and national experts and consultants. Blood pressure measurement: BP was measured using accuracy-validated BP A6 BTs (Microlife Corporation, Taipei, Taiwan) and digital automatic BP monitors. Participants were instructed to abstain from alcohol, cigarette smoking, caffeine consumption, and exercise for at least 30 min before BP measurement. Data collection teams ensured that participants were seated with their legs uncrossed and their back and arm supported, in accordance with the American Heart Association (AHA) guidelines, and that appropriate cuff sizes were used. After a 10 min rest, BP was measured three times, with 3 min intervals between measurements. The average value of the three measurements was then calculated. If one differed by ≥15 mmHg from the other two, it was discarded [16,17]. Physical measurements: Height was measured in centimeters (without shoes) and weight in kilograms (with heavy clothing removed) using a digital scale. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. We measured the WC of subjects while standing, using a soft tape midway between the lowest rib and iliac crest. Biochemical measurements: All participants agreed that their blood samples would be collected using a clot activator. The serum samples were centrifuged at 3500 rpm for 10 min. Overnight fasting blood samples were obtained for the measurement of serum lipids and glucose. Concentrations of HDL-C, TG, and FBG following standard operating procedure (SOP)-Liver Center-005 protocols were assessed using a fully automated biochemical analyzer (ERBA-XL200, Mannheim, Germany). ## 2.3. Definition of the Metabolic Syndrome The NCEP ATP III definition was chosen in this study because Mongolia, as an Asian country, was reported as having a higher BMI compared to Japan and China [18,19]. When using the IDF and JIS definitions, the other two widely accepted definitions of MS chosen in this study, we followed the Asian population thresholds for abdominal obesity [3,4]: WC ≥ 90 cm for men, and ≥80 cm for women. The NCEP ATP III, IDF, and JIS definitions are different from each other in the diagnostic process (Table 1). ## 2.4. Socioeconomic Status (SES) Variables Education variables were divided into two categories of high school and lower (≤12 years) and higher educational attainment (>12 years) according to the Mongolian education system. Occupational class was defined into three groups: non-manual, manual, and “others” referring to the Erikson–Goldthorpe–Portocarero scheme [20]. Individuals classified as students, retired, and individuals whose stated occupation could not be classified were placed in the “others” group. Monthly income was divided into three types: upper, middle, and lower, according to the average salary per month [21]. The average monthly salary in Mongolian is about MNT 1,500,000 (USD ~450) [22]. Housing was categorized into two types: apartment and Ger district. Ulaanbaatar, the capital city of Mongolia, consists of two different housing-type areas: apartment areas, which are located in the central part of the city; and “Ger areas”, which are a very common housing type among nomads located in the suburbs. ## 2.5. Statistical Analysis Descriptive analyses were used to report the demographic characteristics of the study participants and the prevalence of MS. The sample was divided into three age groups: young adults (<40), middle adults (40–59), and old adults (60 and over) [23]. For validity between the individual MS components and different definitions, we measured the sensitivity and specificity. Cohen’s kappa coefficient ($95\%$ confidence interval (CI)) (poor, κ ≤ 0.20; fair, κ = 0.21–0.40; moderate, κ = 0.41–0.60; substantial, κ = 0.61–0.80; very good, κ > 0.80) was used to determine the level of agreement [24]. As a result of the greater agreement, we used the JIS definition to estimate the odds ratios (ORs) and $95\%$ CIs of SES for MS prevalence using logistic regression. The analyses were stratified according to sex. All p values were two-sided, and the alpha level was set at 0.05. Data were analyzed using SPSS version 28.0 (SPSS Inc., Chicago, IL, USA). Mosaic plots were shown for the relationships between MS and individual MS components using JMP Statistical Discovery Software version 16 (SAS Institute Inc., Cary, NC, USA). ## 3. Results Table 2 shows the characteristics of the study population and the clinical components of MS in Mongolians. Of the total study participants, the mean age was 39.9 ± 13.7 years, and $53.6\%$ were female. The height, weight, WC, BP, and TG levels were all higher in men, whereas the HDL-C levels were lower. The differences in height, weight, WC, and BP between males and females were statistically significant ($p \leq 0.05$). The overall prevalence rate of MS was $19.4\%$ according to the NCEP ATP III, $23.6\%$ according to the IDF, and $25.4\%$ according to the JIS definitions. The prevalence of MS was higher in female participants and in the 40–59 and 60 and over age groups when stratified by sex and age (Figure 1). The agreements between the NCEP ATP III, IDF, and JIS definitions, and the individual MS components are presented in Table 3. For men, a moderate agreement was found between the NCEP ATP III and WC (κ = 0.42), and between the JIS and FBG (κ = 0.44) and TG (κ = 0.46). For women, a moderate agreement was found between the NCEP ATP III and HDL-C (κ = 0.43), and between the JIS and HDL-C (κ = 0.43). Table 4 shows the sex-divided ORs of SES factors for MS prevalence based on the JIS definition, which had moderate agreement between more MS components in men and showed a higher prevalence in both sexes. In men, the 40–59, and 60 and over age groups and married participants were significantly associated with MS, while for women, the 40–59, and 60 and over age groups as well as those with lower educational attainment, “others” group for occupation, and married participants were significantly associated with MS. In the multivariable logistic regression analysis, the 40–59 age group (aOR = 1.44, $95\%$ CI 1.01 to 2.04 in men, aOR = 2.28, $95\%$ CI 1.68 to 3.09 in women) and female 60 and over age group (aOR = 3.22, $95\%$ CI 2.04 to 5.08) in addition to married female participants (aOR = 1.57, $95\%$ CI 1.04 to 2.36) were significantly associated with MS. Mosaic plots showed the MS distribution within the individual MS components by sex. Male participants with elevated MS components had a higher percentage of MS based on the JIS definition except for WC. For females, high glucose levels, and elevated cholesterol level participants had a higher percentage of MS according to the JIS definition. Based on the IDF definition, the elevated systolic blood pressure (SBP) participants had a higher percentage of MS, and the elevated diastolic blood pressure (DBP) participants had a higher percentage of MS based on both JIS and IDF definitions. However, according to the NCEP ATP III definition, the high WC participants had a higher percentage of MS (Figure S1). ## 4. Discussion Using an urban population in Mongolia, we established the prevalence of MS, as $15.5\%$, $21.6\%$, and $23.8\%$, respectively, in men, and $22.8\%$, $25.3\%$, and $26.8\%$, respectively, in women as defined by the NCEP ATP III, IDF, and JIS definitions. Concerning the level of agreement, JIS definition had moderate agreements with more MS components in men, while both the NCEP ATP III and JIS definition had moderate agreements with HDL-C in women. According to our knowledge, this is the first study determining the preferred provisional definition of MS for the Mongolians. Considering the greater emphasis of the JIS definition on FBG and TG for Mongolian men, and on HDL-C for Mongolian women, a moderate agreement between the JIS and these components is plausible. Furthermore, the agreement of the JIS definition on FBG and TG was slightly higher in women compared to the agreements of the other two definitions. However, the agreement of the JIS definition on WC was lower in both sexes than the agreements of the other two definitions because WC suffers from a higher measurement error [25,26]. Regarding the high prevalence of CVDs among the Mongolian population, applying these criteria to identify persons at risk might be helpful. In addition, the prevalence of MS measured using the JIS definition was higher in both sexes than that measured using other definitions. The Mongolian government has been building policies to prevent and control NCDs [27] and to set the Mongolia Sustainable Development Vision 2030 report, which aims to reduce the main NCDs, such as CVDs, health risk factors, and preventable deaths [28]. For policy responses, addressing MS is necessary. According to the national NCD STEP surveys, the prevalence of most NCD risk factors remained stable, while that of overweight and obesity increased [29,30]. Thus, the JIS definition could be used to identify more people at high risk and make early interventions for controlling MS in urban populations. A moderate agreement was more related to elevated glucose and cholesterol levels. In Mongolia, the incidence of diabetes and dyslipidemia has increased and is more common in the urban areas [29,31]. A cross-sectional national survey reported in 2019 that the prevalence of dyslipidemia was $58.6\%$, among which $6.2\%$ were aware, $18.9\%$ were treated, and $21.5\%$ were controlled [31]. Another study noted that only a small proportion of the total hypertensive or diabetic population had adequately controlled blood pressure or blood sugar due to a large diagnosis gap, non-treatment of previously diagnosed populations, and inadequate control of the treated population [32]. Traditional and ecological aspects may play a role in the observed patterns of the results. A harsh climate, average atmospheric temperature, atmospheric pressure, precipitation, and mineralization of rivers can have an influence [33]. Traditionally, Mongolians have a unique nomadic lifestyle, and its population prefers a diet of meat, milk, and its derivatives [34,35,36]. Most rural families are physically active, involved in caring for livestock, transportation by horses and camels, milking, shearing, and combing. This unique lifestyle might be associated with a low BMI in Mongolians [30]. However, nearly $67\%$ of the population is living in the capital city, seeking education and a professional job over traditional nomadic life, and migration from rural areas to Ulaanbaatar has been increasing in recent years [37]. For herders, there is a lack of access to education and healthcare services because of nomadic communities living and moving in remote areas [38]. Along with this rapid modernization of the whole country, diet and nutrition have shifted toward a diet with high fat, high energy, and low dietary fibers [39]. Dietary habits are associated with lifestyle-related diseases and early aging in Mongolia [40]. According to the NCD risk factor surveys, urban residents had higher risks, such as smoking, physical inactivity, obesity, and high cholesterol levels [29]. There was a significant association between MS and poor lifestyle habits and some SES factors such as education and marital status [11,13]. Additionally, Mongolian urban adults have a higher prevalence of MS [9,29]. Establishing health promotion policies based on the needs and convenience of urban residents is an urgent matter. In addition, results of the logistic regression showed that the middle (40–59) and older adult (60 and over) groups had one to three times higher prevalence of MS, and married female participants had a higher prevalence of MS according to the JIS definition, which was suggested to be superior in defining MS in Mongolians than the other two definitions based on our main results. The major role of age in predicting MS was consistent with other previous studies [41]. The present study reported that married women had a higher prevalence of MS than those who were never married, which was consistent with the results of some studies [42]. In our study, unmarried participants were younger ($93.1\%$ of unmarried male young adults (<40) and $83.9\%$ of unmarried female young adults (<40)). Nevertheless, due to their traditional social roles, Mongolian married women raise their children, do the housework, and cook the food. This may be related to the higher prevalence of MS components, such as physical inactivity, overweight, and obesity, in Mongolian women when compared to men [29]. Further research on how marital status, especially among women, is associated with MS is needed to clarify these findings. Several limitations of this study should be noted. First, our cross-sectional study did not allow us to derive conclusions regarding causal mechanisms. Second, we did not evaluate visceral fat using computed tomography (CT) and magnetic resonance imaging (MRI) to measure central obesity. Third, our data were extracted only from the Mongolian urban population using multistage cluster sampling which included some sampling errors. However, our sampling was carried out following the WHO STEPS Surveillance Manual, and selection bias was smaller than non-random sampling. 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--- title: 'High Glucose Promotes Inflammation and Weakens Placental Defenses against E. coli and S. agalactiae Infection: Protective Role of Insulin and Metformin' authors: - Rodrigo Jiménez-Escutia - Donovan Vargas-Alcantar - Pilar Flores-Espinosa - Addy Cecilia Helguera-Repetto - Oscar Villavicencio-Carrisoza - Ismael Mancilla-Herrera - Claudine Irles - Yessica Dorin Torres-Ramos - María Yolotzin Valdespino-Vazquez - Pilar Velázquez-Sánchez - Rodrigo Zamora-Escudero - Marcela Islas-López - Caridad Carranco-Salinas - Lorenza Díaz - Verónica Zaga-Clavellina - Andrea Olmos-Ortiz journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048930 doi: 10.3390/ijms24065243 license: CC BY 4.0 --- # High Glucose Promotes Inflammation and Weakens Placental Defenses against E. coli and S. agalactiae Infection: Protective Role of Insulin and Metformin ## Abstract Placentas from gestational diabetes mellitus (GDM) patients undergo significant metabolic and immunologic adaptations due to hyperglycemia, which results in an exacerbated synthesis of proinflammatory cytokines and an increased risk for infections. Insulin or metformin are clinically indicated for the treatment of GDM; however, there is limited information about the immunomodulatory activity of these drugs in the human placenta, especially in the context of maternal infections. Our objective was to study the role of insulin and metformin in the placental inflammatory response and innate defense against common etiopathological agents of pregnancy bacterial infections, such as E. coli and S. agalactiae, in a hyperglycemic environment. Term placental explants were cultivated with glucose (10 and 50 mM), insulin (50–500 nM) or metformin (125–500 µM) for 48 h, and then they were challenged with live bacteria (1 × 105 CFU/mL). We evaluated the inflammatory cytokine secretion, beta defensins production, bacterial count and bacterial tissue invasiveness after 4–8 h of infection. Our results showed that a GDM-associated hyperglycemic environment induced an inflammatory response and a decreased beta defensins synthesis unable to restrain bacterial infection. Notably, both insulin and metformin exerted anti-inflammatory effects under hyperglycemic infectious and non-infectious scenarios. Moreover, both drugs fortified placental barrier defenses, resulting in reduced E. coli counts, as well as decreased S. agalactiae and E. coli invasiveness of placental villous trees. Remarkably, the double challenge of high glucose and infection provoked a pathogen-specific attenuated placental inflammatory response in the hyperglycemic condition, mainly denoted by reduced TNF-α and IL-6 secretion after S. agalactiae infection and by IL-1β after E. coli infection. Altogether, these results suggest that metabolically uncontrolled GDM mothers develop diverse immune placental alterations, which may help to explain their increased vulnerability to bacterial pathogens. ## 1. Introduction Gestational diabetes mellitus (GDM) is a transitory condition of pregnancy characterized by hyperglycemia and low-grade sterile chronic metabolic inflammation. This metainflammatory condition prevails in sera, adipose tissue and, importantly, the placenta [1,2]. Placentas from complicated GDM mothers undergo significant metabolic and immunologic adaptations due to hyperglycemia and metainflammation [2], such as the altered infiltration of immune cells into villous trees [3,4] and increased gene expression for stress- and inflammatory –related genes [5]. Additionally, in vitro studies in a first trimester trophoblast cell line have demonstrated that hyperglycemia induces the secretion of diverse inflammatory cytokines, including interleukin (IL) -1β, IL-6 and IL-8 [6,7]. Despite this high-glucose-dependent proinflammatory response, GDM has been associated with an increased risk of infections, such as vulvovaginal candidiasis, chorioamnionitis and vaginal infections, which are known to contribute to adverse pregnancy outcomes [8]. Likewise, more oral anaerobic bacteria, tuberculosis bacilli, black-pigmented bacteria and actinomycetes have been detected in pregnant women with GDM as compared with nondiabetic pregnant women [9]. GDM patients have higher vulvovaginal infection rates and vaginal dysbiosis rates in comparison to euglycemic women [8,10]. Epidemiologic evidence supports that, in diabetic and GDM pregnant women, the most frequent bacterium isolated from urogenital specimens is S. agalactiae [11,12], with a colonization-adjusted rate in pregnant women of 21–$25\%$ in North America and $18\%$ worldwide [13]. Additionally, Escherichia coli is the leading cause of chorioamnionitis and urinary tract infections in pregnant women [14,15,16,17,18]. After the diagnosis of hyperglycemia, a pregnant woman is referred to diet counseling, and if the blood glucose levels still exceed the target, metformin and/or insulin are usually indicated [19,20,21,22]. Several studies have widely studied both hypoglycemic drugs in terms of glucose metabolic control, insulin resistance and the prevention of fetal and maternal adverse outcomes [23,24,25]. Additionally, the anti-inflammatory effect of insulin and metformin has been known for several years, as reported among in vivo, in vitro and clinical experiments; being the main underlying mechanism the reduction in inflammatory mediators such as tumor necrosis factor alpha (TNF-α), IL-1β, IL-6 and IL-8, as well as nuclear factor kappa B (NFκB) activation [26,27,28,29,30,31,32,33,34,35,36]. Regarding the role of insulin and metformin in the defense against infections, a murine pre-diabetic model revealed that urinary tract infections are more frequent when the insulin receptor is deleted [37]. Additionally, insulin treatment promoted the synthesis of the antimicrobial peptide human beta defensin (HBD) −1 in kidney and colon cell lines [38]. On the other hand, metformin has been described as an antimicrobial agent which reduces bacterial, parasitic and viral infections [39], and recently, its efficacy was demonstrated against uropathogenic E. coli [40]. Nevertheless, there is scarce information about the immunomodulatory activities of insulin and metformin in GDM, especially in the context of maternal infections. In the present work, we showed that severe hyperglycemia promoted the placental synthesis of inflammatory cytokines, reduced the synthesis of beta defensins and compromised the placental innate defense against E. coli and S. agalactiae infection. Insulin and metformin treatments proved to not only be effective anti-inflammatories, but they also helped to confront E. coli and S. agalactiae infection by diminishing bacterial growth and their invasiveness. Finally, the double challenge of high glucose and infection led to a state of immunological unresponsiveness, which may help to explain the increased maternal vulnerability to bacterial pathogens during hyperglycemic pregnancies. ## 2. Results A total of 35 placentas were processed and analyzed. Clinical data from mothers and newborns are presented in Table 1. The mothers were normotensive, with a pre-gestational BMI < 30 kg/m2 and a maternal weight gain < 20 kg, and they had term newborns with a normal weight, length and head circumference. The newborn sex proportion was evenly balanced. ## 2.1. A Hyperglycemic Model in Human Term Placenta We evaluated the production of inflammatory cytokines by placental explants in response to a glucose curve (10, 25, 35 and 50 mM). The glucose dose–response curve was chosen according to previous experiments developed in trophoblasts and other cells to mimic normo- and hyperglycemic environments associated with GDM or diabetes [6,43,44,45,46]. Treatment with 50 mM glucose during 48 h induced a pro-inflammatory phenotype characterized by a significant increased secretion of TNF-α, IL-1β and IL-6 in comparison to glucose 10 mM (Figure 1A–C). Regarding IL-1β, this cytokine was significantly stimulated starting from 35 mM glucose treatment. Notably, the achieved glucose-dependent increase in inflammatory cytokines was not as high as that observed with lipopolysaccharide (LPS), which represents, in this model, an acute stimulus for inflammation (Figure 1A–C). Considering all the above, we chose 10 mM glucose as a non-inflammatory glucose control, whereas 50 mM glucose was chosen as a hyperglycemic metainflammatory-like condition. Moreover, additional controls for osmolarity were performed to check if the pro-inflammatory stimulus in our placental explant model is exclusively associated with the direct effects of a high glucose concentration, instead of a high osmolar pressure (Supplementary Table S1 and Supplementary Figure S1). The placenta, as a critical organ which needs to efficiently mobilize glucose between the fetus and the mother, functions as a large reservoir of glycogen [47]. Therefore, we evaluated the placental glycogen deposition by Periodic acid–Schiff stain (PAS) as magenta signals. Microphotographs showed that the explants treated with control glucose presented predominant glycogen deposits in perivascular areas and below the basal membrane of syncytiotrophoblasts (Figure 1D, depicted with arrows). In comparison, the explants treated with 50 mM glucose had a broad glycogen distribution throughout the villous mesenchyme, indicating more abundant glycogen deposits (Figure 1E). Additionally, we performed an XTT assay with placental explants incubated with control and high glucose concentrations for 96 h. None of the glucose concentrations tested compromised placental viability (Figure 1F), neither insulin nor metformin treatment (Supplementary Figure S2). Therefore, we confirmed that exposition to 50 mM glucose is a good strategy for emulating a model of hyperglycemia in human term placental explants. ## 2.2. Insulin and Metformin Diminish Placental Pro-Inflammatory Cytokines Secretion under a Hyperglycemic Condition Considering that insulin and metformin are frequently used as pharmacotherapy in pregnant women with GDM, we wanted to evaluate the effect of both hypoglycemics on the synthesis of pro-inflammatory cytokines in human placenta. Figure 2A–C show that 50 mM glucose significantly induced TNF-α, IL-1β and IL-6 placental secretion. Interestingly, 500 nM insulin added to the hyperglycemic culture media significantly diminished TNF-α secretion (Figure 2A), while this insulin treatment and all the concentrations of metformin tested significantly reduced IL-1β placental secretion under the hyperglycemic culture media (Figure 2B). Metformin was not able to reduce TNF-α secretion. Finally, both treatments were effective in significantly reducing placental IL-6 secretion (Figure 2C). Therefore, insulin and metformin are useful for reducing the hyperglycemic-dependent inflammatory cytokines in the placenta. In addition to cytokines, several adipokines are also dysregulated in the serum of GDM women as part of the metainflammatory state [48,49]. Therefore, we also evaluated if glucose and hypoglycemics could modulate placental adipokine secretion (Supplementary Table S2). In this model, severe hyperglycemia did not modify the secretion of the main inflammatory (chemerin and leptin) and anti-inflammatory (adiponectin) placental adipokines. Nevertheless, visfatin, an important adipokine with microbicide activity [50,51], was significantly downregulated by hyperglycemia. Intriguingly, insulin treatment further diminished visfatin secretion in comparison with 50 mM glucose, while metformin had no effect upon this adipokine. Hence, in this model, hyperglycemia and hypoglycemics do not modify placental adipokine secretion, except for visfatin, which was downregulated. ## 2.3. Hyperglycemia Diminishes Placental Beta Defensins Synthesis. Metformin and Insulin Do Not Restore HBDs We wanted to explore in this model if hyperglycemia and hypoglycemic treatments could modulate the placental synthesis of human beta defensins, as potent microbicides involved in the control of an infection. As shown in Figure 3A–D, severe hyperglycemia significantly diminished the placental production of HBD 1, 2, 3 and 4 in comparison with the explants treated with 10 mM glucose. These observations suggest a weaker innate defense capacity of the hyperglycemic placenta due to the downregulation of at least these four defensins as well as visfatin, although other antimicrobial peptides not evaluated herein could also be affected. Notably, the co-treatment with insulin or metformin did not reverse the downregulating effect of high glucose upon the production of beta defensins. Therefore, these hypoglycemic drugs did not improve placental innate defense through the direct regulation of HBDs synthesis. ## 2.4. Innate Defense against Bacterial Infections in Pre-Exposed Hyperglycemic Placenta After we corroborated that severe hyperglycemia results in the higher placental expression of inflammatory cytokines and the lower synthesis of antimicrobial HBDs and visfatin, we hypothesized that these high-glucose-dependent changes could derive in a weakened host immune defense against an infection. To test this, we challenged cotyledon explants with a live Gram-positive (S. agalactiae) and Gram-negative (E. coli) infection. Additionally, we explored if hypoglycemics could modulate the immune response against these pathogens, considering that they exerted an anti-inflammatory activity. First, placental explants were preincubated for 48 h with glucose and hypoglycemic treatments. Then, they were challenged with S. agalactiae infection for 4 and 8 h. Afterward, we evaluated bacterial internalization in placental villi by Gram staining (visualized as purple/blue bodies/structures), bacterial growth, and cytokines release in culture media. As shown in Figure 4A, in explants pretreated with 10 mM glucose, bacteria were virtually absent after 8 h of S. agalactiae infection. In contrast, pretreatment with 50 mM glucose resulted in a higher proportion of bacteria mostly contained at the syncytial barrier. On the other hand, under a hyperglycemic state, insulin pretreatment qualitatively diminished bacterial invasiveness into placental villi compared with explants pretreated with 50 mM glucose alone, whereas metformin pretreatment did not improve the defense capacity of placenta against S. agalactiae infection. Next, we quantitatively analyzed bacterial growth in the culture media. As seen in Figure 4B, pretreatment with 50 mM glucose led to a higher number of S. agalactiae at 4 and 8 h post infection in comparison with the explants pretreated with 10 mM glucose. However, neither insulin nor metformin pretreatment modified bacterial counts in comparison with the explants exposed to 50 mM glucose. Altogether, these observations may indicate that insulin does not exert a microbicide effect in the placenta (as observed in the production of antimicrobial beta defensins in Figure 3 and bacterial growth in Figure 4B) but may improve the placental barrier defense against S. agalactiae infection (Figure 4A). We then analyzed if exposure to a high glucose concentration in the presence or absence of hypoglycemic treatments modified cytokines secretion in response to S. agalactiae infection. As expected, the placental infection challenge significantly induced TNF-α, IL-1β and IL-6 secretion in the explants pretreated with 10 mM glucose (Figure 4C–E). As noted, the infection-dependent inflammatory stimulus was significantly greater than that observed by hyperglycemia alone (50 mM glucose without infection). Unexpectedly for us, the explants pretreated with 50 mM glucose and then infected with S. agalactiae exhibited a significantly decreased secretion of TNF-α and IL-6 in comparison with the infected explants incubated with 10 mM glucose (Figure 4C,D). Notably, pretreatment with insulin or metformin did not modify the decreased secretion of TNF-α and IL-6 observed under hyperglycemia. These hypoglycemic treatments results were not significantly different from the those obtained with infected- 50 mM glucose-treated explants. Notably, the infection with S. agalactiae significantly stimulated IL-1β secretion, irrespective of the glucose concentration and the presence of insulin or metformin (Figure 4E). Therefore, and contrary to what we expected, a double inflammatory stimulus by severe hyperglycemia and S. agalactiae infection resulted in a weaker inflammatory profile, despite each one of these insults alone led to increased TNF-α and IL-6 production in comparison to normoglycemia. After the experiments with S. agalactiae infection in our model, we tested E. coli, another clinically relevant bacteria during pregnancy. Gram-negative bacteria stains as red/pink structures, and we observed that 50 mM glucose induced a broad bacterial invasion of placental villi in comparison with the explants incubated with 10 mM glucose (Figure 5A). In fact, the syncytial layer was an effective barrier against E. coli infection in the 10 mM glucose-treated explants, as most of the bacteria were contained at this level. Contrastingly, this barrier was weakened by the presence of 50 mM glucose. As a result, bacteria readily crossed the syncytiotrophoblast layer and penetrated the villous mesenchyme. Interestingly, under this condition, bacteria seemed to also invade fetal capillaries. On the other hand, insulin and metformin acted as good fortifiers of the innate defense against E. coli, because they helped to limit bacterial invasiveness into the mesenchyme and capillaries. Next, we quantified the bacterial growth in the culture media of these E. coli-infected explants. As shown in Figure 5B, the culture media of the placental explants pre-incubated with 50 mM glucose had significantly higher E. coli counts in comparison with that from the explants grown in 10 mM glucose at 4 h of infection; this difference was lost at 8 h post-infection. Interestingly, insulin and metformin pretreatments in hyperglycemic media significantly diminished E. coli growth compared with 50 mM glucose. This behavior agrees with the observed deeper invasiveness of E. coli in the presence of 50 mM glucose, as well as the lesser invasion of the villous trees promoted by insulin and metformin (Figure 5A). Finally, we evaluated the production of pro-inflammatory cytokines in this double challenged placental model. As expected, infection significantly induced IL-1β, TNF-α and IL-6 placental secretion (Figure 5C–E). Again (as observed with S. agalactiae experiments), the double hit of hyperglycemia plus infection modified the placental proinflammatory profile. In this case, the explants pretreated with 50 mM glucose and then infected with E. coli exhibited a significantly decreased secretion of IL-1β, whereas IL-6 and TNF-α were not modified. Pretreatment with insulin or metformin did not modify the decreased secretion of IL-1β observed under hyperglycemia, and resulted in a significantly lesser secretion in comparison with the infected placental explants exposed to 10 mM glucose. Additionally, these hypoglycemic treatments were not significantly different from the infected explants incubated under 50 mM glucose (Figure 5C). All these results point to hyperglycemia as a serious and deleterious scenario which hampers the production of pro-inflammatory cytokines in response to an infection, which may vulnerate maternal host defense. ## 3. Discussion In this manuscript, we established experimental in vitro conditions to preserve both the hyperglycemic and the inflammatory states associated with GDM and hyperglycemia in pregnancy. This model, consisting of cotyledon explants exposed to 50 mM glucose, resulted in a significantly induced secretion of TNF-α, IL-1β and IL-6, and more abundant glycogen deposits, without compromising cellular viability. All these changes concur with the well-described morpho-functional alterations of GDM placentae [5,47,52,53]. Multiple models of hyperglycemia have been developed in established cell lines or primary cultures of human placenta. As the control, glucose concentrations fluctuating from 5 mM to 11 mM have been used to recreate a non-diabetic non-inflammatory state in human placental cell lines [54,55], while concentrations ranging from 20 mM to 50 mM have been frequently used to reproduce an environment similar to gestational diabetes mellitus [6,56,57]. In this study, only the treatment with 50 mM glucose effectively induced the secretion of TNF-α and IL-6, while IL-1β was significantly upregulated starting from 35 mM glucose. It is worth mentioning that 10 mM glucose is above the cut-off repeated fasting plasma glucose levels for diagnosing GDM (range between 5.1 and 6.9 mM) [58]; however, this concentration is below or in accordance with the standard glucose concentration for the culture of placental explants, usually cultivated in DMEM-HG, DMEM/F12 or RPMI-1640 medium (containing 25 mM and 11.1 mM glucose, respectively) [59]. Notably, even if glucose ~11 mM has been used as an experimental control for hyperglycemic assays in cultured trophoblasts [54,55,60,61,62], herein, we evaluated proinflammatory cytokines secretion in cultured explants under 5 and 10 mM glucose concentrations, and we did not observe a significant difference between both conditions (Supplementary Figure S1). In addition, the concentrations of insulin and metformin in our study were chosen based on previous experiments performed in tissues of the maternal–fetal interface [6,61,63,64]. Hence, and as a limitation of this study, the glucose, insulin and metformin concentrations tested herein are more in line with experimental conditions than they are with clinical settings. Notably, the inflammatory stimulus evoked by hyperglycemia was of a lesser degree than that aroused by LPS, a bacterial endotoxin widely used to promote an acute inflammatory response in both cellular and animal models [65,66,67,68]. Therefore, this experimentally induced inflammation coincides with the low-grade chronic metainflammation observed in diabetic patients, which has been associated with negative short- and long-term adverse effects [69]. Although chronic hyperglycemia in GDM is the main trigger for metainflammation, other factors such as lipotoxicity, endotoxemia or self-nucleic acids may also be involved [70,71], which are probably co-acting in this model. On the other hand, several reports have indicated that hyperglycemia weakens innate defense. For instance, we know that hyperglycemia strengthens diverse bacterial and fungal pathogenic mechanisms [72,73], impairs B cells function [74] and dysregulates the synthesis of proteins related to innate immunity defense such as antimicrobial defensins, inflammatory cytokines and chemokines [2,11,75]. Indeed, immune defense against infection involves complex mechanisms and, undoubtedly, a critical one is the synthesis of antimicrobial peptides [76]. Among them, HBDs are the most studied group of microbicidal peptides in the maternal–fetal interface [77,78,79,80]. In vitro and in vivo experiments have demonstrated that a high glucose concentration in sera or culture media diminished the production of HBDs [81,82,83,84]. Accordingly, our results showed that a high glucose environment also diminished the synthesis of placental defensins. In addition to HBDs, visfatin was also downregulated by hyperglycemia. Experimental evidence points out that this adipokine is an enhancer of the synthesis of diverse antimicrobial peptides, including HBDs, cathelicidin and psoriasin [50,51]. Therefore, visfatin reduction in hyperglycemic placenta could be an additional factor explaining the compromised innate response in this model and in GDM pregnancies. Altogether, our results suggest that hyperglycemia impairs the ability of the placenta to respond to an infectious challenge due, at least in part, to a lower synthesis of HBDs and probably other antimicrobial peptides. Further studies are needed for additional mechanistic insights. Is important to note that, in this model, we could not evaluate the role of immune cells and decidua as highly important producers of HBDs and other antimicrobial peptides at the feto-maternal interface [85,86,87], and therefore, these results do not reproduce the entire response occurring at the maternal–placental–fetal interface in vivo. When we analyzed if insulin and metformin modulated the synthesis of inflammatory markers in our placental model, we found that both hypoglycemics helped to reduce the hyperglycemic-induced secretion of TNF-α, IL-1β and IL-6. Accordingly, diminished levels of total NF-κB or its phosphorylation have been described after insulin or metformin treatment in diverse in vitro and in vivo models [88,89,90,91,92]. This action prevents the NF-κB complex from being internalized in the nucleus, which in turn blocks the transcription of genes associated with inflammation. More studies should be carried out in the future to elucidate whether insulin or metformin inhibit the activation of the main promoters of inflammation, especially NF-κB, in the hyperglycemic placenta. Regarding the regulation of HBDs by insulin and metformin treatment, unexpected results were obtained. We had hypothesized that treating placental explants with both hypoglycemic agents would help strengthen the placental innate defense by increasing the synthesis of HBDs. As previously described, several reports supported that insulin and metformin play an inductive role in the expression of antimicrobial peptides. These studies were undertaken in animal models (diabetic rats and worms) or by in vitro approaches (kidney cell lines, pneumocytes) [38,40,81,93,94]. However, all of them are far from representing the morphological and functional characteristics of the human GDM-placenta. Herein, we demonstrated that neither treatment with insulin nor metformin helped to revert the hyperglycemic-dependent reduction in HBDs production by the human placenta. Interestingly, in a previous diabetic animal model [81], insulin was effective in restoring BD1 expression in the rat kidney; however, the low expression of BD1 due to diabetes could not be restored after insulin treatment in either the lung or brain of the same rats. The latter supports the existence of tissue-specific inductive and repressive mechanisms controlling the beta defensins synthesis in response to insulin. In accordance with the lower HBDs production, severe hyperglycemia has been pointed out as a strong associative factor related with infections [75,95,96]. During pregnancy, an infectious process into the uterine cavity represents a major challenging condition that endangers the immune privilege of the maternal–fetal unit, increasing the risk of the premature rupture of membranes and preterm birth [69]. In particular, GDM women with urinary or cervicovaginal infections are in an even more vulnerable position because they have to confront two immune insults: one due to hyperglycemia and the second due to infection. Although this combined scenario is very common in the clinical practice [8,10,14], it has been scarcely studied in experimental biomedical approaches. Therefore, we decided to study the response to pathogen infection in hyperglycemic cotyledon explants by evaluating bacterial growth and invasiveness, as well as the inflammatory cytokines synthesis in response to infection. To our knowledge, this is the first experimental approach addressing the combined scenario of hyperglycemia and infections in the human placenta. As expected, the hyperglycemic condition significantly decreased the innate defense capacity of the placenta against both E. coli and S. agalactiae infection. Severe hyperglycemia favored a higher count and deeper invasiveness of both Gram-positive and Gram-negative bacteria. Interestingly, the explants pretreated with glucose 10 mM were barely positive for S. agalactiae, even after 8 h post-infection, whereas hyperglycemia led to abundant bacteria contained at the syncytial barrier. In our model, insulin was effective in limiting S. agalactiae adhesion to the syncytial layer, but it could not diminish the extracellular bacterial growth. The latter may imply that insulin does not potentiate the synthesis of beta defensins by the human placenta, although the regulation of other antimicrobials is not discarded. Additionally, we believe that other mechanisms of innate defense could be activated by insulin. For instance, insulin treatment helps strengthen the epithelial barrier function by increasing transepithelial electrical resistance [97], while diminishing the permeability of monolayers through the induction of tight junctions [98,99]. Probably, some of these strategies for cellular defense, alone or concomitant, could be occurring at the placenta, which deserves to be further explored. In relation to E. coli infection, the precondition with glucose 10 mM favored placenta to contain infection mainly at the syncytiotrophoblast barrier, whereas exposure to hyperglycemia allowed bacteria to cross the syncytial barrier and to profoundly invade the mesenchyme. Interestingly, it appears that E. coli had a tropism for capillaries. Notably, insulin and metformin readily decreased E. coli counts and showed a better fitted defense barrier to a similar extent as explants pre-exposed to glucose 10 mM. These results agree with the experimental evidence indicating that metformin treatment increases the host resistance to E. coli infection, inhibits its microbial adherence, diminishes its response to chemoattracts and compromises its flagellar motility [40,100,101]. Interesting results were obtained in relation to cytokine secretion in explants incubated with the double inflammatory scenario. First, and as expected, E. coli and S. agalactiae infection induced the placental secretion of TNF-α, IL-1β and IL-6. However, the challenge of hyperglycemia plus infection diminished the placental secretion of proinflammatory cytokines in comparison with infected but normoglycemic explants. This behavior resembles an LPS tolerance state. This phenomenon was described in animal and culture models after a low dose of LPS exposition, which then alters the subsequent response to LPS or other inflammatory stimulus by inducing an immunosuppressive state to protect the host against a cytokine-induced damage [102,103]. This immunosuppression is primarily linked with chromatin alterations and diverse epigenetic changes which suppress the transcription of NF-κB target genes and end in the transient silencing of pro-inflammatory genes [104,105,106]. In this manuscript, we describe, for the first time, the same tolerant state in a hyperglycemic placental model, which agrees with an initial pro-inflammatory hit due to hyperglycemia followed by a second hit represented by the endotoxemic challenge (LPS in the case of E. coli infection or lipoteichoic acid (LTA) for S. agalactiae infection), which results in an immunosuppressive response. Furthermore, the attenuated inflammatory response in hyperglycemic placenta was pathogen-specific, with the suppression of TNF-α and IL-6 or IL-1β after S. agalactiae and E. coli infection, respectively. Thus, the pathogen-specific activation of distinct inflammatory pathways is probably involved in the hyperglycemic placenta. The production and release of IL-1β is regulated by a two-signal NLRP3 inflammasome activation; the first signal is through the stimulation of TLRs and NF-κB activation, leading to the up-regulation of IL-1β protein levels. The second signal is through damage-associated molecular patterns (DAMPs) recognition, the formation of the NLRP3 complex, the activation of caspase 1, which is necessary for the cleavage of pro-IL-1β, and the release of the bioactive mature molecule (reviewed by [107]). The inflammasome can also be activated in a caspase-11-dependent noncanonical pathway, leading to IL-1β release. This noncanonical pathway has been described in Gram-negative bacteria such as E. coli infection, but not in Gram-positive pathogens, such as S. agalactiae. These caspase-1-dependent and -independent pathways induce pyroptosis, a programmed cell death characterized by an increased inflammation occurring after infection but also in chronic diseases such as diabetes [107,108]. Thus, the decreased levels of IL-1β after E. coli infection but not as a result of S. agalactiae’s presence in the hyperglycemic placenta could be explained by the inhibition of the inflammasome complex formation or caspase 1 activation. A second possibility could involve the different activation of the pyroptosis pathway by both pathogens. This lower pro-inflammatory cytokine production may impact placental immunity in different ways. On one side, this could help to avoid an exacerbated inflammatory response at the feto-maternal interface, which may avoid fetal neurodevelopmental damage or preterm birth [109,110,111]. However, on the other hand, the inability to produce enough cytokines to fight an infectious challenge increases the host vulnerability to pathogen virulence and invasiveness, which matches with the observed prolonged maternal infection period and the higher frequencies of cervicovaginal, chorioamnionitis or puerperal infections in GDM patients [8,112]. Altogether, these results point out that hyperglycemia associated with GDM induced profound immune-metabolic adaptations in the placenta, resulting in a marked inflammatory profile incapable of restraining a bacterial infection, particularly against E. coli and S. agalactiae, representing the most frequent Gram-positive and Gram-negative etiologic agents of urinary and cervicovaginal infections. Insulin and metformin exert anti-inflammatory activity and placental protection against E. coli and S. agalactiae infection. As a final comment, we recognize that this in vitro model has technical limitations because it only reproduces some placental alterations observed in the GDM placentae (i.e., a proinflammatory state and greater glycogen deposition). However, this model cannot reproduce the distinct placental vascular dysfunctions or complex cellular and tissue interactions that occur in GDM patients or in the in vivo models. Nevertheless, this approach may help in understanding several morphophysiological adaptations that take place in the placenta of GDM women, especially those related to the innate defense response against an infectious challenge including parasites and fungi. As strengths, this model reproduces well-known clinical conditions reported in placentas from GDM patients and also allows for the mimicking of placental infectious processes in a clear timeline. ## 4.1. Ethics Statement This protocol was approved by the Biosafety, Ethical and Research Committees of the Instituto Nacional de Perinatología Isidro Espinosa de los Reyes (INPer, code number 2018-1-152), Hospital Ángeles México, Hospital Ángeles Lomas (HAL $\frac{366}{2020}$) and Hospital Gineco Obstetricia No. 4 IMSS (R-2020-785-043), all of them being in Mexico City. All methodological approaches were conducted according to the Belmont Report. Written informed consent, according to the Declaration of Helsinki, was obtained voluntarily from each mother before the caesarean section. ## 4.2. Sample Collection Complete placentas were collected from normoevolutive, uncomplicated, term (37.2–40 weeks) pregnant women who gave birth to single newborns, and who attended their cesarean section in the hospitals listed above. According to medical records, the clinical indications for cesarean sections were breech presentation, cephalopelvic disproportion, antecedent of uterine surgery, antecedent of myomectomy and personal maternal decision. The exclusion criteria for this study comprised patients with endocrine, metabolic, infectious and/or other systemic diseases such as hypertension, diabetes mellitus and thyroid, liver or chronic renal diseases. Additionally, patients allergic to penicillin or streptomycin, as well as patients who suffered from cervico-vaginal infections during the third trimester of pregnancy, were excluded. ## 4.3. Reagents The DMEM culture media and fetal bovine serum (FBS) were from Biowest (Riverside, MO, USA). Monohydrated dextrose was acquired from JT Baker–Fisher Scientific (Mexico City, Mexico) and was diluted in deionized water to a 1 M solution, filtered and preserved at 4 °C. Human insulin was reconstituted in 10 mM HCl to stock at a 1 mM solution. Metformin hydrochloride was reconstituted in phosphate buffer saline (PBS) to a 1 M stock solution. Both hypoglycemics were stored in single-use working aliquots at −20 °C. The LPS from E. coli 055:B5 was diluted in PBS to a stock at 100 μg/mL. Insulin (I2643), metformin (PHR1084) and LPS (L4005) were purchased from Sigma-Aldrich (St. Louis, MO, USA). ## 4.4. Cotyledon Explant Culture and Experimental Procedures Placental cotyledons were exhaustively washed with sterile $0.9\%$ NaCl, and visible blood clots, blood vessels, decidua and chorionic and basal plates were removed. Three small placental explants (around 3 to 5 mm3) were placed into 24-well culture dishes and were incubated in DMEM-supplemented culture media (plus $10\%$ FBS + $1\%$ sodium pyruvate + $1\%$ penicillin/streptomycin) in a humidified incubator at 37 °C and $5\%$ CO2–$95\%$ air. DMEM low glucose (5 mM) was adjusted with a D-glucose solution (dextrose) to generate culture media with glucose 10, 25, 35 or 50 mM. The glucose dose–response curve was chosen according to previous experiments developed in trophoblasts and other cells to mimic normo- and hyperglycemic environments associated with GDM or diabetes [6,43,44,45,46]. After the set of the dose–response experiments, we chose glucose 10 mM as the control glucose non-inflammatory media, whereas glucose 50 mM was chosen as a severe hyperglycemic inflammatory condition. The experimental treatment with insulin or metformin was maintained for 48 h, refreshing them at 24 h. We worked with insulin (50, 100 and 500 nM) and metformin (125, 250 and 500 μM) according to previous studies developed in tissues of the materno–placental–fetal interface [6,61,63,64]. After 48 h of incubation, culture media were frozen until cytokine quantification. ## 4.5. Quantification of Cytokines and Adipokines by ELISA After thawing the culture media, we employed an R&D System (Minneapolis, MN, USA) ELISA commercial kit to detect the placental secretion of TNF-α (DY210), visfatin (DY4335), chemerin (DY2324), adiponectin (DY1065) and leptin (DY398). IL-1β and IL-6 were quantified using Peprotech (Minneapolis, MN, USA) ELISA commercial kits (900-K95 and 900-K16, respectively). Assays were performed according to the manufacturer’s instructions. Sigma-Fast OPD was used as the colorimetric substrate (P9187, Sigma-Aldrich. St Louis, MO, USA). Absorbances were read with the microplate spectrophotometer xMark (BIO-RAD. Hercules, CA, USA). At the end of each experiment, the placental explant weight was registered, and the concentration of each analyte was normalized by 1 g of wet tissue. ## 4.6. Tissue Protein Extraction and Human Beta Defensins Quantification by ELISA At the end of the experiment, the explants were placed into cold protein lysis buffer (Tris-HCl 20 mM, EDTA 1 mM, Nonidet P-40 $0.025\%$, sodium deoxycholate $1\%$, SDS $0.1\%$, NaCl 150 mM, Na3VO4 2 mM, NaF 50 mM and protease inhibitor cocktail Sigma-Aldrich P8340, 1:1000) (Sigma-Aldrich. St Louis, MO, USA) and stored at −40 °C until the quantification of beta defensins. The explants were mechanically disrupted using a polytron homogenizer (OMNI International. Kennesaw, GA, USA). Then, the sample lysates were centrifuged at 3500 rpm for 10 min at 4 °C, and the supernatant was collected. The protein content of the tissue lysates was quantified by the Bradford method [113]. The samples were adjusted to load 350 μg of the total placental protein per well for the ELISA procedure. The amounts of HBD-1, HBD-2, HBD-3 and HBD-4 in the tissue lysates were quantified using Peprotech (Minneapolis, MN, USA) ELISA commercial kits (900-K202, 900-K172, 900-K210 and 900-K435, respectively), with an 8 pg/mL detection limit for HBD-1, HBD-2 and HBD4 and a 64 pg/mL detection limit for HBD-3. The assays were performed according to the manufacturer’s instructions. Intratissue beta defensins concentrations were normalized per mg of protein. ## 4.7. Placental Infection with Escherichia coli or Streptococcus agalactiae The E. coli strain used herein was isolated from the blood of a neonate diagnosed with early onset sepsis whose mother was diagnosed with premature rupture of membranes and chorioamnionitis at the INPer. This strain was previously utilized in our lab [79], was identified by means of Vitek® and was confirmed by its 16S rRNA sequencing. The E. coli genotype was determined by multiplex PCR (Polymerase Chain Reaction). The B2-phylogroup and the detected virulence genes were PAI, papA, fimH, ibeA, fyuA, iutA (aerJ), hlyA and traT; it was therefore a pathogenic strain. On the other hand, Streptococcus agalactiae Lehmann and Neumann was obtained from the American Type Culture Collection (ATCC 27956. Rockville, MD, USA); as a control, their beta hemolytic activity was corroborated in every experiment. Placental explants were infected after 48 h of incubation with the experimental treatments. The night before infection, E. coli or S. agalactiae were grown in brain heart infusion broth at 37 °C. The bacterial count was calculated by spectrophotometry (600 nm wavelength), and the infection rate (1 × 105 CFU/mL) was corroborated in every experiment by counting the visible E. coli colonies in Miller’s LB agar plates or the visible S. agalactiae colonies in blood agar plates plus $5\%$ ram’s blood. Both infections were maintained in DMEM-supplemented media without antibiotics (DMEM antibiotic-free media adjusted to glucose 10 mM + $0.2\%$ lactalbumin hydrolysate + 1 mM sodium pyruvate) for 4 and 8 h. A bacteria count of 1 × 105 CFU/ mL in a urinalysis test represents a pathognomonic sign of urinary tract infection [114,115]; therefore, it was chosen for the infection challenge experiments. An aliquot of culture media was used for the CFU count. Briefly, serial 10-fold dilutions were seeded in triplicate into Miller’s LB or blood agar plates. After overnight incubation at 37 °C, the plates with visible and countable CFU were chosen, and the corresponding dilution was registered. The final number of colonies in each treatment was estimated by the following Equation [1]. [ 1]CFU/mL=(No. of colonies)(Dilution factor)Volume of culture well (1000 μL) Non-infected explants were also maintained with DMEM-supplemented media without antibiotics as controls. ## 4.8. Staining Techniques At the end of the experimental procedure, placental explants were fixed in $10\%$ formalin for at least 24 h. The tissues were embedded in paraffin blocks and cut into 10 µm slices. The slides were deparaffinized and cleared in Histo-Clear (National Diagnostic, Atlanta, GA, USA) and rehydrated through graded concentrations of ethanol in water for staining. The sections were stained for modified Gram (Remel, Lenexa, KS, USA) or for Periodic acid–Schiff stain (PAS). Gram dye allowed for the visualization of Gram-negative bacteria in the tissues as pink- to red-colored structures, whereas Gram-positive bacteria are shown as purple- to blue-colored bodies; the tissue was counterstained with yellow picric acid. PAS staining is frequently used to detect different sugars in tissues (such as glycogen, glycoproteins, glycolipids or mucins), and it appears as magenta regions; the nuclei were positive for hematoxylin. ## 4.9. XTT Cell Viability Assay Cell viability was quantified using the Cell Proliferation Kit II XTT (Roche Diagnostics, Basel, Switzerland), according to the manufacturer’s instructions. Briefly, placental explants were incubated for 96 h with culture media adjusted with glucose 10 or 50 mM. On the day of the experiment, the XTT Reagent/Electron Coupling Reagent mixture was added, and the explants were incubated for 2 h at 37 °C in a humidified incubator $5\%$ CO2–$95\%$ air. The absorbance was read at 450 nm using the microplate spectrophotometer xMark (BIO-RAD. Hercules, CA, USA). Absorbance was corrected to wells with XTT substrate and no tissue. ## 4.10. Statistical Analysis Depending on the normality data distribution (Shapiro–Wilk test), statistical comparisons were made by parametric One-Way ANOVA followed by Tukey’s post hoc test for multiple comparisons or by a non-parametric Kruskal–Wallis test followed by Dunn’s post hoc test for multiple comparisons. Data are presented as the mean ± standard deviation for normal data or as the median and interquartile range for non-normal data, as indicated in the figure legends. Statistical analyses were performed with GraphPad Prism software version 9.5.0 (San Diego, CA, USA). $p \leq 0.05$ denotes statistically significant differences. ## 5. Conclusions Our results demonstrated a useful model for experimentally emulating a hyperglycemic milieu in the term human placenta. In this model, we showed that severe hyperglycemia promotes the placental synthesis of inflammatory cytokines, while it reduces the synthesis of beta defensins. Importantly, hyperglycemia compromised the placental innate defense against E. coli and S. agalactiae infection, denoted by higher CFU counts and broader bacterial invasiveness. Insulin and metformin treatments were effective anti-inflammatory treatments in hyperglycemic infectious and non-infectious scenarios. In relation to their properties against bacterial infections, insulin limited the invasiveness of both E. coli and S. agalactiae into placental villous trees and diminished the extracellular E. coli growth. On the other hand, the biguanide metformin improved the placental innate defense by reducing E. coli counts and their bacterial invasiveness into villous trees. 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--- title: 'Living Conditions and the Incidence and Risk of Falls in Community-Dwelling Older Adults: A Multifactorial Study' authors: - Irene Escosura Alegre - Eduardo José Fernández Rodríguez - Celia Sánchez Gómez - Alberto García Martín - María Isabel Rihuete Galve journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048933 doi: 10.3390/ijerph20064921 license: CC BY 4.0 --- # Living Conditions and the Incidence and Risk of Falls in Community-Dwelling Older Adults: A Multifactorial Study ## Abstract Old age represents a social group that is undergoing continuous expansion. The aging population will be prone to chronic diseases and falls, which is a marker of frailty and a public health problem. This study aims to examine the relationship between living conditions and the prevalence of the risk of falls in older adults within the community. As an observational cross-sectional study, intentional sampling was carried out on residents of the metropolitan area over 75 years of age. The socio-demographic data of the subjects and their history of falls were collected. Additionally, the subjects were evaluated on the risk of falling, basic activities of daily living, such as walking and balance, fragility, and their fear of falling. The statistical analyses used were based on the Shapiro–Wilk test for normality, statistics of central tendency with description, mean (M) and dispersion, standard deviation (SD), bivariate contingency tables for studying the relationships between the variables, and the analysis of Pearson’s relational statistics (χ2). The comparisons of means were resolved by parametric or non-parametric routes. We obtained the following results: 1. The socio-demographic profile of our sample consisted of adults over 75 years of age, the majority of whom were overweight or obese women living in an urban area, specifically in an apartment, and receiving care; 2. Older people in the studied community had mild dependency and frailty, and were also at severe risk of falls; 3. The prevalence of falls was higher in women than in men in this study. Through these results, we confirmed the relationship between living conditions and the prevalence of risk of falls in older adults within the community. ## 1. Introduction Different authors [1,2] understand old age as a demographic force, a social group in continuous expansion. This situation will continue in the upcoming years as there is a low birth and mortality rate. The constant increase in life expectancy produces an inversion in the population pyramid, and this shows a clear process of demographic revolution, which has been called the “Silent Revolution” [3]. The increase in the population group aged over 80 years has been identified as the Aging of the Elderly [4]. It has been estimated that by 2030, one in six people in the world will be 60 years of age or older [5]. It is also expected that by 2050, the world population of people in this age group will double (2.1 billion), and the number of people aged 80 and over will triple between 2020 and 2050, reaching 426 million [5]. There is a common phenomenon that is affecting a large proportion of older adults: fall incidence. Falls among the elderly are an important health problem due to their high prevalence and serious consequences: immediate physical consequences (e.g., fractures, bruises, and chest trauma), late physical consequences (e.g., prolonged stay on the floor, which causes dehydration, infections, and immobility), psychological consequences (e.g., post-fall syndrome), and social and economic consequences, which can also entail a high mortality rate [6]. Falls are associated with restricted mobility, decreased ability to perform daily activities, loss of security, fear of falling again, depression, and an increased risk of dependency [7]. Falls have an impact on the entire population, but older adults are the ones at the highest risk of fatal falls [8]. Falls are the leading cause of accidental death in older adults, being responsible for $70\%$ of accidental deaths in people over 75 years of age [9]. Falls among the elderly represent a public health problem worldwide due to their frequency, the associated morbidity and mortality, and the high cost of health resources that they entail [10,11]. Falls incidence is usually the result of a complex interaction between intrinsic factors, extrinsic factors, and circumstantial factors, and there are even other types of factors that have given rise to what some authors have called “unclassifiable falls” [12]. The World Health Organization (WHO) defines a fall as “the consequence of any event that plunges the patient to the ground against his will.” This event is usually sudden, involuntary, and unsuspected, and can be confirmed by the patient or by a witness [13]. Falls make up one of the so-called Great Geriatric Syndromes, or Geriatric Giants, and they themselves represent a marker of frailty [14]. Falls have an impact on the entire population, but older adults are the ones at the highest risk of fatal falls [8]. The main objective of this study was to know, in detail and comprehensively, the relationship between living conditions and the prevalence of risk of falls in older adults within the community of Salamanca. ## 2. Materials and Methods The study design is observational and cross-sectional and is carried out on the population of Salamanca within their Metropolitan district and Public Health System. The selection of the samples was made by the residents of the metropolitan area of the community of Salamanca (refer to Table 1). The sampling technique employed in this study was intentional. Those who met the inclusion criteria and who did not present any of the exclusion criteria were recruited. The calculation of the sample size was conducted through the formula for the estimation of a proportion for infinite populations. The value of n (377.67 people) has been rounded up, identifying 378 people as the minimum size. The number of participants was 425 ($$n = 425$$); however, there were 10 excluded ($$n = 10$$), so the total of participants was: 415 ($$n = 415$$). The variables to be studied are fear of falling, risk of falling, gait and balance, performance of basic activities of daily living (autonomy), and physical performance. To assess the variables, we used the following average instruments: -Fear of Falling Test: instrument to assess the prevalence of fear of falling. [ 15,16,17]. To measure this phenomenon, the question “Are you afraid of falling?” with ordinal response: nothing, a little, moderate, and a lot of fear. Additionally, we used the question “Have you limited your activities for fear of falling?” with yes/no answer. Additionally, the Short FES-I scale was also carried out, with seven items, whose score ranges from 7 to 28 points, the higher the score, the greater the concern related to falls.-Tinetti Test: instrument to assess gait and balance [18]. The maximum score that can be obtained is 28 points. There is a high risk of falling when the score is <19, moderate risk: 19–24 points and low risk: >24 points.-SPPB Test: instrument to assess the frailty of the person. ( It was not used in any calculation due to the inability to differentiate the people studied; all were shown as frail people) [19]. The total score ranges from 0 to 12. Depending on the result obtained in the test, the elderly can be classified as a non-fragile autonomous person if the score is ≥10 points, and a frail person with <10 points.-Barthel Test: instrument for the assessment of the basic activities of daily life [20]. The results are grouped into dependency categories: total (<20), severe (20–35), moderate (40–55), mild (≥60), and independence [100] [20].-Downton Test: instrument to assess the risk of falling [21,22]. The scale has five sections, and each one is scored with 1 if the referred condition is present and 0 if not. The total score is from 0 to 11. If the total result is ≥3, it indicates a high risk of falls. In the information gathering process, first, the work material was prepared: Data collection sheets (source of information) with the assessments made throughout the interview in paper format. The socio-demographic data of the subjects, pathological history, history of falls, and habitual medication were collected. The results obtained were gathered, which were processed through a statistical analysis to later interpret them. For the ethical–legal aspects, confidentiality of data and results, we took into account the rules of special protection that Organic Law $\frac{15}{1999}$, of December 13, Protection of Personal Data (LOPD) establishes for health data, Royal Decree $\frac{1720}{2007}$, of 21 December, which approves the Regulations for the development of Organic Law $\frac{15}{1999}$, and the instructions given by the SAS General Secretariat (21 August 2007) to ensure that the provisions of the LOPD are complied with in its centers sanitary. The personal and affiliation data remained in the custody of the person in charge of the district appointed for this purpose. This study was approved by Clinical Trials with the registry number: 0000281. Each subject included in the study was identified with a unique number (the selection number), to guarantee anonymity. For the statistical methodology, first, the results obtained from the data collection and tests conducted were digitized to prepare a data matrix, and thus, be able to carry out a descriptive analysis of all the variables and data collected in the evaluation. The next step was the classification of each variable in a double aspect (qualitative and quantitative), describing the former by means of frequencies and percentages, with bar chart graphs. As a step prior to the study of the quantitative variables, the assumption of normality was analyzed with the Shapiro–Wilk test; later, they were described with the statistics of central tendency, mean (M) and dispersion, and standard deviation (SD). The relationships between variables were analyzed using the strategy of bivariate contingency tables (qualitative and/or ordinal variables) and the analysis of Pearson’s relational statistics (χ2). In the case of normal quantitative variables (parametric pathway), the relationships were analyzed with the Pearson (r) correlational strategy, and non-normal (non-parametric pathway) with the Spearman (ρ) correlational strategy. The comparisons of means were resolved by the parametric or non-parametric route depending on compliance with the previous assumption of normality. For the parametric pathway and in the case of comparing two means, they were resolved with the Student (t) test, and in the case of comparing three or more means, they were resolved with the Snedecor (F) test. For the non-parametric pathway and in the case of the comparison of two means, they were resolved with the Mann–Whitney (U) test, and in the case of the comparison of three or more means, they were resolved with the Kruskal–Wallis (H) test. In all cases, a type I error (α risk) of 0.05 ($5\%$) has been considered, that is, the confidence interval is $95\%$ with a significance index of $p \leq 0.05.$ The calculations were developed with the statistical package IBM-SPSS Statistics, version 19. ## 3. Results The analysis of the socio-demographic characteristics and the descriptive values of the age, height, and BMI (Table 2), besides the chronic diseases and the medication (Table 3) of the studied population, whose computation is that collected through the previously established inclusion criteria, bringing together a total of 415 people, offers the following results: Fear of falling test: When asked “Are you afraid of falling?” we obtain (Figure 1): When asking the people participating in the study, “Does the fear of falling limit your activities?” ( Figure 2). Within the limitation of activities due to fear of falling, we find that 244 people ($58.8\%$) have a moderate fear of falling from bathing or showering, 204 people ($49.2\%$) have a moderate fear of falling from going up–down stairs, and 220 people ($53\%$) have a moderate fear of falling for going out to an event. A total of 283 people ($68.2\%$) have little fear of falling from dressing–undressing, 278 people ($67\%$) have little fear of falling from sitting-getting up from a seat, and 246 people ($59.3\%$) have little fear of falling from picking things up or on the ground. The record of each patient is completed with the instrumentation of the five objective tests, tests, to which this work has already referred previously. All of them, after conducting normality tests, are not normal. To the question “Have you had falls?”, 226 people ($54.5\%$) answered no and 188 ($45.3\%$) answered yes, and as shown in Figure 3, the recorded number of falls has been: When analyzing the relationships that could occur between having had falls and the gender variable (Table 4), it is confirmed that there is a relationship between the two (χ2 = 20.86, $p \leq 0.001$). Thus, we see that of the 245 women 134 ($54.69\%$) have fallen, while of the 169 men, only 54 ($31.95\%$) fell. There is also a relationship between falls and having had support (χ2 = 9.42, $p \leq 0.001$), that is, of the 226 people who have not fallen, 128 do not have support and 98 do; additionally, of the 188 people who did fall, 78 people do not have support and 110 do (Table 4). Significant differences are found between height ($t = 2.07$ and $$p \leq 0.038$$) and age ($U = 17$,277.50 and $$p \leq 0.001$$), depending on whether they have fallen people who are shorter and older fall more (refer Table 5). Studying the relationship between having had falls and chronic disorders (Table 6), it is possible to show an existing relationship between having had falls and disorders related to the nervous system (χ2 = 7.46, $p \leq 0.001$). Of people who did not suffer from this type of chronic disorder, 142 ($62.8\%$) had not fallen and 84 ($37.2\%$) had, while of those who did, 93 ($49.5\%$) did not fall and 95 ($50.5\%$) did. In the same way, a relationship can be observed between having had falls and chronic psychological disorders (χ2 = 4.33, $p \leq 0.001$). Of the people who did not suffer from this type of psychological disorder, 123 ($54.4\%$) did not had fallen and 103 ($45.6\%$) had, while of those who did, 83 ($44.1\%$) did not fall and 105 ($55.9\%$) did. Additionally, having had falls and disorders related to the female genital tract and the male genital tract are related. In the case of the female, (χ2 = 11.39, $p \leq 0.001$). Of the 226 women who did not suffer from this type of disorder, 202 ($89.4\%$) had not fallen and 24 ($10.6\%$) had, while of the 188 people who did suffer from it, 145 ($77.1\%$) did not fall and 43 ($22.9\%$) did fall. In the case of men (χ2 = 10.48, $p \leq 0.001$), of the 226 men who did not suffer from this type of disorder, 155 ($68.6\%$) had not fallen and 71 ($31.4\%$) had, while of the men 188 who did suffer from it, 155 ($82.4\%$) did not fall and 33 ($17.6\%$) did fall. There is a relationship between having had falls and the consumption of medication (Table 7) for the nervous system (χ2 = 3.60, p = NS) and the respiratory system (χ2 = 7.095, $p \leq 0.001$). Deepening into the relationship between the intensity of fear of falling and the occurrence of falls (Table 8), we show that a relationship can be established between both variables (χ2 = 197.63, $p \leq 0.001$). Quantifying the relationship between the limitation of activities of daily living due to fear of falling and the occurrence of falls, we show that a relationship can be established between these variables (χ2 = 56.38, $p \leq 0.001$). Of the 226 people who have not fallen, 200 individuals ($88.5\%$) have not limited daily activities for fear of falling, and 26 individuals ($11.5\%$) have. Of the 188 people who did have falls, 105 ($55.9\%$) have not limited daily activities for fear of falling and 83 have ($44.1\%$). Observing the relationship between the fear of falling test and the occurrence of falls, we show that a relationship can be established between both variables (χ2 = 114.74, $p \leq 0.001$). Of the 226 people who have not fallen, 142 ($62.8\%$) are not afraid of falling, while of the 188 people who have fallen, 167 ($88.8\%$) are very afraid or moderately afraid of falling. Studying the relationships between having falls and the tests (Table 9), it is verified that there is a relationship between the variables having had falls and the Tinetti Test (X2 = 13.32, $p \leq 0.001$). In the moderate risk group, there are more people who have not suffered falls compared to those who have, 114 ($50.2\%$) compared to 61 ($32.4\%$). In the case of the severe risk group, a certain difference can be seen between both groups, 113 people have not fallen ($49.8\%$) and 127 ($67.6\%$) have fallen. Observing the relationship between the Barthel test and the occurrence of falls (X2 = 9.03, $$p \leq 0.029$$), it can be seen that there is a relationship between both variables; specifically, of the 226 people who have not fallen, 32 people ($14.2\%$) are independent, 189 people ($83.6\%$) are lightly dependent, and 5 people ($2.2\%$) are moderately dependent. Of the 188 people who did fall: 19 people ($10.1\%$) are independent, 154 people ($81.9\%$) are lightly dependent, 13 people ($6.9\%$) are moderately dependent, and 2 people ($1.1\%$) are severely dependent. Independent people do not fall as much, compared to severely, moderately, or slightly dependent people. In other words, the greater the dependency, the greater the proportion of falls that occur. Studying the possible relationship between the Downton-3 test (recoded at two levels) and the occurrence of falls, we show that a relationship can be established between both variables (X2 = 22.79, $p \leq 0.001$); more specifically, of the 226 people who have not fallen, 41 ($18.1\%$) are not at risk of falls and 185 ($81.9\%$) are at risk. Of the 188 people who did fall, 6 ($3.2\%$) are not at risk of falls, but 182 ($96.8\%$) are at risk of falls. ## 4. Discussion The mean age of our study was 82.63 years old, according to the inclusion criteria. This coincides with the CSIC [23] report in 2020, in which they indicate that the proportion of octogenarians continues to increase to a greater extent in Spain. Most of the older people in the current study are women. This distribution by age and sex (in our study: $$n = 245$$, $59\%$) maintains the same trend as that presented in other studies. This phenomenon alludes to the term known as Feminization of old age [23]. Taking as reference Eurostat, Healthy life years, based on the Living Conditions Survey [23], overweight and obesity are two characteristics present in the elderly [23]. In our study, we have verified this reality (BMI, $X = 27.82$, SD = 4.49), which is why we consider that nutritional status is a crucial factor to take into account due to its impact on the health and functionality of the elderly. On the other hand, the height of our sample has an average of 1.57 m, providing a relevant and curious fact: there are significant differences between height and age depending on whether or not they have had falls, being quantitatively more significant in people with less height and with greater age, as we have already commented in the results section. Despite being considered for a more exhaustive analysis of the characteristics of falls, we have not found this point in the specialized literature. Considering the marital status of our subjects, it can be seen that most of them are married, followed by widows and to a lesser extent single. The explanation can be found in the fact that the majority of the population object of this study belonged to the male sex, as in Spain in 2018, where the percentage of married men exceeds that of women in all age groups of 65 and over, exceeding the rest of the marital status of men in the same way [23]. However, this situation differs from other studies [24,25] and may be due to the fact that the populations compared were not homogeneous. Regarding the type of residence and cohabitation, it can be verified that most of the population in our study lives in urban areas and in flats, contrasting with another study on the presence of frail elderly in the population, carried out in Guadalajara [26], in which half of the population studied lived in rural areas. Chronic diseases, such as the presence of the pathology of Arterial Hypertension (HTA), are the majority in our sample ($F = 391$, $94.2\%$). This data coincides with the prevalence in the population over 60 years of age in Spain [27]. In this sense, there are studies that evaluate the association between the consumption of different antihypertensive drugs and the occurrence of falls, obtaining disparate results [28,29]. A relationship between the use of antihypertensives and the risk of serious injuries due to falls has not been proven [30]. Several lines of research suggest that antihypertensive drugs may increase both the risk of falls and injuries from falls in older adults. Most of the population in our study suffers from locomotor system problems ($F = 389$, % = 93.7). Coinciding with EESE 2020 [31], which indicates that they are among the most frequent diseases or chronic health problems suffered by the population: osteoarthritis, low back pain, and cervical pain. According to the World Health Organization (WHO), osteoporosis affects 3.5 million people in Spain. In 2010, it was estimated that, in the European Union, a total of 22 million women and 5.5 million men had been diagnosed with densitometric osteoporosis [32]. Among the fractures, the most common are hip and vertebral. In Spain, a prevalence of 104 cases per 100,000 inhabitants is estimated, which means 45,000 to 50,000 hip fractures per year with an annual cost of EUR 1591 million and a loss of 7218 quality-adjusted life years. Most of the participants present pathologies related to the endocrine system ($F = 358$, % = 86.3), coinciding with the national data, since more than a third of the population over 75 years of age suffer from diabetes [33]. One of the pathologies implicated in the risk of falls and that interferes with the autonomy of the basic activities of daily life is the sensory deficit and, specifically in our study, the visual deficit. $65.1\%$ ($F = 270$) of the participating older adults suffered from it, a prevalence rate similar to that of other investigations [34,35], which is why it also constitutes one of the main aspects in terms of fall prevention. The fact that in our study, we did not find high rates of cognitive impairment, unlike other works [36], may be because those people who had severe cognitive impairment did not fall within the inclusion criteria of the research. However, the prevalence of psychological disorders in our study does not differ from other investigations [37,38]. The results provided in our study on genitourinary problems present in more than half of the population analyzed ($F = 253$, $61\%$) agree with other studies at the national level [39,40]. These conditions interfere with the quality of life of the elderly, also implying a significant social, economic, and health cost. Medication consumed: the type of medication consumed can pose a greater risk in the production of falls, as is the case with psychoactive drugs [41,42]. It has been possible to verify that the following two types of medications, hypnotics and anxiolytics, and antidepressants, have been independently associated, by $50\%$ each, with the probability of falls [43]. The use of digoxin, a type of IA antiarrhythmic and diuretic, has also been associated with an increased risk of falls [44]. and the use of laxatives [45]. The results obtained in our study coincide with the rest of the investigations, the most used have been those related to the cardiovascular system ($F = 364$, $87.7\%$), followed by those that treat pathologies derived from the nervous system ($F = 343$, $82.7\%$), and finally, those related to the alimentary tract and metabolism ($F = 311$, $74.9\%$). We can also mention that the elderly in our study mostly consumed more than four medications. It has been discovered that there is a consistent relationship between polypharmacy and falls in the elderly. There have been several studies [44,45,46] that have considered taking four or more medications as important predictors, although this may be due to the number of associated chronic processes [46]. Most of the studies that analyze the degree of autonomy of the patients do so using the interpretation suggested by Sah et al. [ 47]; however, we have decided to use the interpretation used by Carballo et al. [ 48] because the ranges between the scores are less limited than in Shah’s [44]. Based on our results, the majority are mildly dependent ($82.9\%$). It is noteworthy how some of the people who receive a dependency score manage to hide this characteristic by developing alternative capacities for the execution of BADLs. In the present study, we can observe with Tinetti Test that most adults in our environment present a severe risk of falls ($57.8\%$), data similar to the study by Carballo-Rodríguez et al. [ 48], since $50\%$ of the sample presented a high risk of falls. It must be taken into account that there is a disparity in scores in three items compared to the original version of the Spanish translation in this scale, specifically in: “other medications”, “safe walking with help”, and “impossible walking”, since the author assigned them the value of 0 in the original version; however, in the version translated into Spanish, a value of 1 is provided if the conditions of each item are present. This fact was developed in a systematic review conducted by Aranda-Gallardo et al. [ 21]. In the present study, the Spanish translation has been used and the results have reflected how the majority, 368 people, present a risk of falls, compared to the 47 who do not. It has been possible to verify how this instrument is not recommended for use in the hospital setting, since the ability to predict the risk of falls decreases significantly. However, outside of this setting, there is no evidence to rule out its use. On the contrary, in a study [49] carried out with Swedish older adults, it has been shown how this test could be capable of independently predicting injuries related to falls, brain injuries, hip fractures, and mortality in older people. In a systematic review [50], where the validity of nine instruments was evaluated, including the modified versions, to assess the physical conditions of people aged 60 or over who live in the community, it was possible to verify how this battery is highly recommended, both in terms of validity, such as reliability and responsiveness. Regarding the results obtained in this test ($100\%$), only one level is detected: fragile people. Values shared in other previously conducted studies [19,51] are also in the community. It must be taken into account that the score we used is the same as in the study by Cabrero-García et al. [ 19], that is, categorical according to the execution intervals (0–4 points), unlike Abizanda Soler et al. [ 51] who use continuous scores based on the execution time of the tests. The fear of falling again is the main psychological consequence of falls [46]. Its prevalence in the community ranges in some studies from $3\%$ to others between $21\%$ and $85\%$ [52]. There are studies [53,54] that show that the prevalence of the fear of falling not only occurs in older adults who have suffered a previous fall, but also in older adults with no history of falls. That is why, even though most studies refer to the term “fear of falling again”, in our study the concept “fear of falling” is used, encompassing all people regardless of having suffered a fall. In addition, in our study women suffer more falls than men; of the 245 women, 134 have fallen and of the 169 men, 54 have fallen as in other studies [46,55]. It has been estimated that women have a 58 % more likely to sustain an injury from a non-fatal fall than men [56]. Additionally, in our study, shorter and older people suffered more falls. Regarding height, significant differences have only been found between men and women [48], but not in relation to falls. Regarding age, we can say that there is a relationship between the occurrence of falls and age [57]. There are studies [58] that coincide with our results, since they affirm that people who are 80 years old suffer more falls, since they have more associated pathologies [59]. Speaking of pathologies, our results indicate that there is a relationship between having had a fall and disorders related to the nervous system, chronic psychological disorders, and disorders related to both the female and male genital tract. Within chronic disorders, one of the most common pathologies is dementia, which is related to falls, as has been found in different studies [60,61]. Urinary incontinence is one of the major geriatric syndromes with a high prevalence and a negative impact on quality of life and loss of autonomy [62]. In both men and women, the rush to get to the bathroom caused by the urgency of the moment contributes to an increased risk of falls. We have been able to verify how having had falls and the consumption of medication for the nervous system are related (163 people that had been consuming medication for the nervous system also had fallen). In the same way, a study has confirmed how older women who live in the community and receive an active medication with activity on the central nervous system, including those who take benzodiazepines, antidepressants, and anticonvulsants, present a greater risk of suffering frequent falls [62]. One of the main psychological consequences of falls is the fear of falling. Since the elderly person suffers a fall, the fear of falling again is associated with a decrease in quality of life and an increase in frailty [62]. We can verify how there is a relationship between the intensity of fear of falling and the occurrence of falls (χ2 = 114.74, $p \leq 0.001$). Our results coincide with other studies [62]. ## 5. Conclusions With our study, we have been able to understand more about the socio-demographic profile of the elderly in our community, and to analyze the risk factors related to falls which affect them. In this way, we can provide more knowledge that allows us to promote future implementation of specific, effective risk prevention and management programs, focused on this most vulnerable population group, thus promoting a multidisciplinary and effective approach in reducing falls. In this way, we can also contribute to the achievement of an active ageing model. ## References 1. Oña A., Merino P., De la Cruz J., Montiel P.. Longevidad y beneficios de la actividad física como calidad de vida en las personas mayores. 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--- title: Statins as Potential Preventative Treatment of ETX and Multiple Pore-Forming Toxin-Induced Diseases authors: - Jing Huang - Baohua Zhao - Tingting Liu - Lin Kang - Jiaxin Li - Zishuo Guo - Ming Chen - Shan Gao - Jing Wang - Yanwei Li - Jinglin Wang - Wenwen Xin journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048941 doi: 10.3390/ijms24065414 license: CC BY 4.0 --- # Statins as Potential Preventative Treatment of ETX and Multiple Pore-Forming Toxin-Induced Diseases ## Abstract Epsilon toxin (ETX), produced by type B and D strains of Clostridium perfringens, can cause fatal enterotoxaemia in ruminant animals, particularly sheep, cattle, and goats. Previous studies show that the cytotoxicity of ETX is dependent on the integrity of lipid rafts, the maintenance of which is ensured by cholesterol. Zaragozic acid (ZA) is a statin drug that reduces the synthesis of squalene, which is responsible for cholesterol synthesis. In this study, ZA significantly reduced the toxicity of ETX in Madin–Darby canine kidney (MDCK) cells. We show that ZA does not affect the binding of ETX to MDCK cells, but propidium iodide staining (PI) and Western blotting confirmed that ZA significantly disrupts the ability of ETX to form pores or oligomers in MDCK cells. Additionally, ZA decreased the phosphatidylserine exposure on the plasma membrane and increased the Ca2+ influx of the cells. Results of density gradient centrifugation suggest that ZA decreased the number of lipid rafts in MDCK membranes, which probably contributed to the attenuation of pore-formation. Moreover, ZA protected mice against ETX in vivo. All mice pre-treated with ZA for 48 h before exposure to an absolute lethal dose of ETX (6400 ng/kg) survived. In summary, these findings provide an innovative method to prevent ETX intoxication. Considering many pore-forming toxins require lipid rafts, we tested and found ZA also inhibited the toxicity of other toxins such as Clostridium perfringens Net B and β-toxin (CPB) and Staphylococcus aureus α-hemolysin (Hla). We expect ZA can thus be developed as a broad-spectrum medicine for the treatment of multiple toxins. In addition, other statins, such as lovastatin (LO), also reduced the toxicity of ETX. These findings indicate that statin medicines are potential candidates for preventing and treating multiple toxin-induced diseases. ## 1. Introduction Clostridium perfringens is widespread in nature, one of the world’s most common pathogens and responsible for numerous zoonotic diseases [1]. C. perfringens produces at least 17 exotoxins [2] and is divided into 7 toxinotypes (types A–G) based on its 6 main lethal toxins (α, β, ε, ι, CPE, and Net B) [3,4]. Epsilon toxin (ETX) is secreted by type B and D strains of C. perfringens as an inactive prototoxin that is activated after the removal of the N- and C-terminal peptides by proteases [5]. ETX causes rapidly fatal enterotoxaemia in ruminant livestock, such as sheep, goats, and cattle, leading to large losses in animal husbandry annually [6]. When injected into rats, the toxin accumulates mostly in the kidneys and brain, resulting in injury to the blood–brain barrier and possible death [7,8]. ETX is the third most potent biological toxin after botulinum neurotoxins and tetanus toxin [9], with a $50\%$ lethal dose (LD50) of 65–110 ng/kg in mice [5,10], and is consequently classified as a category B biological agent by the United States Centers for Disease Control and Prevention [11]. Various cell lines are sensitive to ETX, including Madin–Darby canine kidney (MDCK) cells [12], murine renal cortical collecting duct principal (mpkCCDcl4) cells [13], human leiomyoblastoma (G-402) cells [14], Fischer rat thyroid cells (FRT) [15], and human renal adenocarcinoma (ACHN) cells [9,16]. Among these, MDCK cells are the most sensitive to ETX and have typically been used in previous ETX studies. As a pore-forming toxin, ETX binds to its putative receptor and forms a heptameric membrane complex on the MDCK cell membrane [17]. This leads to membrane permeabilization, which, in turn, results in a rapid decrease in intracellular K+, a rapid increase in Na+ and Cl−, and a delayed increase in Ca2+ [18,19]. Eventually, cells are killed by morphological changes, including swelling and the formation of membrane blebs [20]. The CT50 (the dose needed to kill $50\%$ of cells) of ETX in MDCK cells is 15 ng/mL [21]. The interaction between ETX and host cells requires an appropriate lipid environment. For example, ETX preferentially forms a heptameric pore within the detergent-resistant membranes (DRM) of MDCK cells, which are cholesterol- and glycosphingolipid-enriched microdomains [22]. Moreover, the removal of cholesterol by methyl-β-cyclodextrin (MbCD) impairs ETX binding and complex formation [13,22,23]. Many vaccine candidates are effective in protecting against ETX-induced diseases [19,24,25]. However, few options for overcoming ETX toxicity have been reported. One of these is neutralizing antibodies against ETX [26]. Two small-molecule inhibitors of ETX cytotoxicity have been identified as potential treatments [27]. However, given the potential use of ETX in bioterrorism, alternative countermeasures that inhibit the activity of the toxin are needed. A novel approach to drug development is to place the focus on the affected host cells instead of targeting the causative agent. The numerous host factors involved in ETX-induced cytotoxicity provide potential targets to block the toxic effects of ETX. Antagonists or agonists of these targets can potentially be used for new drug design and treating ETX poisoning [28]. Cytotoxicity of ETX depends on the integrity of lipid rafts, and cholesterol plays a vital role in maintaining the integrity of lipid rafts. This led us to speculate that lipid-lowering drugs may work as inhibitors of ETX cytotoxicity. Zaragozic acid (ZA) is a statin drug that inhibits the synthesis of squalene, which is responsible for cholesterol synthesis [29]. In this study, we investigate whether ZA can reduce the toxic effects of ETX on MDCK cells and explore the possibility of ZA as a candidate treatment for the toxic effects of ETX. We also explored whether lovastatin (LO), another statin medicine, could protect against ETX and the potential of ZA to treat other pore-forming toxins. ## 2.1. ZA Reduced Toxicity of ETX on MDCK Cells ETX with different tags (GST, 6×His, and mScar) did not differ significantly in toxicity (Figure 1A). The ETX (GST-ETX and His-ETX) dose needed to kill ~$50\%$ of MDCK cells (CT50) was 0.8 nM, and the CT50 of mScar-ETX was 0.4 nM, in agreement with the reported CT50 of 0.5 to 10 nM for the active wild-type ETX [13,20,21,26,30]. mScar-ETX exhibits stronger toxicity than ETX with other tags, probably because the mScar tag could enhance the stability of ETX. To investigate whether ZA (Figure 1B) could influence the cytotoxicity of ETX toward MDCK cells, cells were treated with a series of concentrations of ZA and then incubated with GST-ETX. A ZA concentration as high as 800 µM showed no cytotoxicity to MDCK cells. ZA significantly reduced the toxicity of 0.8 nM GST-ETX in a concentration-dependent manner, such that 800 µM ZA nearly abolished the toxicity of 0.8 nM GST-ETX (Figure 1C,D). The EC50 (concentration for $50\%$ of the maximal effect) value for ZA was 151.8 μM. However, ZA had no obvious effect on MDCK cells treated with a high concentration (17 nM) of GST-ETX. Morphological changes in MDCK cells exposed to ETX with or without ZA were observed (Figure 1E). When the concentration of ETX was 17 nM, the MDCK cells displayed shrinkage at 9 min, and at 18 min, cells were completely lysed. In contrast, the changes of MDCK cells exposed to 0.8 nM ETX were much slower; cells started to shrink at ~30 min and were completely lysed at ~1 h. In the presence of 800 µM ZA, 17 nM ETX leads to cell shrinkage at 18 min and total cell lysis by ~30 min, while the morphology of MDCK cells exposed to 0.8 nM ETX did not change compared to the control group. In order to exclude the influence of the cholesterol in the medium, the MTS assay was used to test the effect of ZA against 0.8 nM ETX in MDCK cells cultured in medium supplemented with $10\%$ fetal bovine serum (FBS) or $10\%$ lipid-depleted fetal bovine serum (LDS). The results showed no significant difference between the two groups (Figure 1F). Measures of Lactic Dehydrogenase (LDH) leakage revealed that ETX results in the release of intracellular LDH to the extracellular matrix (Figure 1G). However, the LDH released from ZA-treated cells was greatly reduced, which also suggests that ZA can reduce the cytotoxicity of ETX on MDCK cells. ## 2.2. ETX Binds to MDCK Cells Exposed to ZA ETX initiates various steps that induce the death of cells. Specifically, ETX first binds to a putative receptor on the surface of the cell membrane and then forms pores in the regions of DEM [22,31]. Subsequently, ions permeate through the cell membrane and phosphatidylserine exposure on the plasma membrane occurs [32]. The promising effect of ZA against ETX led us to explore which of these steps was obstructed by ZA. Binding to host cells is the first step by which ETX acts to induce cell death. Therefore, a binding assay was performed to study whether ZA affects the binding of ETX to MDCK cells. Confocal microscopy images revealed peripheral staining of mScar-ETX in MDCK cells, indicating that ETX was bound to the plasma membrane (Figure 2A). The average fluorescent intensity of MDCK cells first exposed to ZA was similar to cells not exposed to ZA (Figure 2B), indicating that ZA did not prevent the binding of ETX to MDCK cells. ## 2.3. ZA Reduces the Heptamer Formation on MDCK Cells To assess whether ZA reduces the capacity of ETX to form heptamers in MDCK cells, immunoblotting was performed. A high-molecular-weight band of ~224 kDa (the expected molecular weight of the heptameric complex) was observed in MDCK cells exposed to ETX alone. In cells exposed to ZA prior to ETX exposure, the grayscale of the heptamer band was significantly weakened at both concentrations of ETX tested (Figure 3A and Figure S1). This suggests ZA inhibits the formation of the heptamer. The monomeric ETX (band of ~32 kDa) was not obviously affected by ZA, indicating that the binding of ETX on cells was not affected by ZA, in agreement with the binding assay results above (Figure 2). ## 2.4. ZA Inhibits the ETX-Induced Pore Formation on MDCK Cells Previous studies have shown that ETX forms heptamers on MDCK cells, followed by the formation of β-barrel heptameric transmembrane pores [20,33]. To assess whether ZA inhibits such pore formation, we performed a pore-forming assay using propidium iodide staining (PI) staining, which has been demonstrated to cross these ETX-formed pores. As shown in Figure 3B, PI entered cells treated with 0.8 nM ETX and stained the nucleus, while cells treated with ZA had decreased entry of PI into cells. At an ETX concentration of 17 nM, PI entry into the MDCK cells in the presence of ZA was also reduced, although to a lesser extent (Figure 3C), suggesting that ZA can also disturb pore formation in MDCK cells at a high ETX concentration. ## 2.5. ZA Prevents Toxin-Induced Phosphatidylserine Exposure ETX was previously reported to trigger phosphatidylserine (PS) exposure on the plasma membrane of human erythrocytes [32], an indicator of apoptosis [34]. Similarly, ETX triggered PS exposure on $90.81\%$ of MDCK cells exposed to 17 nM ETX and $67.28\%$ of MDCK cells exposed to 0.8 nM ETX (Figure 4A). ZA significantly decreased the percentage of cells with PS exposure to $65.62\%$ with 17 nM ETX and $8.22\%$ with 0.8 nM ETX (Figure 4A). These results indicate that ZA decreases ETX-induced apoptosis (Figure 4D) and the death of cells (Figure 4C). We also measured the proportion of cells positive for PI staining. In the group with 17 nM ETX, $99.22\%$ of cells were stained with PI; while ZA had no effect on the percentage of cells stained, it did reduce staining intensity (Figure 4A,E). In the 0.8 nM ETX group, ZA decreased the proportion of PI-stained cells from $63.93\%$ to $16.78\%$ (Figure 4A). These results are consistent with the results from our pore formation assay above (Figure 3). The protective effect of ZA on cells is associated with the concentration of ETX. The higher the concentration of ETX, the lower the protective effect of ZA on cells. ## 2.6. ZA Strengthens Ca2+ Influx of MDCK Cells Treated with ETX To assess whether ZA can prevent the toxin-induced Ca2+ influx in MDCK cells, intracellular Ca2+ concentrations of cells were measured (Figure 4B,F). ETX exposure increased intracellular Ca2+ concentration of MDCK cells; $95.6\%$ of cells in the 17 nM group and $96.8\%$ of cells in the 0.8 nM group showed a clear Ca2+ influx. Surprisingly, ZA further increased the proportion of cells with Ca2+ influx (Figure 4B,F). In particular, the fluorescence intensity increased (Figure 4B, Figures S2 and S4), indicating that ZA increased the Ca2+ influx of MDCK cells treated with ETX. We also tested whether ZA could strengthen the Ca2+ influx of MDCK cells without ETX and found that ZA alone induces potent Ca2+ influx in MDCK cells (Figures S3 and S4). However, the role of Ca2+ influx in the cytotoxicity of ETX has yet to be precisely determined. A study indicated that the increments of the intracellular Ca2+ concentration reduced PS exposure and protected the cells [35]. Therefore, ZA-induced Ca2+ influx in MDCK cells probably has a positive effect on cells. ## 2.7. ZA Protects Mice from Death by ETX To see whether ZA can prevent the toxin-induced death of animals, BALB/c mice were given an intraperitoneal injection of ZA (50, 10, 2, and 0.4 mg/kg/day) (Figure S5) and then challenged with an absolute lethal dose of ETX (6400 ng/kg) (Figure S6) on time 0; 3 dosing groups were tested: 3 injections (−48, −24 and −0.5 h), 2 injections (−24 and −0.5 h), and 1 injection (−0.5 h) (Figure 5A). ZA significantly improved the survival rate of mice challenged with ETX (Figure 5B). All control mice challenged with an absolute lethal dose of ETX died within 3 h. In contrast, all mice injected with ZA three times and then challenged with ETX survived. Though the mice injected with ZA for 2 injections or 1 injection before the challenge with ETX died ($\frac{4}{5}$ dead for 2 injections and $\frac{5}{5}$ dead for 1 injection), ZA significantly extended the survival time (Figure 5B). In addition, the weight of surviving mice at different time points suggests that ZA protected mice and kept them in healthy living conditions (Figure 5C). We performed histopathological analysis on the organs (liver, kidney, lung, brain, and heart) of all mice 3 days after the ETX injection. In mice that died from ETX, obvious hemorrhage was present in the liver, kidney, and lung, while edema was present in the lung, liver, kidney, and brain of the ETX-only treated group. However, mice in the ZA-treated group (three injections) followed by the ETX challenge did not show hemorrhage or edema in the organs (Figure 5D). In addition, no histopathological changes were observed in the group injected with ZA alone or the group injected with PBS (Figure 5D). These results indicate that ZA can protect mice against ETX. Finally, we tested the ability of ZA injected 30 min after the ETX challenge (Figure 5E) and found that ZA significantly extended the survival time (from 4 to 8 h), although all mice died (Figure 5F). Multiple blood parameters of mice in each group were examined, and the results are shown in Figure 6 and Table 1. After being challenged by ETX, white blood cells (WBC), the number of lymphocytes (LYM), basophils ratio (BASO%), number of neutrophils (NEU), neutrophil ratio (NEU%), and number of monocytes (MON) in blood clearly increased, while the LYM ratio (LYM%) decreased (Figure 6B–H), indicating that ETX induced inflammatory response in mice [36]. ETX also led to increased alkaline phosphatase (ALP) (Figure 6I), suggesting liver damage and abnormal liver metabolism [37]. In addition, an abnormal increase in glucose (GLU) (Figure 6G) was presumably because liver damage prevented the degradation of GLU, which is mainly degraded in the liver [38]. A decreased urea nitrogen (BUN) and creatinine (Cre) and increase in serum calcium (Ca) and serum sodium (Na) (Figure 6K–N) indicate that ETX could damage the kidney of mice [39,40,41,42,43], consistent with previous reports that the kidney is one of the main target organs of ETX. However, after the treatment with ZA, these values tended toward normal; mice with the maximum ZA treatment time had blood biochemical parameters similar to the control group. Thus, results indicate that ZA can effectively protect mice from the toxicity of ETX. ## 2.8. ZA Inhibits the Synthesis of Cholesterol and Disrupts Lipid Rafts ZA inhibits cholesterol synthesis, and we hypothesized that ZA reduces the toxic effects of ETX by disrupting the membrane lipid rafts of host cells. We thus measured the cholesterol content of cells treated with a series of concentrations of ZA. As Figure 7A shows, ZA effectively reduced the cholesterol concentration of cells in a dose-dependent manner. For the animal assay, triglyceride (TG) levels of blood and cholesterol content of organs, such as the liver, kidney, and brain, in ZA-treated mice were significantly reduced (Figure 7B–F). To confirm that ZA disrupts lipid rafts, we measured the content of caveolin-1, a marker of lipid rafts on the membranes of MDCK cells. Cells were incubated with ZA for 30 min, then collected, and a density gradient centrifugation was performed. Western blots (Figure 7G) showed that caveolin-1 was recovered in fractions of lower density (fractions 4 to 9) and higher density (fractions 12 to 18). Compared with the control group, the bands of caveolin-1 from MDCK cells treated with ZA were weaker in lower-density fractions. This may indicate that ZA reduced the toxicity of ETX by decreasing the number of lipid rafts present on the membrane of MDCK cells. It is noteworthy that caveolin-1 still exists at a higher density, probably because the association of caveolae with the actin cytoskeleton is not disrupted by the lysis procedure, resulting in the generation of relatively heavy caveolar membranes. The results indicate that ZA decreases lipid rafts on the cell membrane by inhibiting the synthesis of cholesterol and that this more likely protects against the toxicity of ETX in vitro and in vivo. ## 2.9. Other Statin Medicine Inhibits the Toxicity of ETX Many approved statins other than ZA can lower cholesterol. We thus considered whether other statin medicines could inhibit the toxicity of ETX. To prove this, we treated MDCK cells with LO prior to exposure to ETX (0.8 nM and 17 nM) and measured cell viability with an MTS assay. LO inhibited ETX toxicity in a dose-dependent manner; the protective rate of cells exposed to 0.8 nM ETX, with a concentration of 250 μM, LO was nearly $100\%$, and the same concentration of LO increased cell viability under 17 nM ETX from $11\%$ to $23\%$ (Figure 8A). When mice were challenged by 6400 ng/kg ETX, compared with the untreated mice, the survival of mice pretreated with 10 mg/kg LO significantly increased from $0\%$ to $80\%$ (Figure 8B). These results indicated that other statins such as LO can be used to prevent ETX poisoning. ## 2.10. ZA Inhibits the Toxicity of Other Pore-Forming Toxins We demonstrated that ZA inhibits the toxicity of ETX by disrupting the membrane lipid rafts of host cells. Considering many pore-forming toxins require lipid rafts, we hypothesized that ZA could inhibit the toxicity of other pore-forming toxins. The MTS assay was used to test whether the inhibiting ability of ZA extended to other pore-forming toxins (Hla, CPB, and Net B). We chose MDCK cells for Hla and CPB based on their known sensitivity to these toxins (Figure S7). The CT50 dose of Hla to MDCK cells is 10.72 nM, the CT50 dose of CPB to MDCK cells is 136.2 nM, and the CT50 dose of Net B to MDCK cells is 22.46 nM. The three toxins were used to measure the protective effect of ZA on cells. ZA reduced the toxicity of these toxins in a concentration-dependent manner (Figure 9). The EC50 values for ZA was shown in Table 2. Specifically, 800 μM ZA increased the cell viability of MDCK cells from ~$50\%$ to ~$87\%$ when exposed to the CT50 dose of Net B. ZA at the same concentration almost completely inhibited the toxicity of CPB or Hla on MDCK cells. Thus, ZA appears to protect against a wide variety of pore-forming toxins. ## 3. Discussion ETX is a potent toxin, causing serious zoonotic diseases such as enterotoxaemia. It is also a potential bioterrorism agent. As such, ETX threatens the health of both livestock and humans. However, there are no effective therapies against ETX-induced diseases. Inspired by the fact that the cytotoxicity of ETX is dependent on the integrity of lipid rafts [22], we speculated that statin medicines might act as a medical countermeasure to reduce the toxicity of ETX. In this study, we show that ZA reduces the toxic effects of ETX on MDCK cells and protects mice against ETX in vivo by disrupting the ETX-induced pore-formation in lipid rafts. Lipid rafts play a central role in many cellular processes, including membrane sorting and trafficking, cell polarization, and signal transduction processes [44]. Several groups of pathogens, bacteria, prions, viruses, and parasites hijack lipid rafts for their purposes [45]. Lipid rafts in cell plasma membranes play a critical role in the life cycle of many viruses such as severe acute respiratory syndrome coronavirus 2 [46]. Lipid rafts are also key to the toxicity of many toxins [22,47,48]. ETX forms heptameric pores within the detergent-insoluble microdomains of MDCK cells and removal of cholesterol by MbCD impairs the complex formation of ETX [13,22]. Our results showed that ZA largely impedes the formation of pores by ETX, consistent with the findings of previous studies. Pore formation by ETX requires the fluidity of putative receptor molecules in the lipid rafts [49]. We suggest that ZA lowers the cholesterol level in lipid rafts and disrupts the lipid rafts of host cells. Cholesterol is thought to serve as a spacer between the hydrocarbon chains of the sphingolipids and to function as a dynamic glue that keeps the raft assembly together [44]. Hence, we suggested that ZA disrupts membrane lipid rafts of host cells by reducing cholesterol levels. It’s reported that cultured cells get most of their cholesterol from the serum in the media, not via de novo synthesis [50]. To exclude the influence of the cholesterol in the medium on the experiment, the MDCK cells were cultured with DMEM supplemented with $10\%$ FBS or $10\%$ LDS. ZA can reduce cholesterol in cultured cells regardless of whether the medium is supplemented with cholesterol or not. In addition, in contrast to lovastatin, which inhibits HMGCR [51], ZA inhibits the synthesis of squalene [52,53], which is located downstream of cholesterol synthesis. Considering ZA can effectively reduce the cholesterol concentration of cells, we speculate that ZA is less likely to protect cells by inhibiting sterols other than cholesterol. It is also possible that ZA inhibits the cytotoxicity of ETX by more than one pathway. Surprisingly, we found that ZA can increase the influx of Ca2+ in MDCK cells. The role of Ca2+ influx in the cytotoxicity of ETX is not yet precisely understood. Some studies have shown that Ca2+ influx may contribute to the swelling and apoptosis of MDCK cells exposed to ETX [13,31]. However, in our previous study, the activation of Ca2+ influx pathways did not always potentiate hemolysis of human erythrocytes, suggesting it may not be an absolute requirement for lysis to occur [32]. Another study found that Ca2+ influx may protect the cell from swelling and lysis [35]. ZA alone also induces potent Ca2+ influx in MDCK cells (Figure S6) but not cell death (Figure 1), and, thus, ZA-induced Ca2+ influx in MDCK cells likely has a positive effect on cells. This may suggest that ZA inhibits the cytotoxicity of ETX by more than one pathway. ZA protected mice against an ETX challenge. ZA is a blood-cholesterol-lowering agent, which may take hours or days to work in animals. Therefore, in this study, we measured the therapeutic effect of ZA against a high dose of ETX after pre-treating mice with ZA at −48, −24, and −0.5 h prior to the challenge. Results were consistent with the previous hypothesis that the longer mice are pretreated with ZA, the higher their survival rates when challenged with ETX. In addition, ZA is effective in reducing various symptoms of mice caused by ETX including inflammatory response and damage to the kidney and liver. ZA belongs to the large family of statin medicines, such as atorvastatin, fluvastatin, and lovastatin, many of which have been approved by the FDA for clinical use. Our results indicate that other statins, such as lovastatin, can be used in the treatment of ETX poisoning in mice. Statin medicines are typically cheap and widely available. Developing therapeutic medicine from available statins provides a clear advantage. Since cholesterol is essential for numerous pore-forming toxins, especially cholesterol-binding toxins, statins provide a good starting point for the treatment of multiple toxins. We show that ZA can inhibit the toxicity of many pore-forming toxins, such as Hla, CPB, and Net B, opening up the possibility of ZA as a promising broad-spectrum candidate to defend against a wide variety of pore-forming toxins. ## 4.1. Recombinant Toxins and Reagents Horseradish peroxidase (HRP)-conjugated goat anti-mouse IgG (H + L) antibody and anti-His monoclonal antibody were purchased from Abcam (Cambridge, MA, USA). 3-(4,5-dimethylthiazol-2-yl)-5(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium inner salt (MTS) was purchased from Promega Corporation (Madison, WI, USA). Anti-glutathione S-transferase (GST) monoclonal antibodies were purchased from EARTHOX Life Sciences (Millbrae, CA, USA). Annexin V, annexin-V-binding buffer, and PE anti-human CD235a (glycophorin A) antibodies were purchased from BioLegend (San Diego, CA, USA). DAPI (4′,6-diamidino-2-phenylindole), fluo-4, and propidium iodide were purchased from Sigma (St. Louis, MO, USA). Zaragozic acid A trisodium salt (ZA) was purchased from ChemCruz (Huissen, The Netherlands). Recombinant toxins, 6×His-tagged ETX, GST-tagged ETX, mScar-ETX (mScarlet fluorescent protein fusion protein), CPB, Hla, and Net B proteins, were expressed and purified as previously described [32,54]. ## 4.2. Cell Culture and Animals MDCK cells were grown and maintained in Dulbecco’s Modified Eagle Medium (DMEM, Gibco, Carlsbad, CA, USA) and supplemented with $10\%$ fetal bovine serum (FBS, Gibco, Carlsbad, CA, USA) or $10\%$ lipid-depleted fetal bovine serum (LDS, VivaCell, Shanghai, China) at 37 °C in an atmosphere of $5\%$ CO2. BALB/c mice (6–8 weeks old, 20–25 g body weight) were purchased from Vital River (Beijing, China). ## 4.3. Cytotoxicity Assay Three recombinant ETX with different tags (Glutathione-S-transferase (GST), 6×His, and red fluorescent protein mScarlet (mScar)) were used in this study. A concentration of 105 MDCK cells/mL was grown to confluence in 96-well plates for 24 h. After 3 washes with PBS, cells were exposed to mScar-ETX, His-ETX, and GST-ETX at 20 levels ranging evenly from 0 (as a control) to 2100 nM (diluted by double volume) and incubated at 37 °C for 1 h. MTS was added to plate wells, and toxicity was estimated by measuring absorbance at 492 nm. The CT50 dose of CPB, Hla, or Net B to MDCK cells also were measured by MTS assays. Subsequently, cells were treated with ZA (800, 400, 200, 100, and 50 μM) for 30 min, then incubated with toxins for 1 h. Toxins alone (no ZA) were added to cells as a positive control, and DMEM was added to the cells as a negative control. Next, MDCK cells grown in 96-well plates for 24 h were treated with different serial concentrations of ZA [0 (as a control), 12.5, 25, 50, 100, 200, 400, and 800 μM] for 30 min at 37 °C. GST-ETX, CPB, Net B, or Hla was then added to the medium, and cells were incubated at 37 °C for 1 h. The cytotoxic activity of pore-forming toxins was measured using the MTS colorimetric assay. In a third experiment, MDCK cells were observed using a Molecular Devices ImageXpress Micro confocal microscope (Molecular Devices, California, CA, USA). Briefly, cells cultured in 96-well plates for 24 h were washed with PBS 3 times, then preincubated with 800 μM ZA. After 30 min, different concentrations of GST-ETX (0, 0.8, and 17 nM) were added to the wells, immediately after which cells were observed under the confocal microscope. Each experiment was performed in triplicate. ## 4.4. Binding of Recombinant Proteins to MDCK Cells MDCK cells (~105 cells/mL) were seeded in a confocal dish and incubated at 37 °C for 24 h. Cells were then washed with PBS 3 times and incubated with ZA for 30 min, followed by mScar-ETX for 1 h. After being washed again three times with PBS, the cells were stained using DAPI. Samples were observed using a laser confocal scanning microscope (SP8; Leica, Wetzlar, Germany). ## 4.5. Heptameric Oligomerization To observe the effect of the formation of ETX complexes in MDCK cells, cells were grown to confluence in 150 mm diameter plates and then incubated in the same culture medium with ZA (800 μM or 0 μM as a control) for 30 min at 37 °C. Then, His-ETX (17 and 0.8 nM) was added to the medium, and cells were incubated for 1 h. After incubation, cells were washed 3 times with PBS and scraped off with a rubber policeman into 500 µL of ice-cold lysis buffer (PBS with $1\%$ Triton-X 100) supplemented with $1\%$ protease inhibitor. The lysates were centrifuged at 16,000× g for 30 min at 4 °C. Supernatants and pellets were electrophoresed on a $15\%$ polyacrylamide SDS-PAGE gel and electrically transferred to a PVDF membrane (Millipore, Burlington, MA, USA). After blocking with $5\%$ skim milk powder for 1 h, the membrane was incubated with a primary antibody (mouse anti-His monoclonal antibody diluted in PBST as 1:1000) at 4 °C overnight, and then incubated with HRP-conjugated secondary antibodies (goat anti-mouse polyclonal antibodies diluted in PBST as 1:5000) for 2 h. The blots were imaged and analysis of bands using the ImageQuant LAS4000 system (GE Healthcare, Boston, MA, USA). ## 4.6. Pore-forming Assay MDCK cells were grown on a confocal dish and incubated with or without 800 µM ZA for 30 min, followed by exposure to GST-ETX (17, 0.8, or 0 nM as a control) for 1 h. PI was also added to the culture medium with GST-ETX. After three washes with PBS, cells were stained with DAPI and examined using a laser confocal scanning microscope (SP8; Leica, Wetzlar, Germany). The percentage of PI-positive cells was obtained by dividing the number of PI-positive cells by the total number of cells (PI/DAPI). ## 4.7. LDH Assay We assumed that increased LDH concentrations reflected pore formation. To test this hypothesis, MDCK cells were incubated with 800 µM ZA for 30 min and then exposed to ETX (17 nM, 0.8 nM, and 0 nM as control) for 60, 45, 30, 15, 10, 5, or 0 min. The leakage of cellular LDH was measured in cell culture supernatants using the LDH-GloTM cytotoxicity assay kit (Promega Corporation, Madison, WI, USA) according to the manufacturer’s instructions. ## 4.8. Flow Cytometry MDCK cells were grown on a 6-well plate, preincubated with 800 µM ZA for 30 min, and then incubated with GST-ETX (0.8 or 17 nM) for 1 h at 37 °C. After incubation, cells were washed 3 times with PBS and digested with $0.25\%$ trypsin to get a suspension of cells. For annexin-V-binding studies, 105 cells were analyzed per experimental condition. PI was added to the same suspension. The cells were centrifuged for 10 min at 1000× g, resuspended in a solution containing annexin V and annexin V binding buffer, and incubated in the dark for 10 min. The suspension was diluted 5-fold in Ca2+-containing saline, and then analyzed on a FACSaria flow cytometer (Becton Dickinson and Company, New Jersey, NJ, USA) with excitation at $\frac{488}{535}$ nm and emission at $\frac{520}{615}$ nm. All experiments were conducted at 37 °C. The intracellular Ca2+ concentration was also measured on a FACSaria flow cytometer. MDCK cells were preincubated with 800 µM ZA for 30 min and then digested with $0.25\%$ trypsin to get single-cell suspensions. A suspension of MDCK cells (~106 cells/mL) was incubated for 25 min at fluo-4 AM (5 μM) at 37 °C, washed once with PBS, and then centrifuged at 1000× g for 10 min at room temperature. After the addition of ETX (0.8 and 17 nM) in Ca2+-containing saline and incubation for 10 min, the cells were measured in a FACSaria flow cytometer with excitation at 488 nm and emission at 520 nm. ## 4.9. Animal Experiments To learn whether ZA can inhibit the toxicity of ETX in vivo, we analyzed its effect in a murine model. BALB/c mice approximately 6 weeks old were injected with 0.1 mL of ZA (50 mg/kg/day) 1 (−0.5 h), 2 (−24 and −0.5 h), or 3 times (−48, −24, and −0.5 h) before being challenged with GST-ETX (6400 ng/kg) at time 0. As a control, 1 group of mice was injected with 0.1 mL of PBS with −48, −24, and −0.5 h. Mice were monitored for 3 days, and survival was recorded. Finally, blood was taken from the hearts of all mice for biochemical analysis, and the samples of organs dissected from mice were fixed in $4\%$ formaldehyde for 24–48 h. Samples from dissected mouse organs were dehydrated using ethanol solutions of increasing concentration and xylene solution and then embedded in paraffin. The paraffin-embedded tissue was sliced into 5-µm-thick sections. Sections were heated at 63 °C for 2 h, followed by dewaxing of xylene and decreasing ethanol solutions. Sections were stained with hematoxylin and eosin (H & E). Photographs of the sections were taken using a bright field microscope with a digital camera (Olympus IX71, Tokyo, Japan). ## 4.10. Density Gradient Centrifugation MDCK cells were plated onto D150 plates. After 24 h, the plates of cells were washed with PBS and pre-incubated with 800 μM ZA for 30 min. Cells were then washed and scraped into base buffer (20 mM Tris-HCl, 250 mM sucrose, 1 mM CaCl2, and 1 mM MgCl2, to which was added $1\%$ protease inhibitors; pH = 7.8). Cells were pelleted by centrifugation for 2 min at 250× g and resuspended in 1 mL of base buffer. The cells were then lysed by ultrasonication for 1 min, and lysates were centrifuged at 12,000× g for 10 min. The supernatant was collected and transferred to a separate tube. The sediment was resuspended with 1 mL base buffer and ultrasonicated for 1 mL. After centrifugation at 12,000× g for 10 min, the second supernatant was combined with the first. An equal volume (2 mL) of base buffer containing $50\%$ OptiPrep (Stemcell, Vancouver, VAN, Canada) was added to the combined postnuclear supernatants and placed in the bottom of a 12 mL centrifuge tube. An 8 mL gradient of $0\%$ to $20\%$ OptiPrep in base buffer was poured on top of the lysate. Gradients were centrifuged for 90 min at 52,000× g using an SW-41i rotor in a Beckman ultracentrifuge. Gradients were fractionated into 0.67 mL fractions, and the distribution of proteins was assessed by Western blotting. ## 4.11. Cholesterol Assay For measurement of cholesterol, MDCK cells were seeded at 105 cells/mL in complete culture medium (containing $10\%$ FBS or LDS) in 96-well plates. After 24 h, media was replaced with ZA (800, 400, 200, 100, 50, or 0 μM as control) for 30 min. Total cellular cholesterol was measured using the Amplex red cholesterol assay kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. For the animal assay, mice were injected with ZA 1 to 3 times (at −48, −24, and −0.5 h; Figure 7A). After 30 min on the last day, liver, kidney, and brain tissue were dissected from mice and ground. Cholesterol content in organs was measured using the Amplex red cholesterol assay kit. ## 4.12. Statistical Analysis Flow cytometry data were analyzed using analysis of variance (ANOVA) and Student’s paired t-tests. $p \leq 0.05$ was used as the criterion for statistically significant differences between groups. ## 5. Conclusions In conclusion, we found that ZA significantly reduced the toxicity of ETX to MDCK cells and mice. Subsequently, we demonstrated that ZA did so by disturbing the associated pore formation and not affecting binding to host cells. In addition, ZA decreased the exposure of PS on the plasma membrane and promoted the Ca2+ influx of the cells, which likely contributed to the attenuation of ETX cytotoxicity. ZA inhibits the synthesis of cholesterol and further disrupts membrane lipid rafts of cells. Excitingly, ZA protected mice against ETX. ZA also inhibited the toxicity of other pore-forming toxins and thus may prove to be a broad-spectrum therapeutic medicine. Furthermore, other statins, such as LO, also can reduce the toxicity of ETX. These findings indicate that statin medicines are potential candidates for preventing and treating multiple toxin-induced diseases. ## References 1. Uzal F.A., Giannitti F., Finnie J.W., Garcia J.. 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--- title: Do Weight Changes Affect the Association between Smoking Cessation and the Risk of Stroke Subtypes in Korean Males? authors: - Seulji Moon - Yeun Soo Yang - Heejin Kimm - Keum Ji Jung - Ji Young Lee - Sun Ha Jee - Sunmi Lee - So Young Kim - Chung Mo Nam journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048944 doi: 10.3390/ijerph20064712 license: CC BY 4.0 --- # Do Weight Changes Affect the Association between Smoking Cessation and the Risk of Stroke Subtypes in Korean Males? ## Abstract [1] Background: We investigated whether weight changes affect the association between smoking cessation and stroke risk; [2] Methods: Overall, 719,040 males were categorized into eight groups according to smoking status (sustained smokers, non-smokers, long-term quitters (quit > 4 years), and recent quitters (quit < 4 years)) and post-cessation weight change (−5 kg, −5.0 to 0.1 kg, maintainers, 0.1–5.0 kg, and >5.0 kg). The hazard ratios (HR) and $95\%$ confidence intervals (CI) for incident total, ischemic, and hemorrhagic strokes, including subarachnoid and intracerebral hemorrhage, were calculated using Cox proportional hazard models; [3] Results: We detected 38,730 strokes (median follow-up, 25.7 years), including 30,609 ischemic and 9055 hemorrhagic strokes. For recent quitters with a >5.0 kg or 0.1–5.0 kg weight increase, maintainers, or those who lost 0.1–5 kg, the multivariable HR for total stroke was 0.73 ($95\%$ CI, 0.67–0.79), 0.78 ($95\%$ CI, 0.74–0.82), 0.77 ($95\%$ CI, 0.69–0.85), 0.84 ($95\%$ CI, 0.77–0.90), and 1.06 ($95\%$ CI, 0.92–1.23), respectively, compared with that of sustained smokers; [4] Conclusions: Comparable patterns were obtained for stroke subtypes. Thus, we strongly recommend quitting smoking, as weight gain after quitting smoking does not alter the stroke-related benefits. ## 1. Introduction Stroke is a major cause of disability and mortality worldwide [1,2,3]. According to the 2017 Global Burden of Diseases, Injuries, and Risk Factors Study, stroke is ranked third among all global causes of death and disability, and is the second-leading cause of death. Additionally, a slow decline in age-standardized stroke incidence has been reported between 1990 and 2017 [3,4]. The lifetime risk of stroke has not decreased since 1990, but has, rather, increased steadily [1]. Moreover, East Asian nations have been experiencing an increase in the aging population, while changes in lifestyle and eating habits, due to rapid industrialization, urbanization, and automation, have led to distinct patterns in stroke risk factor profiles [5]. The main risk factors for stroke include hypertension, active smoking, obesity, poor eating habits, inadequate exercise, and diabetes [6,7,8]. Tobacco smoking is a modifiable risk factor for all stroke subgroups (including hemorrhagic and ischemic strokes) [9,10,11]. Moreover, smoking cessation lowers the risk of stroke [12,13,14]. In contrast, smoking after a stroke increases the risk of stroke recurrence, with the amount of smoking directly influencing the stroke risk [15,16]. Therefore, controlling these risk factors is essential for preventing stroke and lowering the risk of other cardiovascular diseases (CVD). Smoking cessation can reduce the risk of stroke; however, it is often accompanied by weight changes [17,18]. According to a meta-analysis, 16–$17\%$ of individuals who quit smoking lose weight in the first year, whereas approximately $80\%$ gain weight [19]. The average weight increase among these individuals is reportedly 4.10 kg, with a significant 2.61 kg difference between smokers and quitters [17]. Moreover, weight independently influences the incidence of a stroke. There is an increase in stroke risk with a considerable decrease or increase in weight, resulting in a U-shaped relationship between the two variables [20,21,22]. Given that tobacco smoking and weight changes represent distinct risk factors for stroke [23], the current study aimed to determine whether there is a change in the risk of stroke when considering both factors. Previous studies have exclusively focused on weight increases following smoking cessation [24,25], whereas we examined the association of weight reduction with smoking cessation as well. Moreover, unlike myocardial infarction (MI), caused by large-vessel atherosclerotic disease affecting the coronary arteries, identifying stroke risk is challenging because of its complex etiology, resulting in multiple stroke subtypes [6]. Bogiatzi et al. classified stroke subtypes, but only investigated ischemic stroke [26]. Similarly, Grau et al. divided stroke subtype into five categories; however, they did not include hemorrhagic stroke subtypes, such as subarachnoid hemorrhage (SAH) and intracerebral hemorrhage (ICH). Therefore, to explore the effects of each stroke subtype, we analyzed ischemic and hemorrhagic stroke and their subsets (SAH and ICH). Our research question was, “Do weight changes affect the association between smoking cessation and the risk of stroke subtypes in Korean males?” The primary aim of this study was to demonstrate the association between tobacco smoking cessation in the long-term and recent quitting, increased and decreased body weight, and each stroke subtype, including SAH and ICH. ## 2.1. Study Population The study participants were recruited from the Korean Cancer Prevention Study (KCPS) [27,28]. The KCPS cohort comprised insured individuals who were government employees and the staff of private schools. Participants were enrolled with the Korean Medical Insurance Corporation (currently the National Health Insurance Service (NHIS)) as members of the government employees’ union and private school staff union. Participants who underwent biennial physical examinations at least once between 1992 and 1999 were enrolled in the KCPS cohort [27,28]. Study participants were selected from 2,376,395 individuals who enrolled in the KCPS between 1992 and 1999. Among these, 1,047,682 individuals with health checkup information available 4 years after their initial visit were included in this study. The exclusion criteria were as follows: history of cancer or stroke ($$n = 13$$,557), age < 20 years ($$n = 1671$$), missing baseline data ($$n = 16$$,144), and survival time < 0 days ($$n = 22$$). In addition, we disregarded outliers regarding height, weight, and body mass index (BMI; $$n = 975$$). By examining the scatter plots for height and weight, we confirmed that neither of the variables exhibited a normal distribution. Each variable was log-transformed, and outliers were defined as those with values that deviated from the mean by more than six times the standard deviation. Individuals with an extreme BMI (<16 and >100 kg/m2) were excluded (Figure 1). Moreover, females were excluded from the primary analysis, as they comprised approximately $30\%$ of the overall study population, of whom $98\%$ were non-smokers. Finally, the analysis included 719,040 males. ## 2.2. Data Collection and Definitions of Covariates Participants were required to complete self-reported questionnaires created by the NHIS about their basic lifestyles and undergo standard examinations [29]. They were instructed to respond to questions on smoking history (never, former, or current), daily alcohol consumption (g/day: ethanol), participation in exercise (yes, no), medical history, including diabetes (yes, no), hypertension (yes, no), hyperlipidemia (yes, no), stroke (yes, no); and family medical history, including cancer (yes or no) and stroke (yes or no). Participants were instructed to remove their shoes and wear thin clothes for weight and height measurements. BMI was calculated by dividing the weight (kg) by the square of height (m2). BMI was then classified according to the World Health Organization Asia-Pacific classification; individuals were defined as “underweight” (BMI < 18.5 kg/m2), “normal” (18.5 ≤ BMI ≤ 22.9 kg/m2), “overweight” (23.0 ≤ BMI ≤ 24.9 kg/m2), or “obese” (BMI ≥ 25 kg/m2) [30]. Blood pressure was measured using standard mercury or an automatic sphygmomanometer in a sitting position [27,31]. Fasting blood specimens were collected and assayed for total cholesterol levels. Baseline blood sample characteristics and patient history, determined by self-reported responses, were used to determine disease history, which was analyzed as a covariate. Hyperlipidemia, diabetes, and hypertension were defined as total cholesterol levels ≥ 200 mg/dL, fasting blood glucose level ≥ 126 mg/dL, and systolic blood pressure (SBP) ≥ 140 mm Hg or diastolic blood pressure (DBP) ≥ 90 mm Hg, respectively. ## 2.3. Definition of Main Outcome and Exposure The primary outcome of the study included stroke and its subtypes (hemorrhagic, ischemic, or unspecified stroke). Hemorrhagic stroke included SAH and ICH. ICD-10 codes for SAH (I60), ICH (I61), ischemic hemorrhage (I63), and unspecified strokes (I64) have been validated previously [32,33,34,35,36]. Follow-up was conducted from 1992–1999, to 31 December 2019. During follow-up, disease outcome variables were ascertained using the NHIS. Abstractors coded incident disease cases according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10). Our main focus, smoking status, was defined as follows. Participants enrolled in the study between 1992 and 1999 were defined in terms of smoking status 4 years after enrollment. For instance, by comparing the smoking status 4 years after enrollment, based on the study enrollment period (1995 (for the 1992 study participants) and 1996 (for the 1993 study participants)), data were used to determine the subjects’ smoking status (Figure 2). We evaluated participants according to their self-reported smoking status based on three choices: never smoker, former smoker, or current smoker. Based on self-reported questionnaire responses from the first to third visit after 4 years, smokers were divided into four categories (sustained smokers, non-smokers, long-term quitters, and recent quitters). Sustained smokers and non-smokers were defined as individuals who responded with “current smoker” or “never a smoker” during both visits, respectively. Those who had quit smoking were divided into long-term and recent quitters; the former were defined as those who responded with “former smoker” during both visits, while the latter were defined as those who changed their response from “current smoker” during the first visit to “former smoker” during the third visit. Recent quitters were divided into five groups based on the degree of weight change, by comparing the subjects’ weight between the first and third visits. Individuals with weight changes between −0.1 kg and +0.1 kg were classified as maintainers; between +/−0.1 kg and +/−5.0 kg as mild gainers/losers; greater than +/−5 kg as severe gainers/losers. ## 2.4. Statistical Analyses This study is an observational study with a long-term retrospective cohort. It was conducted following the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. We evaluated the effect of smoking cessation and weight change on stroke risk using sustained smokers as the reference group. Person-years were estimated from the beginning of the study to the end, considering the date of death, loss to follow-up, or end of the study, depending on which occurred first. Estimated hazard ratios (HR) and $95\%$ confidence intervals (CIs) for smoking status and stroke incidence were calculated using Cox proportional hazards regression models. The Cox proportional hazards hypothesis was evaluated using log cumulative hazard graphs and time-dependent coefficients in Cox models. HRs were calculated from two different models: a basic one with adjustment for age and BMI (Model 1), and a multivariate one (Model 2) with adjustment for age, BMI, exercise, alcohol consumption, medical history (diabetes, hyperlipidemia, and hypertension), and family medical history (cancer and stroke). All outcomes were for males, and the HRs were reported for both models. Statistical calculations and analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and data were visualized using R 4.0.5 (R Core Team, Vienna, Austria). p-values < 0.05 were considered statistically significant. ## 3.1. Baseline Characteristics Table 1 shows the baseline characteristics of the study participants according to smoking and weight gain/loss statuses. Among the 719,040 males enrolled in the study, $60.2\%$ were sustained smokers, followed by non-smokers ($23.3\%$), and long-term ($8.3\%$) and recent quitters ($8.2\%$). The most prevalent age range within each smoking group was 30–39 years. In the long-term quitter group, the highest proportions of individuals, following the 30–39 year-old group, were observed in 40–49 and 50–59 year-old age groups, accounting for approximately $50.0\%$. This proportion was higher than those of the corresponding ages in other groups. Sustained smokers comprised $48.0\%$ of overweight or obese populations, whereas long-term quitters comprised $56.5\%$ of obese populations. These results showed long-term quitters were more likely to be overweight than sustained smokers. However, the mean weight change between the two groups at the first visit and after 4 years was not significantly different. Among recent quitters, mild gainers (0.1–5 kg increase) accounted for $41.7\%$, followed by severe gainers ($27.0\%$) and mild losers ($17.0\%$). Age proportions, according to the weight change of recent quitters, were similar; however, the ratio of those aged 20–29 years in the severe gainer group differed by at least $10\%$ from their corresponding age ratios in the other four groups. Severe losers, including those who lost >5 kg, experienced an average weight change of 7.3 kg; this group comprised the highest percentage of obese individuals. Additionally, this group showed high mean values for all blood parameters, including total cholesterol, SBP, DBP, and fasting blood glucose, as well as having high rates of diabetes, hypertension, and hyperlipidemia in their medical histories. ## 3.2. Association between Smoking Cessation and Stroke Outcomes in Korean Males First, the risk of stroke according to the smoking cessation period was analyzed. During follow-up, we identified 38,730 stroke events of any type, 30,609 ischemic strokes, 9055 (SAH: 2593 and ICH: 6727) hemorrhagic strokes, and 1390 stroke events of unspecified type. The incidence rates of total stroke were higher among sustained smokers (221 per 100,000 person-years) and long-term quitters (229 per 100,000 person-years) than among non-smokers (176 per 100,000 person-years) and recent quitters (204 per 100,000 person-years). Ischemic stroke had the highest incidence rate, followed by hemorrhagic stroke, subarachnoid hemorrhage, intracerebral hemorrhage, and unspecified stroke (Table 2). The risk of all stroke types was lower among non-smokers, long-term quitters, and recent quitters than among sustained smokers. After multivariable adjustment, the risk of total (HR 0.62, $95\%$ CI 0.60–0.64), ischemic (HR 0.59, $95\%$ CI 0.57–0.61), hemorrhagic (HR 0.74, $95\%$ CI 0.69–0.80), and unspecified stroke (HR 0.81, $95\%$ CI 0.68–0.97) was significantly lower for long-term quitters. Similarly, after multivariate adjustment for recent quitters, the risk of total (HR 0.79, $95\%$ CI 0.76–0.82), ischemic (HR 0.78, $95\%$ CI 0.74–0.81), and hemorrhagic (HR 0.80, $95\%$ CI 0.74–0.69–0.87) strokes were significantly reduced. Among recent quitters, the risk of all other types of stroke was significantly lower in Model 1 (adjusted for age and BMI) and Model 2 (adjusted for all covariates), except for undefined stroke (Table 3). These protective results were also observed among recent quitters; however, recent quitters exhibited fewer positive health effects than long-term quitters. ## 3.3. Association between Weight Changes in Recent Quitters and Stroke Outcomes in Korean Males Table 4 shows stroke incidence rates in the subgroups of recent quitters based on weight changes in Korean males. Regarding total stroke, severe losers had the highest total number of stroke incidences (377 per 100,000 person-years), and severe gainers had the lowest (138 per 100,000 person-years). Regarding stroke subtypes, severe losers had the highest ischemic stroke incidence rate (309 per 100,000 person-years), and severe losers had the lowest ischemic stroke incidence rate (107 per 100,000 person-years). The incidence rate of hemorrhagic and ischemic stroke was similar in all groups, with severe losers having the highest hemorrhagic stroke incidence rate (68 per 100,000 person-years) and severe gainers having the lowest incidence rate (31 per 100,000 person-years). The table also shows that SAH was less common than ICH, with severe losers having the highest SAH incidence rate (30 per 100,000 person-years) and severe gainers having the lowest incidence rate (10 per 100,000 person-years). Finally, the table shows the incidence of unspecified stroke, with severe losers having the highest rate (18 per 100,000 person-years) and severe gainers having the lowest rate (6 per 100,000 person-years) (Table 4). We assessed the risk of stroke in the recent quitters’ subgroup, based on weight changes in Korean males. We investigated the results of Model 1 after adjusting for age and BMI, and observed differences among the weight change findings in the recent quitters’ subgroup. The detailed HR values and $95\%$ CIs are shown in Supplemental Table S1, and the overall results are represented graphically (Figure 3). The weight change outcomes in all subgroups were statistically significant for total stroke incidence. An overall decrease in the risk of total stroke was observed in the weight change groups when severe losers were excluded (Figure 3). Among these subgroups, severe gainers had the lowest risk (HR 0.72, $95\%$ CI 0.66–0.78), followed by mild gainers (HR 0.78, $95\%$ CI 0.72–0.82), maintainers (HR 0.78, $95\%$ CI 0.71–0.87), and mild losers (HR 0.88, $95\%$ CI 0.81–0.94). After multivariate adjustment, a significant reduction in the risk of total stroke was detected among severe gainers (HR 0.73, $95\%$ CI 0.67–0.79), followed by mild gainers (HR 0.78, $95\%$ CI 0.74–0.82), maintainers (HR 0.77, $95\%$ CI 0.69–0.85), and mild losers (HR 0.84, $95\%$ CI 0.77–0.90) (Supplemental Table S1). These results were similar to the trends of ischemic and hemorrhagic strokes; severe gainers experienced the most protective association among all weight change subgroups (Figure 3). In addition, we examined the results, focusing on weight loss. Severe weight loss of >5 kg was associated with a higher risk of total (HR 1.18, $95\%$ CI 1.02–1.36) and ischemic stroke (HR 1.19, $95\%$ CI 1.02–1.40), after adjusting for age and BMI. In addition to hemorrhagic stroke, its subtypes (SAH and ICH), and unspecified stroke, severe weight loss was associated with an increased risk of health outcomes, but it was not significant. Among unspecified strokes, no significant results were observed according to the weight change subgroups. Therefore, we conducted a sensitivity analysis by omitting patients who were obese at baseline. The findings of the sensitivity analysis supported the trends of the main study (Supplemental Tables S2–S4). ## 4. Discussion Using a large prospective cohort study dataset for Korean males, we confirmed a reduced risk of stroke and its subtypes in individuals who quit tobacco smoking when compared with sustained smokers. This study confirms that weight gain after tobacco smoking cessation alters the health benefits of smoking cessation, in terms of the incidence of certain types of strokes. This stroke-related health benefit of quitting tobacco smoking persisted despite weight gain after smoking cessation. These findings were corroborated by a meta-analysis of similar cohort studies, which reported that weight gain in quitters did not reduce the protective association against stroke [13]. There were two significant disparities between our study and previous studies [24,25]. In previous studies, participants were divided into weight gain and no weight gain groups, and the total incidence of stroke was reported. However, our study classified weight loss into severe and mild weight loss groups, while also reporting the stroke subtypes. A follow-up study of 69,910 Japanese individuals with an average follow-up period of 14.8 years reported that the risk of stroke after weight gain following smoking cessation was 0.75 ($95\%$ CI 0.52–1.09), which was not significant; however, it is comparable to our study [24]. The results of this study are comparable to ours, because it focused on a particular subset of smoking status and included a large sample size, long follow-up duration, and stroke outcome analysis. However, no direct weight loss analysis was conducted, and the results of no weight gain, including that of the weight maintenance group, were insignificant (0.81; $95\%$ CI 0.59–1.10). Our results can also be compared with those of a Korean population cohort study that assessed total stroke, which was further categorized into ischemic and hemorrhagic stroke groups, as the outcome, and applied changes in BMI, which may be related to weight change, as the main exposure [14]. Previous studies have shown similar trends in outcomes for ischemic and hemorrhagic strokes after weight gain due to smoking cessation; however, none of the subtypes, including a total stroke risk of 0.75 ($95\%$ CI 0.57–1.00), was statistically significant [14]. Thus, the relationship could not be defined. In this study, smoking cessation reduced the risk of stroke, and this protective effect was maintained regardless of weight maintenance or increase after smoking cessation. This might explain the etiologic mechanism by which CVD exerts its protective effect, despite the deteriorating metabolic status due to weight gain after smoking cessation. Smoking, particularly nicotine, has been linked to higher levels of triglycerides, low-density lipoproteins (LDL), high-density lipoproteins (HDL), and postprandial dyslipidemia, with higher levels of insulin resistance and atherosclerosis [37,38]. This is primarily caused by tobacco smoking-induced increases in the amount of catecholamines in the blood, resulting in an increased lipolysis, free fatty acid release into the blood, and lipoprotein formation, particularly LDL, which promotes atherosclerosis [39]. Consequently, smokers have higher plasma levels of LDL and triglycerides, lower levels of HDL, and higher levels of oxidized LDL, which are preferentially taken up by macrophages, and are essential for the formation of the foam cells observed in atherosclerotic lesions [38,40]. Therefore, the benefits of quitting smoking on lipid profiles, such as LDL, triglycerides, HDL, and oxidized LDL levels, are more noticeable with an increased duration of smoking cessation [41,42]. However, no significant changes were reported in the oxidized LDL levels in smokers who gained weight, because oxidative stress increases with weight gain, contributing to the attenuation of reduced oxidized LDL levels [43]. Therefore, we inferred that oxidized LDL levels do not change, even with weight gain. This validates the finding that no modified CVD risks are associated with smoking cessation, even if weight increases. In this study, when smokers lost >5 kg, the HR for stroke was 1.18 ($95\%$ CI 1.02–1.36) in the model adjusted for age and BMI for total stroke, and was 1.06 ($95\%$ CI 0.92–1.23) in the multivariate model. Weight reduction is desirable to maintain a healthy form [44]. However, several studies have reported that weight loss increases the chance of developing various health outcomes, such as CVD, cancer, and death [45,46]. A large-scale prospective cohort study of Koreans revealed an increased risk ratio for death when the BMI is <18.5 [28]. A meta-analysis confirmed that weight loss increased the risk of CVD and death [47]. This pattern of increased risk might be because of bias caused by weight loss resulting from wasting illnesses, such as cardiovascular illness, cancer, renal disease, or chronic obstructive pulmonary disease [48]. Weight loss decreases body fat and causes the body to lose considerable nutrients and water [49]. Additionally, it causes diuresis, resulting in a major loss of magnesium, calcium, and phosphorus [50]. Thus, severe weight loss might increase health risks because of nutritional deficiencies [49]. Our study showed that despite smoking cessation, the risk of total and ischemic strokes increased considerably in the severe losers’ group, but only in the model adjusted for age and BMI. However, the difference was not significant when we controlled for factors such as physical activity, alcohol use, medical history (diabetes, hyperlipidemia, and hypertension), and family medical history (cancer and stroke). Hence, weight loss due to these correcting variables or other conditions might increase the risk of stroke during smoking cessation, in people with a weight loss of >5 kg. Considering that unintentional or intentional excessive weight loss can influence mortality, we categorized participants into five subgroups based on their weight change status, using a 5 kg change as the standard, in accordance with a previous study [16]. However, the current study has certain limitations. First, we only included males, as the small number of females that met the inclusion criteria made it difficult to perform a complete analysis based on stroke subtype and weight changes. Second, recent quitters were defined as those who quit smoking within the 4 years we examined, whereas long-term quitters were defined as those who reported past smoking in both surveys. According to the two survey periods, we assumed that the smoking cessation time for recent quitters was a maximum of 4 years, whereas it was at least 4 years for long-term quitters. However, since these were operationally defined by the follow-up interval of every 2 years, they cannot be quantified as continuous variables; hence, they are considered limitations, as they were self-reported and subject to bias. Furthermore, a distinct risk difference was anticipated, due to the gap of at least 1 year between the two groups regarding smoking cessation. Third, this study did not consider the details of smoking behavior, such as the number of cigarettes smoked, the smoking duration, or types of tobacco products. The use of novel tobacco products, such as e-cigarettes and heated tobacco, has increased in recent years; however, it is also important to emphasize the benefits of tobacco smoking cessation. This is because studies have shown that e-cigarettes lower the risk of CVD by 30–$40\%$ more than cigarette smoking [51], implying that cigarette smoking is more harmful than e-cigarettes. Fourth, we did not consider the various factors that may affect weight change. Participants with a history of cancer and stroke were excluded, but we could not consider additional underlying diseases because of limited information. Finally, since the KCPS data used in the analysis were obtained from a cohort of individuals who underwent health examinations every 2 years; the study population comprised individuals who led relatively healthy lives and those who were interested in health maintenance. Hence, the issue of selection bias could not be completely addressed [52]. Nevertheless, including a sizable sample size, wide age range, and long-term follow-up data for Asians, particularly Koreans, is noteworthy. The advantage of this study is that it examined both weight gain and loss while considering potential weight changes in individuals who had quit smoking. In addition, it is a large-scale cohort study reporting long-term prospective risk findings of total stroke, ischemic stroke, hemorrhagic stroke, and the specific subtypes ICH and SAH. Further, this study characterized short- and long-term quitter groups in accordance with previous studies [53]. This classification is necessary, as there are differences in the health outcomes of short- and long-term quitters. The risk of stroke decreases after 2 to 4 years of quitting and is similar to that of non-smokers after 5 years [8,54]. Thus, we must conduct an analysis that considers the influence of the duration after smoking cessation. According to the findings of this study, maintaining or gaining weight after smoking cessation appears to have a protective association and does not increase the risk of total stroke, hemorrhagic stroke, SAH, or ICH, except for unspecified stroke, which includes special circumstances, such as the absence of neuroimaging, non-referral by the primary care physician, death before reaching an imaging device, or refusal of additional treatment by the patient [55]. Because of worries about weight gain, half of the female smokers and a quarter of male smokers do not attempt to quit smoking [56]. Our research hypothesis was tested to determine whether weight changes affect the association between smoking cessation and the risk of stroke subtypes in Korean males. However, the results indicated that weight change did not alter the protective effect of smoking cessation on stroke. That is, the reluctance to quit tobacco use, owing to fear of weight gain, will not be beneficial for preventing stroke, and this evidence provides a basis to strongly promote smoking cessation. Finally, it is expected that this study will provide essential information for the population needing a thorough long-term follow-up prognosis for stroke, following smoking cessation and weight change. ## 5. Conclusions Weight gain following smoking cessation does not alter the health advantages of smoking cessation for stroke and its subtypes. Thus, the reluctance to quit smoking owing to fear of weight gain will not be beneficial for preventing stroke. ## References 1. Johnson C.O., Nguyen M., Roth G.A., Nichols E., Alam T., Abate D., Abd-Allah F., Abdelalim A., Abraha H.N., Abu-Rmeileh N.M.. Global, regional, and national burden of stroke, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. (2019) 18 439-458. DOI: 10.1016/S1474-4422(19)30034-1 2. 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--- title: 'Visualization of Organ-Specific Lymphatic Growth: An Efficient Approach to Labeling Molecular Markers in Cleared Tissues' authors: - Carolin Christ - Zoltán Jakus journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048960 doi: 10.3390/ijms24065075 license: CC BY 4.0 --- # Visualization of Organ-Specific Lymphatic Growth: An Efficient Approach to Labeling Molecular Markers in Cleared Tissues ## Abstract Organ-specific lymphatics are essential for the maintenance of healthy organ function and lymphatic dysfunction can lead to the development of various diseases. However, the precise role of those lymphatic structures remains unknown, mainly due to inefficient visualization techniques. Here, we present an efficient approach to visualizing organ-specific lymphatic growth. We used a modified CUBIC protocol to clear mouse organs and combined it with whole-mount immunostaining to visualize lymphatic structures. We acquired images using upright, stereo and confocal microscopy and quantified them with AngioTool, a tool for the quantification of vascular networks. Using our approach, we then characterized the organ-specific lymphatic vasculature of the Flt4kd/+ mouse model, showing symptoms of lymphatic dysfunction. Our approach enabled us to visualize the lymphatic vasculature of organs and to analyze and quantify structural changes. We detected morphologically altered lymphatic vessels in all investigated organs of Flt4kd/+ mice, including the lungs, small intestine, heart and uterus, but no lymphatic structures in the skin. Quantifications showed that these mice have fewer and dilated lymphatic vessels in the small intestine and the lungs. Our results demonstrate that our approach can be used to investigate the importance of organ-specific lymphatics under both physiological and pathophysiological conditions. ## 1. Introduction Lymphatic vessels have long been known to play a role in fluid homeostasis, immune cell trafficking and dietary lipid uptake. Recent studies have shown that lymphatics have additional organ-specific functions and that lymphatic dysfunction can lead to the development of disorders such as obesity, atherosclerosis, lung diseases, lymphedema and further metabolic diseases [1,2,3,4,5,6,7,8,9,10,11]. Lymphatic dysfunction can be caused by mutations of important regulators of lymphangiogenesis, such as Flt4 [12,13]. A mouse model showing symptoms of lymphatic dysfunction is the Flt4kd/+ strain, carrying a mutated Flt4 allele which leads to the inactivation of the tyrosine kinase activity of vascular endothelial growth factor receptor-3 (VEGFR-3) [14]. These mice develop chylous ascites, show enlarged lymphatics in the intestinal tissue, are missing lymphatic vessels in the skin and suffer from swollen feet [14], but little is known about the lymphatic vasculature of other organs, mainly due to inefficient visualization techniques. Despite obvious limitations, lymphatic vessels are still widely visualized by immunostaining of sections, although tissue sectioning is time-consuming and sections represent only small parts of a complex organ. Morphological changes of the lymphatic vasculature are difficult to capture and no information can be obtained about the length of lymphatic vessels, the number of lymphatic junctions or the total lymphatic area. Alternative visualization techniques are whole-mount immunostaining or lymphatic reporter mouse models, but the opacity of native tissue restricts light penetration and prevents sufficient imaging of the organ-specific lymphatic vasculature [15]. Tissue opacity is mainly caused by light scattering and absorption [15], both of which can be reduced by using tissue-clearing techniques. Tissue clearing aims to achieve an optically transparent tissue sample that can be used for image acquisition. Numerous protocols have been introduced, but not many protocols are suitable for the visualization of lymphatic vessels. Protocols such as Adipo-clear, CRISTAL, FASTClear, iDISCO and BABB contain hazardous organic solvents that often quench fluorescence signals, shrink organs and inevitably alter tissue morphology [16,17,18,19,20]. The CLARITY protocol utilizes an expensive solution called FocusClear™ in combination with a complex electrophoretic setup to clear organs [21]. WOBLI (whole organ blood and lymphatic vessel imaging) has been developed specifically for the visualization of lymphatic vessels, but clearing times are extremely long and the entire protocol can take up to 3 months to complete [22]. One of the most promising protocols is the CUBIC system (clear, unobstructed brain/body imaging cocktails and computational analysis), an efficient tissue-clearing protocol, developed to visualize whole mouse brains in single-cell resolution and later modified to clear whole mouse bodies [23,24,25]. CUBIC consists of two parts: the removal of lipids and pigments in the decolorization step followed by the matching of refraction indices in the RI-adjustment step, both of which result in a transparent tissue sample that can be used for imaging [23,24,25]. The latest protocols based on the original CUBIC protocol produce remarkable 3D images and use advanced quantification tools, but they require expensive light-sheet fluorescence microscopes (LSFMs) and specialized equipment as well as knowledge of machine learning and programming to quantify and analyze images [26,27,28]. The data volume of 3D images is vast and requires special computers and, more importantly, special knowledge for processing [24]. The analysis of LSFM images is time-consuming and the analysis of a single image can take hours and even days to complete [24,26]. Since these requirements discourage the majority of researchers from using tissue-clearing techniques on a regular basis, we developed an efficient approach to label molecular markers in cleared tissues in order to make tissue-clearing available to anyone. With our approach, we then characterized the organ-specific lymphatic vasculature of the Flt4kd/+ mouse to investigate lymphatic growth under pathophysiological conditions. ## 2.1. The Lymphatic Network Cannot Be Sufficiently Visualized with Conventional Visualization Methods To test whether conventional visualization techniques can be used to visualize the organ-specific lymphatic vasculature, we stained tissue sections and used a lymphatic reporter mouse model (Figure 1). We first detected the native fluorescence in various organs of Prox1GFP lymphatic reporter mice expressing the green fluorescent protein (GFP) in lymphatic endothelial cells (Figure 1a). We could detect lymphatic vessels in the ear skin, small intestine, lungs and aorta, but due to the opacity of native tissue we could only visualize superficial lymphatic vessels. We then performed paraffin-based histology on various organs of adult C57BL/6 mice and stained sections with the lymphatic marker anti-LYVE1 (Figure 1b). Anti-LYVE1-staining showed that lymphatic vessels are present in the villi intestinales and in the submucosal layer of the intestinal tissue. We could detect lymphatic vessels around the bronchi of the lungs, in the dermis of the ear skin and around the aorta. Since sections only represent a limited part of an organ, it was impossible to assess the total length or area of lymphatic vessels or to efficiently quantify the organ-specific lymphatic vasculature of those organs. ## 2.2. Optimized Tissue-Clearing Protocol Clears Mouse Organs Quickly and Effectively Our tissue-clearing protocol was 15 days long and consisted of the following main elements: a transcardial perfusion, tissue collection and post-fixation on day 0, decolorization for 5 days with Reagent 1, whole-mount immunostaining with the primary and secondary antibodies in Staining Solutions 1 and 2 and an RI-adjustment step in which the refraction indices of the cellular compartments were matched overnight in Reagent 2 (Figure 2). With this approach, we could successfully clear various mouse organs with everyday lab equipment. We tested our protocol with the ear skin, small intestine, lungs, heart with aorta, uterus with ovaries and testicles with epididymis and achieved a high level of transparency in all cases (Figure 3). This tissue-clearing protocol is based on the previously described CUBIC protocol [23,24,25]. We modified the original protocol by omitting several steps, adjusting the incubation times and adding whole-mount immunostaining after the decolorization step. ## 2.3. Our Approach Enables Us to Visualize Organ-Specific Lymphatic Growth in Various Mouse Organs To visualize organ-specific lymphatic growth in healthy animals, we tissue-cleared and stained organs of C57BL/6 wild-type and Prox1GFP lymphatic reporter mice with various immunostainings (Figure 4). We were able to visualize the organ-specific lymphatic vasculature of the ear skin of C57BL/6 mice with an anti-LYVE1-staining (Figure 4a). Lymphatics are clearly visible in the images at higher magnifications. We were also able to visualize the organ-specific lymphatic vasculature of the intestine of adult C57BL/6 mice with an anti-LYVE1-staining (Figure 4b). Images at different magnifications show that the intestinal lymphatic vasculature can be visualized in great detail, even with a stereo microscope. Our approach allows us to visualize the morphology of the lymphatic network. Images of the lumen of the small intestine show the lacteals in the villi intestinales and the lymphatics in the submucosal region. The organ-specific lymphatic vasculature of the lungs of Prox1GFP mice could be visualized with GFP staining (Figure 4c). The lymphatic network of the lungs is visible in different magnifications and the specific morphology of the lymphatic vessels is clearly visible. We then visualized the organ-specific lymphatic vasculature of the femoral arteries of C57BL/6 mice with an anti-LYVE1 antibody (Figure 4d). Images show that the arteries are surrounded by a network of lymphatic vessels. The lymphatic vasculature of the uteri of C57BL/6 mice was visualized by an anti-LYVE1 staining (Figure 4e). Lymphatic vessels form an organized network surrounding the uterine horns and the cervix. Visualization of cardiac lymphatics with an anti-GFP antibody and visualization of blood vessels with an anti-vWF (Figure 4f) or anti-αSMA antibody (Figure 4g) show the great potential of our tissue-clearing protocol. It enables us to label molecular markers with various antibodies in cleared tissues. Confocal microscopy enabled us to visualize the lymphatic vessels surrounding a coronary artery in great detail. We then visualized the lymphatic vasculature of the uteri of Prox1GFP mice with anti-GFP and anti-vWF antibodies with confocal microscopy (Figure 4h). The image shows the lymphatic vessels and blood vessels of the uterine horn in higher magnification. Anti-GFP and anti-LYVE1 staining helped us visualize the lymphatic vasculature of the testicle and epididymis of Prox1GFP mice (Figure 4i). Furthermore, we found that long-term storage of stained samples in Reagent 2 at 4 °C did not quench fluorescence signals. A strong, preserved fluorescent signal was still detectable 18 months after staining (Figure 5). ## 2.4. Lymphatic Structures Can Be Quantified with AngioTool, a User-Friendly Tool for the Analysis of Vascular Networks To simplify the quantification process, we tested AngioTool, a free computational tool which was developed for the quantitative analysis of vascular networks [29]. We quantified the lymphatic junctions and lymphatic end points of four different confocal images of the small intestines of Prox1GFP mice with AngioTool (Figure 6a). To evaluate whether the quantifications with AngioTool met our requirements, we manually quantified the lymphatic junctions and end points of the same images and compared the results (Figure 6b). To analyze the results of the individual images, we marked all images with a different color. We could not detect a significant difference between the manually quantified images and the images that were quantified with AngioTool. ## 2.5. Flt4kd/+ Mice Show Morphologically Altered Lymphatic Structures in All Investigated Organs To visualize the organ-specific lymphatic growth in Flt4kd/+ mice, we tissue-cleared and stained various organs of Flt4+/+ and Flt4kd/+ mice crossed with the Prox1GFP transgenic reporter strain (Figure 7). First, we visualized the organ-specific lymphatic growth in the skin of Flt4+/+; Prox1GFP and Flt4kd/+; Prox1GFP mice with an anti-GFP antibody and anti-von Willebrand Factor (vWF) (Figure 7a). Flt4+/+; Prox1GFP mice show a normal blood and lymphatic vessel network in the skin. Flt4kd/+; Prox1GFP mice show normal blood vessels in the skin, but lymphatic vessels could not be detected. We then visualized the lymphatic vasculature of the intestines of Flt4+/+; Prox1GFP and Flt4kd/+; Prox1GFP mice with an anti-GFP antibody and anti-LYVE1 (Figure 7b). Flt4+/+; Prox1GFP mice show normal and normal lymphatic vasculature in the small intestine. The GFP-positive nuclei of the lymphatic endothelial cells are clearly visible in the lymphatic structures. In comparison to their control littermates, Flt4kd/+; Prox1GFP mice show greatly enlarged and dilated lymphatic vessels in the intestine. Next, we visualized the lymphatic vasculature of the lungs of Flt4+/+; Prox1GFP and Flt4kd/+; Prox1GFP mice with an anti-GFP antibody (Figure 7c). Flt4kd/+; Prox1GFP mice show fewer but dilated lymphatic structures in the lungs in comparison to their littermate control mice. The lymphatic vasculature did not reach the periphery of the lungs. We then visualized the organ-specific lymphatic vasculature of the heart of Flt4+/+; Prox1GFP and Flt4kd/+; Prox1GFP mice with an anti-GFP antibody and the lymphatic marker anti-LYVE1 (Figure 7d). Flt4kd/+; Prox1GFP mice show extremely dilated lymphatic vessels in the heart. Finally, we visualized the lymphatic vasculature of the uteri of Flt4+/+; Prox1GFP and Flt4kd/+; Prox1GFP mice with an anti-GFP antibody and the smooth muscle marker anti-alpha smooth muscle actin (αSMA) (Figure 7e) and found that Flt4kd/+; Prox1GFP mice show extremely dilated and morphologically altered lymphatic vessels in the uterus. ## 2.6. Morphological Changes of the Lymphatic Vasculature Can Be Detected with Our Approach but Not with Conventional Visualization Techniques To highlight the advantages of our protocol, we quantified images of lung and small intestine sections and images of tissue-cleared lungs and intestines (Figure 8). We performed paraffin-based histology of the lungs and the small intestines of Flt4+/+; Prox1GFP and Flt4kd/+; Prox1GFP mice and stained them with an anti-GFP antibody and anti-LYVE1 (Figure 8a). We quantified the images of the lung sections manually with NIS-Elements imaging software (Figure 8b). Quantifications showed no significant differences regarding the number of lymphatics, the total lymphatic lumen and the average lymphatic lumen in the lungs between Flt4kd/+; Prox1GFP mice and their littermate control mice. We then quantified the images of the small intestine sections manually with NIS-Elements (Figure 8c). Quantifications showed a significant decrease in the number of intestinal lymphatics in Flt4kd/+; Prox1GFP mice. Total lymphatic lumen and the average lymphatic lumen were significantly increased in the small intestines of Flt4kd/+; Prox1GFP mice in comparison to the intestines of their littermate control mice. To highlight the advantages of our approach, we tissue-cleared and stained lungs and small intestines of Flt4+/+; Prox1GFP and Flt4kd/+; Prox1GFP mice and acquired confocal images of the samples (Figure 8d). Quantifications of the tissue-cleared lungs showed a decreased total lymphatic area, decreased total length of lymphatic vessels, fewer lymphatic junctions and fewer lymphatic end points in the lungs of Flt4kd/+; Prox1GFP mice in comparison to their controls, but a significant increase in the lymphatic diameter (Figure 8e). Our results suggest that Flt4kd/+; Prox1GFP mice have significantly fewer but dilated lymphatic vessels in the lungs. Quantifications of the tissue-cleared small intestines showed no difference in the total lymphatic area but a significant decrease in the total length of lymphatic vessels, fewer lymphatic junctions and fewer lymphatic end points in the small intestines of Flt4kd/+; Prox1GFP mice in comparison to their controls, but a significant increase in the lymphatic diameter (Figure 8f). These results suggest that Flt4kd/+; Prox1GFP mice have fewer but dilated lymphatic vessels in the small intestine. The results show that morphological changes in the lymphatic vasculature, especially changes in lymphatic parameters such as total lymphatic area, total length of lymphatic vessels, lymphatic junctions and end points can be detected effectively with our approach. ## 3. Discussion In this study, we presented an efficient approach to visualize, analyze and quantify the organ-specific growth of lymphatic vessels. Conventional visualization techniques such as paraffin sections, reporter mouse models and whole-mount staining are not sufficient to visualize the lymphatic vasculature of organs due to the opacity of native tissue (Figure 1) [15]. Tissue-clearing techniques address this problem by clearing pigments and matching RI-indices, creating optically transparent tissue samples that can be used for imaging, but many of the protocols have limitations that make them unsuitable for the visualization of lymphatic vessels [16,18,19,21,22,26,30,31,32,33,34]. We decided to base our protocol on a non-toxic and efficient tissue-clearing protocol named CUBIC [23,24,25]. We modified the original protocol by omitting steps, changing incubation times and combining it with whole-mount immunostaining to be independent from reporter mouse models (Figure 2). Our protocol enabled us to sufficiently clear various mouse organs in a reasonable time and without any visible shrinkage or damage to the tissue (Figure 3). We showed that our protocol works with different fluorescent markers that allow us to visualize organ-specific lymphatic structures (Figure 4). We demonstrated that it can be used for double-staining with different antibody combinations and is suitable for the investigation of other structures of interest, such as blood vasculature (Figure 4f–h). With our approach, we then aimed to visualize and quantify the organ-specific lymphatic vasculature of the Flt4kd/+ mouse, a mouse model showing symptoms of lymphatic dysfunction. The model was described nearly 20 years ago, but, aside from the lack of lymphatics in the skin and dilated intestinal lymphatics, little is known about the organ-specific lymphatic vasculature of this model [14]. We could confirm that these mice lack lymphatic vessels in the skin and that they have enlarged lymphatic vessels in the small intestine, as previously described [14]. Furthermore, we found that these mice have morphologically altered lymphatic vessels in the lungs, heart and uterus that have not yet been described (Figure 7). The fluorescence signals of our samples remain stable for more than 18 months. Long-term stability of staining allows flexibility regarding the imaging of samples and even allows re-imaging of a sample after a long period of time if the focus of a study has changed (Figure 5). In comparison to other CUBIC protocols [23,24,25,26], we could acquire images of great quality by stereo and confocal microscopy and did not need an expensive and complex LSFM (Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8). We focused on simplifying analysis and quantified our images using AngioTool (Figure 6 and Figure 8). AngioTool is a free and user-friendly computational tool specifically developed for the quantification of vascular structures [29]. It quantifies images in seconds by simply uploading them into the program. It allowed us to adjust parameters such as vessel diameter, intensity and particle size and to quantify vessel-relevant parameters such as total vessel area, total vessel length, number of vessel junctions and end points. Results generated with AngioTool showed no significant differences when compared to manual quantifications; therefore, we propose that AngioTool represents a suitable tool for quantifying images of lymphatic vasculature (Figure 6). We then quantified sections of the lungs and small intestines of Flt4kd/+ mice and compared them with quantifications of tissue-cleared lungs and small intestines (Figure 8). Based on the sections, we could quantify the number of lymphatic vessels, the total lymphatic lumen and the average lymphatic lumen. Results of the small intestine sections showed that lymphatic vessels are dilated in Flt4kd/+ animals; results of the lung sections on the other hand were inconclusive. Based on the tissue-cleared samples, we were able to quantify more parameters, including lymphatic junctions, lymphatic end points and the total length of lymphatic vessels. Quantifications revealed that lungs and small intestines of Flt4kd/+ animals have fewer lymphatic junctions and end points and that the total lymphatic area and length of lymphatic vessels is greatly reduced. The significantly increased lymphatic diameter shows that lymphatic vessels in the lungs and the small intestine are dilated, a sign of lymphatic malfunction. We showed that tissue-cleared samples allow a much better assessment of the lymphatic network of an organ than immunostaining of sections. Our protocol enabled us to visualize and quantify the differences between the organ-specific lymphatic vasculature of Flt4kd/+ mice and their healthy littermate controls. In this study, we demonstrated that our efficient protocol enabled us to visualize the lymphatic network of the studied organs, to detect differences and morphological alterations of lymphatic structures in mouse models and to quantify those differences. Many tissue-clearing protocols aim for high-resolution 3D-images of tissue-cleared organs but require expensive LSFM and previous knowledge of analyzing 3D-image data that are not readily available in every laboratory and therefore prevent the majority of researchers from using tissue-clearing techniques [18,21,26,30]. Here, we could provide an efficient protocol without the need for any special equipment or knowledge, making tissue-clearing possible for anyone. We showed that Flt4kd/+ mice have morphologically altered organ-specific lymphatic vessels, which suggests that lymphatic vessels are important for the maintenance of healthy organ function. Further functional studies could help clarify the role of organ-specific lymphatic vessels and the impact of morphologically altered lymphatic vessels in the Flt4kd/+ organs on the lymphedema phenotype. ## 4.1. Mouse Models In this study, we used 3- to 5-month-old male and female C57BL/6 and Prox1GFP BAC transgenic lymphatic reporter mice to visualize lymphatic vasculature [35]. Prox1GFP mice were maintained on a C57BL/6 genetic background in heterozygous form and genotyped by a transgene specific PCR using 5′ -GAT GTG CCA TAA ATC CCA GAG CCT AT−3′ forward and 5′-GGT CGG GGT AGC GGC TGA A−3′ reverse primers. Additionally, we used 3- to 5-month-old Flt4kd/+ mice, a previously described model for primary lymphedema, and their littermate control mice [14]. Mice carrying the kinase-dead Flt4 allele (MRC Harwell, UK) were maintained on a NMRI background [14]. The Flt4 point mutant allele was genotyped using 5′-CTGGCTGAGTCCCTAACTCG-3′ forward and 5’-CGGGGTCTTTGTAGATGTCC-3′ reverse primers, followed by a restriction enzyme digestion with BglII, as previously described [9]. Furthermore, Flt4+/+ and Flt4kd/+ mice were crossed with the Prox1GFP reporter strain, resulting in Flt4+/+; Prox1GFP and Flt4kd/+; Prox1GFP animals. All animals were housed under a $\frac{12}{12}$ h light/dark cycle with unrestricted access to food and water. All procedures were carried out according to the Animal Experimentation Review Board of Semmelweis University and the Government Office for Pest County, Hungary. ## 4.2. Paraffin-Based Histology and Immunostaining of Sections Mice were deeply anaesthetized by an intraperitoneal injection of $2.5\%$ 2,2,2-Tribromoethanol (Sigma-Aldrich, T48402, St. Louis, MO, USA) and transcardially perfused with 10mL ice-cold phosphate-buffered saline (PBS)-Heparin (Teva Pharmaceuticals, Tel Aviv, Israel) (5000 IU/mL) followed by 10mL freshly prepared $4\%$ paraformaldehyde (PFA) (Sigma-Aldrich, P6148, St. Louis, MO, USA). Tissue samples were collected and fixed overnight in $4\%$ PFA at 4 °C, washed with PBS, dehydrated and embedded in paraffin using an embedding station (Leica, EG1150H, Wetzlar, Germany). A microtome (Thermo Fisher Scientific, HM340E, Waltham, MA, USA) was used to generate 7μm sections which were stained with the lymphatic marker goat-anti-LYVE1 (Bio-Techne, AF2125, Minneapolis, MN, USA) in a dilution of 1:100 and anti-goat secondary antibody conjugated to Alexa Fluor 488 (Thermo Fisher Scientific, A11055, Waltham, MA, USA) in a dilution of 1:250. Nuclear staining with 4′,6-Diamidino-2-phenylidole (DAPI) (Vector Laboratories, Inc., H-1200, Burlingame, CA, USA) helped to visualize the gross morphology of the section. Images were taken with an upright microscope (Nikon Instruments, ECLIPSE Ni-U, Tokyo, Japan) connected to a camera (Nikon Instruments, DS-Ri2, Tokyo, Japan). ## 4.3. Detection and Imaging of Native Fluorescence Mice were deeply anaesthetized by an intraperitoneal injection of $2.5\%$ 2,2,2-Tribromoethanol followed by cardiac perfusion with 10 mL ice-cold PBS-Heparin (5000 IU/mL). Tissue samples were collected, washed with PBS and visualized with a stereo microscope (Nikon Instruments, SMZ25, Tokyo, Japan) connected to a camera (Nikon Instruments, DS-Ri2, Tokyo, Japan). ## 4.4.1. Cardiac Perfusion and Tissue Collection Mice were deeply anaesthetized by an intraperitoneal injection of $2.5\%$ 2,2,2-Tribromoethanol on day 0 and transcardially perfused with 10 mL ice-cold PBS-Heparin (5000 IU/mL) followed by 10 mL freshly prepared $2\%$ PFA. Tissue samples were collected and fixed overnight in $2\%$ PFA at 4 °C. ## 4.4.2. Decolorization and Delipidation Our tissue-clearing protocol is based on the previously published CUBIC protocol [23,24,25]. On day 1, the fixed samples were washed with PBS and immersed in Reagent 1 (25 wt% urea (Sigma-Aldrich, U5378, St. Louis, MO, USA), 25 wt% N, N, N′, N′-tetrakis (2-hydroxypropyl) ethylenediamine (Sigma-Aldrich, 122262, St. Louis, MO, USA) and 15 wt% Triton™ X-100 (Sigma-Aldrich, X100, St. Louis, MO, USA) in dest. H2O). Incubation in Reagent 1 lasted for 5 days at 37 °C and 80 rpm. Reagent 1 was changed daily. On day 6, the transparent tissues were washed twice with PBS and rehydrated overnight in PBS, room temperature, 80 rpm. ## 4.4.3. Whole-Mount Immunostaining On day 7, the rehydrated tissue samples were stained for 4 days with the primary antibody(s) in Staining Solution 1 (serum ($10\%$), sodium azide ($0.2\%$), Tween® 20 (Sigma-Aldrich, P1379, St. Louis, MO, USA) ($0.1\%$) in PBS with primary antibody(s) in the following dilutions: goat-anti-LYVE1 (Bio-Techne, AF2125, Minneapolis, MN, USA) in a dilution of 1:650, rabbit-anti-GFP (LifeTechnologies, A11122, Carlsbad, CA, USA) in a dilution of 1:500 and mouse-anti-αSMA (Sigma-Aldrich, A5228, St. Louis, MO, USA) in a dilution of 1:500) at 37 °C, 80 rpm. On day 11, the samples were washed with PBS-Tween® 20 ($0.1\%$) for 2 h at room temperature, 80 rpm, and then stained for 3 days with the secondary antibody(s) in Staining Solution 2 (serum ($2\%$), sodium azide ($0.2\%$), PBS-Tween® 20 ($0.1\%$) in PBS with secondary antibody(s) in the following dilutions: donkey-anti-goat Alexa Fluor 488 (Thermo Fisher Scientific, A11055, Waltham, MA, USA) in a dilution of 1:3000, donkey-anti-goat Alexa Fluor 568 (Thermo Fisher Scientific, A11057, Waltham, MA, USA) in a dilution of 1:3000, donkey-anti-rabbit Alexa Fluor 488 (Thermo Fisher Scientific, A21206, Waltham, MA, USA) in a dilution of 1:3000 and donkey-anti-mouse Alexa Fluor 568 (Thermo Fisher Scientific, A10037, Waltham, MA, USA) in a dilution of 1:3000) at 37 °C, 80 rpm (tubes were wrapped in tinfoil to prevent degradation of the fluorophores during the incubation process). On day 14, stained tissues were washed for 2 h with PBS-Tween® 20 ($0.1\%$) at room temperature, 80 rpm. Control stainings have been performed for all antibodies used in this study. ## 4.4.4. Adjustment of the Refraction Indices (RI-Adjustment) The stained samples were then incubated overnight in Reagent 2 (50 wt% sucrose (Sigma-Aldrich, S7903, St. Louis, MO, USA), 25 wt% urea, 10 wt% triethanolamine (Sigma-Aldrich, 90279, St. Louis, MO, USA), 0.1 wt% Triton™ X-100 in dest. H2O) at 37 °C, 80 rpm. On day 15, samples were ready for imaging and were transferred to 4 °C for long-term storage. ## 4.4.5. Microscopic Imaging and Processing On day 15, samples were imaged with a stereo microscope connected to a camera or a confocal microscope (Nikon Instruments, A1 HD25, Tokyo, Japan) connected to a confocal scanner unit (Yokogawa Electric Corporation, CSU-W1, Tokyo, Japan). Images were processed and analyzed using NIS-Elements imaging software (Nikon Instruments, version BR 4.60.00). ## 4.5. Manual Quantification of Vascular Structures Manual quantifications were performed using NIS-Elements. For the quantification of sections, lymphatic number, total lymphatic lumen and average lymphatic lumen of 5 fields of view (20× magnification) were quantified per mouse. For the quantification of the lymphatic diameter of tissue-cleared lungs and intestines, a minimum of 40 lymphatic vessels (10× magnification) were measured per mouse. ## 4.6. Quantification of Vascular Structures with AngioTool Total lymphatic area, total length of lymphatic vessels, lymphatic junctions and lymphatic end points were quantified with AngioTool, a free computational tool that has been developed for the quantitative analysis of vascular networks [29]. After uploading the images to AngioTool, we adjusted parameters such as the vessel diameter and intensity and removed small particles from the calculation. All images were quantified using the same parameters. ## 4.7. Data Presentation and Statistical Analysis Representative images of the experiments are shown. 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--- title: Does Pain Acceptance Contribute to Improved Functionality through Walking in Women with Fibromyalgia? Looking at Depressive Comorbidity authors: - Cecilia Peñacoba - Carmen Ecija - Lorena Gutiérrez - Patricia Catalá journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048968 doi: 10.3390/ijerph20065005 license: CC BY 4.0 --- # Does Pain Acceptance Contribute to Improved Functionality through Walking in Women with Fibromyalgia? Looking at Depressive Comorbidity ## Abstract In the last decade, research has pointed to physical exercise as an effective treatment in fibromyalgia patients. Some studies have highlighted the role of acceptance and commitment therapy in optimizing the benefits of exercise in patients. However, given the high comorbidity in fibromyalgia, it is necessary to value its possible influence on the effect of certain variables, such as acceptance, on the benefits of treatments, such as physical exercise. Our aim is to test the role of acceptance in the benefits of walking over functional limitation, further assessing whether this model is equally valid, considering depressive symptomatology as an additional differential diagnosis. A cross-sectional study with a convenience sample through contacting Spanish fibromyalgia associations was carried out. A total of 231 women with fibromyalgia (mean age 56.91 years) participated in the study. Data were analyzed with the Process program (Model 4, Model 58, Model 7). The results highlight the role of acceptance as a mediator between walking and functional limitation (B = −1.86, SE = 0.93, $95\%$ CI = [−3.83, −0.15]). This model, when depression is incorporated as a moderator, is significant only in patients without depression, revealing the need for personalized treatments in fibromyalgia, considering their most prevalent comorbidity. ## 1. Introduction Recent studies have considered fibromyalgia (FM) as a central sensitization syndrome, since they suggest that the predominant pathogenic mechanism is the alteration of pain regulation at the brain level [1]. Traditionally, pain management paradigms have aimed on one hand to decrease pain levels and on the other to increase control over pain responses [2]. In contrast, the effects of these interventions have not always been as desired [3]. In recent times, health research has recommended multidisciplinary treatments for fibromyalgia patients, which combine psycho-education, recommendations to carry out a healthy lifestyle, and non-pharmacological interventions [4]. Physical exercise together with psychological therapy is considered the most effective treatment for fibromyalgia [3]. Within the different modalities of aerobic physical exercise, it has been proven that regular walking offers multiple benefits in these patients [5,6]. Some of the most notable benefits are the reduction of the impact of the disease on daily life, fatigue or improvement in functional limitation, and pain control [7,8]. However, walking with the aim of reducing pain levels can be counterproductive if the way of coping with the activity is not adequate (e.g., pain avoidance) [9]. When pain is interpreted as a threat, it leads to kinesophobia, defined as excessive fear of movement or fear of physical activity, which can induce disability [10]. Despite the positive effects of walking as a form of physical exercise, the results are not always as desired, given the low adherence that patients with fibromyalgia have to this healthy behavior [11]. For this reason, previous researchers have focused their interest on analyzing the risk factors for non-adherence, the so-called inhibitors of walking behavior. In this sense, the symptoms of fibromyalgia, pain, and fatigue are shown as the most frequent inhibitors, causing the perception and coping that the patient makes of them to be especially relevant [12]. Pain catastrophizing has been analyzed within motivational approaches, which understand it as the result of the conflict of goals: goals aimed at pain control (basically through activity avoidance behaviors) and goals with a vital purpose for patients (normally associated with carrying out an activity) [13]. There is a large amount of research that has focused on the role of catastrophizing as a maladaptive filter for interpreting symptoms and their future consequences. Fibromyalgia patients with high catastrophizing scores perform less physical activity and present greater pain and fatigue and greater functional limitation [14,15]. Therefore, effective coping is the key to control the symptoms and improve the quality of life of these patients, especially when a treatment potentially associated with experiencing symptoms such as pain and fatigue (i.e., walking) is prescribed. In this sense, compared to the abundant research on catastrophizing as maladaptive coping, as we have pointed out, research on adaptive strategies is considerably less. The latest research shows that the benefits of pain acceptance go beyond standard coping strategies [2]. In a recent study comparing both pain catastrophizing and acceptance in fibromyalgia and obese patients, an association was found between lower levels of activity (both self-reported and through the 6-min walking test (6MWT)) with higher pain catastrophizing and lower acceptance scores [16]. A current study noted that pain acceptance explained six times the variance in seven measures of physical and psychosocial functioning compared to five widely studied coping strategies [17]. The acceptance of pain not only involves learning to live with ongoing pain without trying to avoid, change, or reduce it, but also involves making conscious decisions about the prescribed treatment (i.e., walking) [18,19]. In addition, the acceptance process includes recognizing that pain is an integral part of fibromyalgia and that, despite there being no cure, one can learn to manage pain effectively. There is evidence that people who have a greater acceptance of pain are free to pay attention to it frequently. This fact allows them, on the one hand, to have a greater capacity, motivation, and commitment to carry out an active lifestyle and, on the other, to maintain a positive vision of life [2]. Likewise, previous studies have pointed to this ability as a powerful correlate of positive physical, psychological, and social adjustment to chronic pain [20,21,22]. There are data that confirm that a greater acceptance is related to less intensity of pain, symptoms, and disability and to better physical functionality [3,23,24]. Despite this, the role of pain acceptance in fibromyalgia patients remains controversial. The challenge of the most current research lies in revealing what factors are influencing the results [3]. Knowing the heterogeneity among patients with fibromyalgia, due in large part to the complexity of the disorder and the high associated comorbidity [25], it is possible that the mechanisms involved do not work the same in all patients [26]. In fact, different studies have pointed out the need to identify subgroups within the heterogeneity of patients with fibromyalgia in order to rationalize and clearly define the most appropriate interventions [27,28]. Given the broad comorbidity associated with fibromyalgia, it is not surprising that the symptoms themselves formed the basis for establishing subgroups [29]. Thus, by way of example, Pérez-Aranda et al. [ 29] use the FIQR as a general measure of the impact of the disease, establishing four differential subgroups that allow the detection of differences for economic costs and for different clinical outcomes (i.e., anxiety, depression, stress, cognitive impairment, inflammatory markers’ levels, gray matter volumes). Vicent et al. [ 30], using the usual symptomatology in patients with fibromyalgia (i.e., pain, fatigue, function, sleep disturbance, depression, dyscognition, anxiety, stiffness) as a criterion, found four clusters that classify the sample in severity levels: Clusters 1 and 4 refer to the lowest and highest average levels across all symptoms, respectively, and clusters 2 and 3 reflect moderate symptoms levels, the latter differing in the severity of the depressive and anxiety symptoms. Increasing data support the comorbidity of fibromyalgia and psychiatric conditions [31,32]. A recent review indicates that depression/major depressive disorder is considered the most prevalent psychiatric comorbidity in this population, noting that more than half of the patients with fibromyalgia (weighted prevalence up to $63\%$) have received this diagnosis throughout their lives. However, anxiety-related disorders were much less common [32]. The data from this review point to the need to consider depression as a comorbid disorder that is highly present in patients with fibromyalgia, to increase the generalization and applicability of the research results [32]. In order to shed some light on the influence of depression in patients with fibromyalgia and in particular on the processes involved in adherence to walking, the present research aims first to evaluate the mediating role of pain acceptance as a key variable in the benefits of walking behavior on functional limitation, and second to verify the validity of this model when considering depression as a comorbidity. The proposed model is really based on two conceptual approaches: (a) On the one hand, in relation to pain acceptance as a mediator, the application of acceptance and commitment therapy (ACT) in patients with chronic pain shows the positive results of the same in the acceptance of their chronic condition and in the increase in functional autonomy [33]. In this context, acceptance is a nuclear variable of the model. The process of acceptance of chronic pain is associated with a lower impact of the disease, including less disability [34,35]. Specifically, changes in acceptance, pain-related anxiety, compensation strategies, and pain interference in walking ability have been found after the application of ACT in chronic pain [36]. ( b) On the other hand, proposing depression as a moderating variable in the model is due to the heterogeneity previously described in fibromyalgia [27] and the need to establish profiles or subgroups to respond to this reality and design more personalized treatments. Given the prevalence of depressive comorbidity in patients with fibromyalgia [32], the analysis of this variable is proposed, establishing subgroups and incorporating depression as a moderating variable. Taking into account the existing literature, it is hypothesized that acceptance benefits the relationship between walking behavior and functional limitation and that this mechanism varies depending on the presence or absence of depression. ## 2.1. Participants A total of 268 participants agreed to participate in the study. All of them met the following inclusion criteria: being a woman, being older than 18 years, and having received a diagnosis of fibromyalgia by rheumatologists or primary care physicians. All of them were diagnosed according to the American College of Rheumatology criteria [37]. As exclusion criteria, the existence of concomitant rheumatic disorders and the existence of psychotic disorders were taken into account. A minimum n of 200 was established, following the criteria established for the regression analyses [38], and the recommendations for the analyzes moderation using the PROCESS tool in SPSS [39]. Finally, effective responses were obtained from 231 patients (22 patients did not attend the scheduled evaluation appointment, 6 patients did not sign the informed consent, and the questionnaires of 9 patients contained a large amount of missing data, so it was decided to eliminate them from the study). All the women were recruited from different mutual aid associations in Spain. In Spain, most patients with fibromyalgia belong to an association [40]. Mutual aid associations represent significant savings for both patients and the health system, which is why it is a very common practice [33]. A psychologist from the research team went to the different associations to deliver an evaluation protocol to the patients. Completing this protocol lasted between 20 and 30 min. Ethical principles for research with human participants were followed for all evaluation procedures. The University Ethics Committee (Universidad Rey Juan Carlos, Reference number: PI$\frac{17}{00858}$) approved this study. ## 2.2. Measures Walking: We used an ad hoc dichotomous question (no = 0/yes = 1) to test whether participants engaged in the behavior of walking for physical exercise. Specifically, one of the walking patterns usually recommended for patients with fibromyalgia was evaluated: “walk 2 to 4 days a week, a minimum of 30 min a day, in 15–20 min shifts, with a small rest between shifts for a minimum of six consecutive weeks in order to exercise” [41]. Acceptance: The Chronic Pain Acceptance Questionnaire (CPAQ) [34] is a self-report questionnaire designed to assess acceptance of chronic pain. In this study, the validation into Spanish was used [42]. The CPAQ questionnaire is made up of 20 items that present a bifactorial structure: pain willingness (11 items) and activities engagement (9 items). The response scale used is 7 Likert-type points, where 0 is never true and 6 is always true. The total score of the scale ranges from 0 to 66. In this study we used the pain willingness subscale. Higher scores mean a high acceptance of pain. The validity and reliability of this instrument has been demonstrated [42]. In our sample, Cronbach’s alpha for the pain willingness subscale was 0.84. Functional limitation: The Revised Fibromyalgia Impact Questionnaire (FIQ-R) [43] is a 21-item self-report questionnaire that assesses three factors: physical function, general impact, and symptoms. For this study, the functional limitation subscale of the Spanish version of FIQ-R [44] was used, which evaluates the degree of difficulty experienced by the patients when performing a series of physical exercises in activities of daily living (for example, “going shopping” or “climbing stairs”). A Likert-type response format of 11 points ranging from 0 to 10 is used. To obtain the score for this subscale, the first nine items are added and divided by three. Higher scores indicate less functionality. The subscale has obtained good reliability and validity indices in previous studies [45]. Cronbach’s alpha in the present study was 0.88. Depression: The medical history was examined to verify a diagnosis of depression by a psychiatric professional. In addition, this diagnosis was verified through the administration of the Spanish version of the Hospital Anxiety and Depression Scale (HADS) [46,47], depression dimension, establishing a cut-off point score of 12 or higher in this population [48]. Pain intensity: The Brief Pain Inventory is an instrument used to assess pain intensity [49]. In this study, the average of the four items (BPI items) that make up the questionnaire (maximum, minimum, and general pain intensity during the last 7 days and current pain intensity) was used. The response scale ranges from 0 (no pain) to 10 (the worst pain imaginable). This procedure for measuring pain intensity has been widely used in the pain literature [50]. In this study, the internal consistency of this scale was high (α = 0.86). Sociodemographic and clinical data: Questions related to age, marital status, educational level, employment status, medication prescribed, and time since diagnosis of the participants were included. ## 2.3. Data Analysis For data analysis, the statistical package SPSS 22 [51] was used. The bivariate associations between the variables under study (walking, depression, acceptance, and functional limitation) were analyzed and then a series of multivariate regressions were calculated through the macro PROCESS [52]. Specifically, a mediation analysis was performed with Model 4 and a moderate mediation analysis with Model 58. Acceptance was used as a mediator, depression as a moderator, walking as an independent variable, and functional limitation as the outcome. Pain intensity was included as a covariate for all models. Statistical significance was set at an alpha level of 0.05. The PROCESS macro uses the ordinary least squares (OLS) analysis to calculate the mediation and moderate mediation effects, and the bootstrap is used to calculate the confidence intervals (CI). Specifically, bias-corrected bootstrap CIs based on 5000 bootstrap samples with a $95\%$ confidence level are used. When the confidence intervals do not include zero, the effect is considered significant. Non-centered variables were used in the post hoc analyses in order to facilitate the interpretation of the results. ## 3.1. Sample Characteristics The age of the participants ranged from 19 to 78 years (mean = 53.89; SD = 9.25). Seventy-five percent of the women were married or in a stable relationship. The remaining $25\%$ were distributed in the following categories: separated or divorced ($12\%$), single ($8\%$), and widows ($5\%$). Most of the participants had completed upper secondary education ($46.3\%$). Similar percentages were found for university studies ($24\%$) and for primary education ($27\%$). Only $2.7\%$ had not completed official studies, only knowing how to read and write. Regarding the medication they took on a daily basis, 16 women took antidepressants ($21\%$), 22 women took anti-inflammatory drugs ($24.3\%$), and 38 women took other types of medication to regulate their glucose levels, blood pressure, or allergic problems ($54.7\%$). The mean time since diagnosis of fibromyalgia was 9.85 years (SD = 8.49; range 1–46 years). Mean pain intensity was 7.05 (SD = 1.49; range 1–10). ## 3.2. Descriptive Analysis and Correlations Descriptive data for walking, depression, acceptance, and functional limitation are shown in Table 1. Regarding the correlations between the variables under study, acceptance was negatively correlated with functional limitation ($p \leq 0.001$). In addition, significant differences were observed in patients with depression versus without depression in acceptance ($t = 6.93$, $p \leq 0.001$) and functional limitation (t = −4.84, $p \leq 0.001$). Patients without depression obtained higher scores in acceptance and lower scores in functional limitation. Similarly, significant differences were observed in patients who walked versus those who did not walk in acceptance (t = −2.14, $$p \leq 0.036$$) and functional limitation ($t = 3.53$, $p \leq 0.001$). The patients who walked obtained higher scores in acceptance and lower scores in functional limitation. Finally, significant differences were observed in patients with depression versus without depression and those who walked versus did not walk (X2 = 8.78, $$p \leq 0.002$$). Specifically, $65.9\%$ of patients without depression walk, while $46.1\%$ of patients with depression do so. ## 3.3. Mediation Model of the Relationship between Walking and Functional Limitation with Acceptance as a Mediator Figure 1 shows the results of the mediation analysis controlling for the effects of pain intensity. Acceptance fully mediates the effect of walking on functional limitation. The total model effect was significant (B = −1.86, SE = 0.93, $95\%$ CI = [−3.83, −0.15]). In addition, there was a direct effect of walking on functional limitation (B = −5.87, SE = 2.09, t = −2.80, $95\%$ CI = [−10.00, −1.74], $$p \leq 0.05$$). The mediation model explains a total variance of $46\%$ ($F = 64.20$, $p \leq 0.001$). ## 3.4. Moderate Mediation Model Table 2 shows results of the moderate mediation model (Model 58). This model (see Figure 2) includes a mediation process where acceptance is the mediating variable between walking (predictor) and functional limitation (outcome). Additionally, the model includes the possible moderating role of depression between walking and acceptance on the one hand, and between acceptance and functional limitation on the other hand. Pain is entered as a covariate in the model. The results indicate a significant indirect effect only for the interaction between walking and depression, as the interaction between acceptance and depression was not significant. That is, we follow this model with a simpler model (Model 7). The indirect effect of walking that predicts functional limitation through acceptance was conditioned by the presence of depression (moderate mediation index = 2.82, SE = 1.47, $95\%$ CI [0.157, 5.978]). The results reveal that depression conditions the contribution of walking to acceptance. Specifically, this relationship (walking to acceptance) was significant in patients without a diagnosis of depression (B = −2.05, SE = 1.11, $95\%$ CI [−4.53, −0.22]) (Table 3). This indicates that the positive effect of walking on functional limitation through acceptance is favored when patients do not present depression. The proposed model contributes to the explanation of $49\%$ of the variance of the functional limitation. ## 4. Discussion This research analyzed the mediating role of acceptance in the relationship between walking behavior and functional limitation and tried to verify if the relationship established between walking behavior and functional limitation through acceptance is maintained when it takes into account whether or not patients present depression. Considering the first aim, the results show that the effect of walking on functional limitation is completely mediated by acceptance. According to acceptance and commitment therapy, acceptance not only implies accepting pain passively, but also makes it easier to carry out valuable activities for the person without the need to avoid or control pain [53]. It has been shown that patients who accept their experiences are more willing to undertake efforts to relieve their pain [54] such as going for regular walks. In this context, it has also been pointed out that the acceptance of pain allows patients to better adapt to suffering from chronic pain [55] and to maintain adaptive daily functioning while continuing to experience pain [34]. Taking into account what is stated here and the model proposed, it could be said that patients who manage to accept pain as an integral part of fibromyalgia may perceive an improvement in their ability to carry out daily activities by incorporating regular physical activity into their lifestyle. Therefore, for treatment based on physical activity to have a beneficial impact on the health of these patients, it must be combined with psychological techniques for pain acceptance. These results could even point to the need to consider acceptance as a differentiating feature when establishing subgroups in patients with fibromyalgia. As we have previously pointed out, most of the preceding research establishes subgroups of patients based on symptomatology [39]. However, some research also includes personality traits and cognitive–emotional variables as factors of interest when establishing differential profiles. Specifically, pain catastrophism is included in some of these clusters [56,57]. To our knowledge, the role of acceptance has not been included when establishing differential profiles within patients with fibromyalgia. Likewise, it would be interesting to assess the role of kinesophobia, since the fear of movement could also influence the ability to walk [10]. In line with the second aim, when performing the moderate mediation model, the findings showed that the presence of depression modifies the effect of walking on acceptance. Specifically, the patients perceive the benefits of walking on functional limitation through the acceptance of pain only when they do not present a diagnosis of depression. Previous studies had already reported the beneficial role of positive health factors in the symptoms of fibromyalgia [58,59,60]. These results show the need to identify subgroups of patients with fibromyalgia in order to carry out adequate treatments. Previous studies already mentioned the usefulness of establishing subsets based on symptomatology and biopsychosocial factors [57,61,62]. In this line, different subgroups derived from exploratory cluster analyses of a wide range of variables have been published [29,62,63]. For the most part, studies have established subgroups based on clinical characteristics [29,63,64]. Studies that analyze having depression as a differential diagnosis in fibromyalgia have pointed out the influence that this has on the health outcomes of these patients. Specifically, it has been observed that fibromyalgia patients with depression have lower scores on positive affect, higher scores on pain vigilance and negative affect, and slower reaction times than FM patients with low depression and pain-free controls [65]. In studies in which anxiety and depression are studied jointly as comorbid symptoms in FM patients, compared with fibromyalgia patients without those diagnoses, it has been verified that the main effects of anxiety and depression were significant for the index scores on activity-related discomfort, subjective work capacity, and quality of life [66]. However, it is important to note that in the latter case, it would be interesting to study depression independently, since different studies have reported that anxiety and depression are independently associated with the severity of pain symptoms in fibromyalgia [67,68], and that the proportions of comorbid anxiety and depression are different. It has been proven that the proportion of patients with fibromyalgia and comorbid depression is much higher than with anxiety [68]. However, to our knowledge, until now, the mediating effect of acceptance in the relationship between walking behavior and limitation in fibromyalgia patients with and without depression had not been evaluated. Despite having results on the role of acceptance in adherence to the behavior of walking and its benefits [69], in view of our results it seems that this role of acceptance does not work in the same way in patients with depressive comorbidity. A recent review that analyzes the effectiveness of the interventions in patients with fibromyalgia of the therapy of acceptance commitment and full attention already indicated that in patients with depression, the effects are small [24]. It is possible that the consequences of suffering from depression are influencing these results. Patients with this disorder are known to experience anhedonia, negative thoughts, lack of motivation, exhaustion, hopelessness, or a feeling that things will never get better, which can make it more difficult for a person to find ways to manage pain. In addition, it has been proven that depression can exacerbate physical pain and cause fatigue and a feeling of exhaustion, which could make it even more difficult for these patients to find benefits in carrying out the behavior of walking [70,71]. These findings have important practical implications as they suggest that implementation-oriented treatments for walking in fibromyalgia do not have the same effect in patients with depression. In this last case, a priori, walking would not have a positive impact on the functionality of patients with fibromyalgia. Thus, working in the first place to reduce the levels of depression in these patients would be the most appropriate treatment. These results are consistent with previous studies showing that the benefits of leading an active lifestyle vary depending on the characteristics of the population examined [12,72,73]. Our results confirm that, before promoting the pre-registration of aerobic physical exercise (e.g., walking) in order to increase the functionality of these patients, it would be necessary to carry out a previous analysis of the levels of depression. One of the limitations of this study was its design; given its cross-sectional nature it is not possible to establish cause–effect relationships. In addition, the population was limited to women only, which makes it difficult to generalize the results, despite women being the predominant gender in the diagnosis of fibromyalgia. Therefore, it is considered necessary to carry out more research in other populations with chronic pain and men. Finally, self-report questionnaires were used for most measures. Despite being a common problem in this area in the existing literature [74], this could affect the results. ## 5. Conclusions In conclusion, the findings presented here are relevant both for the field of research and for the clinical setting. Specifically, the results point to acceptance as a relevant psychological mechanism to perceive the benefits of carrying out an active lifestyle (e.g., going for a walk) on functional limitation. However, considering depression as a comorbidity, the validity of this model varies, being significant in patients without depressive comorbidity. Thus, it appears that acceptance is a key positive marker, especially when patients do not report depression. This is important to increase the efficacy of treatments in patients with multiple health conditions comorbid with fibromyalgia. ## References 1. Sifuentes-Giraldo W.A., Morell-Hita J.L.. Fibromialgia. Med.-Programa Form. Méd. Contin. Acreditado (2017) 12 1586-1595. DOI: 10.1016/j.med.2017.02.004 2. 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--- title: 'A Study on the Localization of Urban Residents’ Recreation: A Moderated Mediation Model Based on Temporal Self-Regulation Theory' authors: - Hui Tao - Qing Zhou - Qian Yang journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048969 doi: 10.3390/ijerph20065160 license: CC BY 4.0 --- # A Study on the Localization of Urban Residents’ Recreation: A Moderated Mediation Model Based on Temporal Self-Regulation Theory ## Abstract The pandemic has resulted in a further reduction in travel distance, recreational radius of destinations and other levels of tourism activity, making “local people traveling locally” a new feature. From the perspective of localization of urban residents’ recreation, this paper describes a moderated mediation model based on temporal self-regulation theory. Five representative urban parks in Beijing were selected as study areas, and data collected through a questionnaire were used to discuss the behavioral characteristics of localized recreation and the formation mechanism of sense of place among urban residents in Beijing. The results showed that: [1] connectedness beliefs and temporal valuations positively influenced sense of place, and had a positive indirect effect on sense of place through the mediating role of recreation involvement; [2] recreation benefits positively influenced sense of place; [3] recreation benefits reinforced the direct and mediating role of recreation involvement. Based on these findings, the paper concludes with a discussion of the theoretical value and practical implications, as well as future research directions for park and city management. ## 1. Introduction The fast-paced lifestyle, intense work pressure and recurrent outbreaks of COVID-19 have increased the overall negative emotions and psychological needs of individuals. Meanwhile, the demand of residents for local travel and daily leisure is greatly increasing due to the restrictions on cross-regional movement of people caused by the epidemic prevention and control measures. According to the Annual Report of China Domestic Tourism Development (2022–2023), the pandemic has made residents more cautious in their travels, which is reflected in the obvious reduction in travel distance and recreational radius of destinations, showing new characteristics such as short time, close distance and high frequency. The urban recreation market will enter the new stage of development in which the service targets are mainly local residents. “Recreation” consists of something carried out for refreshment or diversion, it is an activity that renews one’s health and spirits by relaxation and amusement [1]. The localization of recreation emphasizes recreational activities performed “in situ” (workplace, living place or recent permanent residence) from the geospatial dimension. Unlike long-distance travel on holidays, daily recreational needs can be met in the inner space or suburbs of cities. Meanwhile, the localization attaches great importance to placeness, which is regarded as a quality that distinguishes a place from other places and as a significant consumption element for recreationists. As places become more homogeneous and standardized, their distinctiveness and diversity are weakened, making it difficult for recreationists to form local identity and emotional attachment [2]. In the context of localization of recreation, through repeated interaction and complex connections between people and places, individuals are placed in familiar and meaningful environments, with local memories and emotions, resulting in the transformation of places from physical space to humanized emotional space. From this perspective, the sense of place is formed under the influence of placeness and its constructability. On the one hand, it can create a unique local image and emotional quality to satisfy the physiological and psychological needs of recreationists; on the other hand, it can enhance the sense of belonging and responsibility of recreationists, and stimulate their willingness to revisit and recommend the place. In addition, the localization of recreation is not just the exclusive discourse of local residents. In recent years, tourism has been showing the characteristics of “localization”, in which tourists show a tendency to converge with local residents in terms of preferences and behavioral patterns. An increasing number of tourists are widely integrated into the public space of destinations to experience the recreational activities preferred by local residents. Temporal self-regulation theory (TST) has been described as a “viable, integrative framework for contemporary research” that synthesizes ideas from cognitive psychology, behavioral economics and neuroscience into a relatively comprehensive “bio-psycho-social” model, which explains the multiple factors that influence people’s health behaviors [3,4]. According to TST, the capacity to be involved in behavior in accordance with long-range interests arises from a complex combination of biological, cognitive and social factors. With respect to personal behavioral choices, individuals intend to pursue behaviors that they believe are likely to have positive, immediate consequences. Individual differences in time perspective are associated with health-relevant decision-making processes [5,6]. TST has been widely used to explain and predict the occurrence of and change in individual health behaviors, including episodic drinking [7], sugar-sweetened beverage consumption [8], everyday smoking [9], supplement use [10] and medication adherence [11]. There is a strong link between health behaviors and personal choices of recreational activities [12]. Empirical studies in environmental and public health have shown that frequent participation in recreational activities can effectively reduce the risk of mental disorders and chronic diseases. Recreational behavior is a typical personal decision-making behavior in a healthy life. TST allows for the good explanation and prediction of health behaviors. Similarities in underlying mechanisms of health and recreational behavior, as well as evidence in both fields for the importance of TST, indicate that the theory can be applied to recreational behavior. Hence, applying the theory to assess recreational behavior may allow for simultaneous assessment of structures that have an impact on behavior and provide a comprehensive explanation of the issue. Residents’ sense of place is a complicated and dynamic process that results from the long-term interaction between residents and recreation areas [13]. This study constructed a moderated mediation model based on TST from the perspective of localization of recreation to investigate the behavioral characteristics of localized recreation and the formation mechanism of sense of place among urban residents. Using TST to explore the sense of place may be an essential process for identifying the potential relationships between important variables. The contributions of this study are as follows: first, in theory, it develops and extends the application of TST in the field of recreation behavior by applying it to the study of localization of recreation among urban residents; at the same time, it explores in depth the relationship among recreation involvement, recreation benefits and sense of place, which provides a powerful complement to urban park research from the perspective of demand. Second, in practice, this study explores important factors in the behavioral intention and decision-making process of localized recreation of residents, which helps to provide theoretical support and management suggestions for urban planning departments to reasonably meet the needs of localized recreation and improve the construction of localized recreation. ## 2.1. Sense of Place The concept of sense of place reflects the emotional, symbolic and spiritual aspects of places. It implies a relational notion of place, according to which undifferentiated space becomes place when we endow it with value [14]. In human geography, the term is used to encompass all the subjective meanings that become attached in some way to a place, identification with a place therefore grows through psychological investment and repeated encounters over time, leading to the gradual accumulation of meanings for that place, which in turn contributes to a sense of self and belonging [15]. As a complex affective bond between people and a specific location, sense of place should be seen as socially constructed, relational and part of social interactions and wider social processes [16], for example, a place may have restorative properties that enable individuals to get away from everyday routines or provide spiritual fulfillment. Some scholars have explored the variables that influence recreationists’ sense of place, such as: recreation involvement, recreation benefits. Involvement is regarded as a motivational variable reflecting the extent of personal relevance of the decision to the individual in terms of basic goals, values and self-concept. Manfredo defined involvement as the degree of interest in an activity and the affective response associated with that interest [17]. The construct of involvement in this study is conceptualized to be reflective of an enduring trait. It can reflect the level of commitment to recreation activities, related products or experience, and is regarded as an important concept for understanding recreation behavior [18]. When an individual has pleasant experiences from recreation participation and regards it as an important form of recreational life, recreation involvement arises as a state of motivation, incentive or interest, which persists in the whole process of activity participation and always brings benefits to the recreation subject. Recreation benefits are the main motivation for individuals to go out for recreation, as individuals gain continuous satisfaction by participating in any enjoyable recreational activities, being a subjective factor that helps to improve physical and mental conditions or satisfy needs by means of recreation behavior. Participation in recreational activities can alleviate depression, reduce stress, boost happiness, improve interpersonal relationships and enhance self-efficacy, all of which stimulate intrinsic desire of individuals to improve life satisfaction, and people will pursue the benefits of recreation at both individual and society levels [19]. After achieving the objective of recreation, the subjective feeling for recreation benefits is obtained, and the greater the benefits received, the more active the participation behavior will be [20]. To sum up, this study attempts to further investigate the formation mechanism of the sense of place based on recreation involvement and recreation benefits from the perspective of localization of recreation. ## 2.2. Temporal Self-Regulation Theory According to Bandura’s social cognitive theory (SCT), self-regulation is the interaction of the individual, behavior and environment, and it refers to the process of self-generated thoughts, feelings and actions that are planned and cyclically adapted to the attainment of personal goals [21,22]. Self-regulation is cyclical, as personal, behavioral and environmental factors are constantly changing during the course of learning and performance, and the feedback from prior performance is used to make adjustments during current efforts [23]. However, most studies have found that health-related behaviors are not completely under intentional control [24]. There has been a growing awareness of the influence of temporal factors on the decision-making process itself. Individuals’ behavioral choices are limited by temporal factors, the rationality of behavior largely depends on the temporal frame adopted, and long-term factors can motivate people to engage in health behaviors [25]. Therefore, the TST, which is based on the “intention–behavior” link and emphasizes the combined effects of temporality, behavioral prepotency and self-regulatory capacity, appears to be suitable for exploring the localization of urban residents’ recreation and the formation mechanism of sense of place. In the motivational stage, intentions to engage in a certain behavior are driven by connectedness beliefs and temporal valuations. Within the motivational sphere, connectedness beliefs arise from the strength of connectedness between the behavior itself and the outcomes, and the stronger the connectedness between the two, the more likely the subject is to develop connectedness beliefs. Temporal valuations inform intention to perform or refrain from behavior [26]. For example, most health protective behaviors such as exercise and healthy eating are beneficial in the long term if performed consistently. Evidence increasingly suggests that self-regulation, defined as the capacity to manage and control cognitive, behavioral and emotional responses to internal or environmental cues, is also an important component of health behavior theory [27]. Since the costs and benefits of health protective behaviors and health risk behaviors differ in temporal dispersion, adequate self-regulatory capacity is thought to be quite relevant to health behaviors. Adherence to healthy lifestyle behaviors requires planning and the ability to adapt to changes in the environment, these are all contained within the concept of self-regulation. Behavioral prepotency refers to the likelihood of performing a behavior given the frequency of performance in the past, habit strength and salient environmental cues; habit strength is one component of behavioral prepotency and is considered a strong predictor of behavior [28]. Specifically, executive function tests such as the Go/No-Go test are used. In addition, the TST purports that the influences on the intention–behavior relationship differ depending on the environmental context present at the time of performance. If the environment were perceived as being supportive of behavioral performance, then behavioral performance would be less reliant upon intentions and self-regulation than if the same behavior were performed in an environment that was highly distracting and unsupportive. However, for individuals who experience the immediate environment as unsupportive for behavioral performance, their behavior is determined by intentions, behavioral prepotency and self-regulation [29] (Figure 1). ## 3. Research Hypotheses By specifying behavioral self-regulation as influenced by both motivational and momentary factors, TST not only offers the potential to improve the prediction of behavior but also to identify pre- and postintentional factors influencing successful behavioral self-regulation which could be targeted by interventions. In light of previous research and the tenets of TST, this study aims to explore the constructs of TST as predictors of sense of place. Five variables are integrated into the TST framework, namely, recreation involvement into intention valuation, recreation benefits into perception evaluations where behavioral prepotency and self-regulatory capacity work together and sense of place into behavioral intention. ## 3.1. Connectedness Beliefs, Temporal Valuations and Sense of Place The motivational sphere describes conscious deliberations whether or not to engage in a behavior, this includes, in particular, temporal valuations about the time points when positive or negative consequences of the behavior can be expected. These temporal valuations inform intention to perform or refrain from behavior. The sense of place reflects a deep emotional connection between individuals and places. The perception and impression of recreation subjects for the space are a kind of local meaning that people give to the space, which endows places with recreational characteristics that are different from other spaces. Hammitt found that the more frequently individuals use a place, the stronger their sense of place, as well as their level of identification with the place [30]. Manzo pointed out that the sense of place has a significant incentive for individuals to make greater use of public places and spend more time on social interactions [31]. According to TST, connectedness beliefs and temporal valuations are at play in the motivational spectrum, behavior takes on a kind of psychological inertia as performance is repeated over time and habit strength is generated when behaviors are performed with high frequency in stable situational contexts [32]. Min Xiangxiao found that the mechanism of action between tourism image and sense of place was similar to the mechanism between “cognition and emotion” in psychology. Perception lies at the point of action between the two. When receiving external stimuli, recreationists make value judgments through the process of perception, thus ascending to a stable and abstract emotional experience and forming a sense of place [33]. On this basis, the following hypotheses are proposed in this study: ## 3.2. Connectedness Beliefs, Temporal Valuations and Recreation Involvement Involvement is essentially an attitude toward recreation that represents a determination about what is important, meaningful or relevant and can be used to explain participants’ recreation decisions and the process of decision-making [34]. Havitz and Dimanche defined recreation involvement as an unobservable state of motivation, arousal or interest toward a recreational activity, place and facility. The motivational sphere describes conscious deliberations whether or not to engage in a behavior and connectedness beliefs and temporal valuations in TST models form the main determinants of intention. This sphere of influence is similar to SCT in that it results in a deliberate decision to become involved in behavior or intention [35]. According to SCT and TST, those with high-outcome expectancy are more motivated to engage in the behavior, and there is indeed a causal relationship between outcome expectancy and formation of intention to engage in health behaviors. In addition, several studies have shown that increasing the salience of the association between current behavior and later outcomes has a strong motivational effect on intentional behavior [36]. Therefore, the following hypotheses are proposed: ## 3.3. Recreation Involvement and Sense of Place In the TST, frequency of past behavior can be a proxy for behavioral prepotency, and past behavior consistently appears to be the best predictor of future behavior [37]. The relationship between involvement and sense of place has been extensively researched by scholars, who have found a significant positive correlation between recreation involvement and sense of place. Recreation involvement predicts the level of sense of place and influences people’s emotional attachment to a place of recreation. Sense of place also reflects the level of involvement in recreation and influences people’s related behavior in recreation areas [38]. Havitz et al. proposed that recreation involvement had a driving effect on sense of place, emphasizing individuals’ sense of participation and experience during recreation, and their hope to enrich their spiritual world through recreation, which reflects emotional factors of recreationists about places from the perspective of involvement and can deeply explore the influence mechanisms of recreationists’ sense of place [39]. Wang Kun et al. proposed that involvement has a direct impact on sense of place [40], and Chen Wanru et al. transferred the model “leisure involvement → sense of place” to urban space for daily recreation activities [41]. Hence, the following hypothesis is formulated: ## 3.4. The Mediating Role of Recreation Involvement The relative predictability of behavior from intention, self-regulatory abilities and behavioral prepotency will depend on the ambient contingency structure of the social and physical environment in which the behavior occurs, in a manner consistent with the conceptualization of Rothman of the change process, all of these relationships are conceptualized within TST to represent cumulative feedback loops [42]. Involvement theory shows that recreation involvement is a mediating variable between intentions and behavioral intentions. The higher the degree of individual involvement in recreation activities, the stronger the “intention–behavior” link, and the more salient the participation behavior, recreation intentions and behavioral intentions of the subject [43]. Guo Qigui et al. examined the role of involvement as a mediating variable in the influence of recreation motivation on satisfaction [44]; Wang Zhenning et al. examined the correlation among motivation, involvement and behavioral intention of urban residents in self-driving travel, and the results showed that involvement played a partial mediating role in the influence of motivation on behavioral intention [45]; Fan Mengdan found that rural imagery had a significant positive influence on the sense of place through the mediating role of involvement, taking B&Bs in Xiamen as an example. Therefore, the following hypotheses are proposed: ## 3.5. Recreation Benefits and Sense of Place Some scholars believe that the measurement of recreation benefits should be integrated into a systematic evaluation model. Recreation has significant benefits for individual physical health (e.g., improving physical fitness, relieving stress, etc.), while implicit benefits are invisible satisfaction and growth in psychological states, and participants are more active in recreation activities when the benefits are highly evaluated [46]. Recreation benefits, as the subjective evaluation of recreationists’ perceptions about their experience during recreation activities, are closely related to the sense of place. Bi Lu Luan et al. argued that participating in recreational sport had the benefit of motivating individuals to interact with places, and that recreation benefits of different experiences were the driving force of intention to revisit [47]. Recreational activities and experience affect recreationists’ perceptions of local meaning, and to some extent, affect the level of well-being of recreationists in many ways, both physically and mentally, and in social relationships [48]. Thus, the following hypothesis is proposed: ## 3.6. The Moderating Role of Recreation Benefits Individual differences in responsiveness to environmental triggers should determine which variables—self-regulation or behavioral prepotency—are more likely to predict an ability to behave in a manner that is consistent with intentions to maintain a healthy lifestyle [49,50]. From a subjective and empirical perspective, recreation benefits are a strong predictor of participation in recreation activities [51]. Recreation benefits represent the achievement of recreation goals, and participants’ psychological needs are met and they feel the benefits of recreation after participating in recreation activities [52]. Tinsley et al. argued from an individual perspective that the higher the participants’ evaluation of potential benefits from recreation activities, the more obvious the attitude of participation and the more active the behavioral performance of participation [53]; similarly, studies have been conducted in Chongqing, a popular city in China, demonstrating the moderating role of recreation benefits in the relationship between behavior and sense of place [54]. Referring to the role of behavioral prepotency and self-regulatory capacity in the TST framework, this study uses recreation benefits as a moderating variable and proposes the following hypotheses: ## 3.7. A Moderated Mediation Role Those who experience positive outcomes may come to believe that the balance of future costs and benefits of continuation of the behavioral change (i.e., maintenance) will be worth it; that is, the likelihood of positive outcomes given the next performance of the behavior is strengthened, or value of the future experience of the outcome is enhanced by prior experience. As such, TST captures the influence of experiential aspects of the behavior change process on future behavior change efforts [55]. Edwards and Lambert showed that a moderated mediation effect may be established when the theoretical mechanisms of the moderating effect and the mediating effect work together [56]. From the above hypotheses, it is clear that connectedness beliefs and temporal valuations positively influence sense of place through the mediating mechanism of recreation involvement, and recreation benefits help to strengthen the relationship between recreation involvement and sense of place. In summary, recreation benefits may moderate the two mediating paths, namely, “connectedness beliefs → recreation involvement → sense of place” and “temporal valuations → recreation involvement → sense of place”, that is, a mediating effect may be moderated as well. Thus, the following hypotheses are proposed: To sum up, this paper has constructed a moderated mediation model based on TST, and the hypothetical model is shown in Figure 2. ## 4.1. Scale Development The research questionnaire is divided into two parts. The first part is the central part of the questionnaire, including the scales of different variables. The measurement items of each variable in the model are from mature scales widely used in the relevant literature. Among them are [1] connectedness beliefs scales, which refer to the research of Hsu [57] and Chiu [58], four items in total; [2] temporal valuations, which mainly use the CFC scales developed by Feng Jiaxi [59], three items in total; [3] recreation involvement variable, which mainly use the involvement scales developed by Li Qun [60], six items in total; [4] recreation benefits, mainly based on the recreation benefit scales developed by Yin Jianjun [61], five items in total; [5] sense of place variable, which refers to the research of Liu Qunyue [62], six items in total. All variables were measured using a 5-point Likert scale. The second part is the personal information of tourists, which contained three demographic characteristics and four recreation behavior characteristics. The characteristics of individual recreation behavior were used as the main control variables [63]. ## 4.2. Case Parks Due to the impact of COVID-19, recreation in parks is becoming a new way of life, the placeness contained in urban parks means important emotional significance to residents. We chose to collect data in urban parks of Beijing for two reasons: [1] Beijing has many urban parks which share obvious characteristics of large scale, diversity and balanced development, providing both tourism services for foreigners and leisure services for local residents [64]. [ 2] The five case parks attract crowds of recreationists and serve a wide range of people, forming high-quality places for recreation with certain social influence. Therefore, they are very representative and typical in reflecting recreation behaviors. Beijing Municipal Forestry and Parks Bureau has classified urban parks into: comprehensive parks, community parks, historical parks, ecological parks and cultural theme parks according to the main functions undertaken by the parks, with reference to Urban Green Space Classification Standard (CJJ/T85-2002) and Regulations of Beijing Municipal Parks. Specifically, comprehensive parks refer to parks with complete functions, well-equipped facilities and rich content, which can satisfy the diverse needs of different groups of visitors. Community parks refer to parks with necessary supporting facilities and activity areas, mainly serving the residents within a certain residential area for daily leisure activities. Historical parks refer to parks with outstanding historical and cultural value, which have had an impact on the urban transformation or cultural and artistic development of the city. Ecological parks refer to parks with natural environments characterized by original ecology or low human interference, which focus on meeting visitors’ needs to get close to nature, including forest parks, suburban parks, wetland parks, etc. Cultural theme parks refer to parks with special themes or cultures as their core content, including theme parks, botanical gardens, zoos and amusement parks. Based on the classification of urban parks in Beijing by Tao Xiaoli [65], five representative urban parks were selected as study areas, namely: comprehensive park (The Summer Palace), community park (The Black Bamboo Park), historical park (Temple of Heaven), ecological park (Olympic Forest Park) and cultural theme park (China National Botanical Garden). The locations of the case parks are shown in Figure 3. ## 4.3. Data Collection In the process of scale design, in order to ensure the accuracy and applicability of the scale, a presurvey was conducted with a total of 100 questionnaires distributed in the case parks from 6 April 2022 to 10 April 2022. The formal questionnaire was finally designed after analyzing the presurvey, deleting or revising ambiguous and unclear items. The presurvey results show that the Cronbach’s alpha of each construct is greater than 0.7, indicating that the scale has good reliability; the standardized factor loading values of each item are above 0.6, indicating that the scale has good construct validity. The formal survey was carried out from 13 April 2022 to 28 April 2022. Recreationists were chosen using systematic random sampling in areas with numerous recreationists, every five recreationists passing through the areas were approached. Before conducting the questionnaire research, an optional question of “Are you a local resident?” was set to filter out non-target objects. After the recreationists had accepted the invitation, one research assistant informed them of the purpose of the survey, the confidentiality of information and the meanings of some incomprehensible concepts, and all chosen recreationists agreed to participate. A total of 600 questionnaires were collected, and after eliminating invalid data, 545 valid samples were obtained, with a completion rate of $90.83\%$. ## 4.4. Sample Characteristics The statistical results of demographic characteristics are presented in Table 1. It can be seen that among the valid samples, female respondents ($50.5\%$) marginally outnumbered their male counterparts ($49.5\%$), but the gender distribution was relatively even. The highest proportion of participants was aged 19 to 30, followed by those aged 31 to 45. Regarding physical condition, $36.5\%$ of recreationists thought that they were in good health and $37.8\%$ assessed their health as fair. The descriptive statistical analysis of recreation behavior characteristics (Table 2) showed that: recreationists spent mainly 1–2 h in the park, accounting for $35.2\%$; most recreationists engaged in recreational activities in the park less than 3 times or 3–15 times per month, accounting for $64.6\%$; traveling alone was the main travel mode, accounting for $30.6\%$; recreationists were mainly visitors in the neighborhood, accounting for $55.6\%$, followed by long-distance travelers ($44.4\%$). ## 5.1. Measurement Model The 545 valid questionnaires were examined concerning their reliability using SPSS 25.0, and the results are shown in Table 3. Cronbach’s alpha of the five dimensions was greater than 0.7, so this questionnaire indicated a relatively high level of stability and internal consistency. The confirmatory factor analysis (CFA) was used to test the model fit and verify the accuracy of this structure. χ2/df = 3.285 < 5, RMSEA = 0.065 < 0.08; RFI = 0.904, CFI = 0.940, NFI = 0.916, IFI = 0.940, TLI = 0.931, all of which were greater than 0.90. The fit indexes all met the fit criteria, thus the goodness of fit was high in the overall model, and the model is valid. The standardized factor loading values (24 items in the scale) were all greater than 0.6, which meant that the loading for each item met the criteria. The composite reliability values (five variables) were all greater than the acceptable threshold of 0.7, indicating that the latent variables showed good construct reliability. The average variance extracted results for five variables were all above 0.5, demonstrating good convergent validity in the study, and that the dimensions selected for the questionnaire could well explain the variance of the variables. It can be seen from the discriminant validity (Table 4) that there was a significant correlation ($p \leq 0.001$) among variables, all of which were less than the square root of AVE, indicating that the latent variables were correlated and discriminated from each other, and the discriminant validity of the scale was ideal. ## 5.2. Structural Model To test the research hypotheses, multiple linear regression analysis was performed on the sample data, and the VIF values of all regression models were less than 5, indicating that multicollinearity was not a serious concern in the models. Meanwhile, the interaction terms involved were all centered in order to avoid potential threat of multicollinearity. [ 1]Main effects. To test H1a and H1b, sense of place was first included as the dependent variable, and then the control variable and independent variable were added into the regression equation. As shown from model 2 in Table 5: connectedness beliefs had a significant positive effect on sense of place (β = 0.382, $p \leq 0.001$); temporal valuations had a significant positive effect on sense of place (β = 0.366, $p \leq 0.001$). Therefore, H1a and H1b were supported. As shown in model 3: recreation benefits had a significant positive effect on sense of place (β = 0.591, $p \leq 0.001$). Thus, H5 was supported.[2]Mediating effects. This study followed the steps proposed by Baron and Kenny to test the mediating effect of recreation involvement. As shown in model 8: connectedness beliefs had a significant positive effect on recreation involvement (β = 0.338, $p \leq 0.001$); temporal valuations had a significant positive effect on recreation involvement (β = 0.249, $p \leq 0.001$); therefore, H2a and H2b were supported. Model 4 was used to test the relationship between recreation involvement (the mediating variable) and sense of place (the dependent variable). Model 4 showed that recreation involvement had a significant positive effect on sense of place (β = 0.585, $p \leq 0.001$). Therefore, H3 was supported. Meanwhile, as shown in model 2, connectedness beliefs and temporal valuations had a significant positive effect on sense of place, and it can be seen from model 5 that after adding recreation involvement as a mediating variable, the effect of connectedness beliefs and temporal valuations on sense of place was still significant (connectedness beliefs: β = 0.252, $p \leq 0.001$; temporal valuations: β = 0.271, $p \leq 0.001$), but compared to model 2, there was a greater decrease in their values. In other words, recreation involvement played a partial mediating role in the relationship among connectedness beliefs, temporal valuations and sense of place. Therefore, H4a and H4b were supported.[3]Moderating effects. As shown in model 6, recreation involvement had a significant positive effect on sense of place (β = 0.312, $p \leq 0.001$); the interaction term between recreation involvement and recreation benefits had a significant positive effect on sense of place (β = 0.237, $p \leq 0.001$), indicating that recreation benefits positively moderate the relationship between recreation involvement and sense of place. Therefore, H6 was supported. In order to test the moderating effect of recreation benefits, the method of Aiken and West was used to demonstrate the consistency of the moderating effect with the research hypotheses. Figure 4 displays the differences in the effect of recreation involvement on sense of place at different levels of recreation benefits, and it can be found that the slope of high-level recreation benefits was greater than that of low-level recreation benefits. Therefore, H6 was also supported. [ 4]Moderated mediation effects. The regression coefficients and $95\%$ confidence intervals were estimated using the bootstrapping method according to the test for moderated mediation effects proposed by Hayes, and the simple slope was used to test interaction effects of the moderating variables with the mediating variables, thus finally obtaining the changes after the mediating effects were moderated (a total of 5000 bootstrap samples were chosen). As shown in Table 6, when recreation benefits took a low value of −1.087, that is, when recreation benefits were within one standard deviation below the mean (M − 1SD), the value of the effect of connectedness beliefs on sense of place through recreation involvement was 0.0104, and the $95\%$ bootstrap confidence interval was [−0.0018, 0.036], containing zero; when recreation benefits took a high value of 1.087, that is, when recreation benefits were within one standard deviation above the mean (M + 1SD), the value of the effect of connectedness beliefs on sense of place through recreation involvement is 0.0972, and the $95\%$ bootstrap confidence interval was [0534, 0.152], not including zero. The confidence intervals ranged from including zero to not including zero, indicating that recreation benefits had a moderating effect in the mediation path “connectedness beliefs → recreation involvement → sense of place”. Thus, H7a was supported. Similarly, it can be found that recreation benefits played a moderating role in the mediation path “temporal valuations → recreation involvement → sense of place”, thus H7b was supported. ## 6.1. Conclusions In this paper, a moderated mediation model based on TST was constructed to explore the effect of connectedness beliefs and temporal valuations on sense of place, and to examine the mediating and moderating roles of recreation involvement and recreation benefits in the relationships. The study showed that:[1]Connectedness beliefs and temporal valuations positively influenced sense of place, and they also had a positive indirect effect on sense of place through the mediating variable of recreation involvement. Given that a large number of scholars have verified the significant positive correlation between recreation involvement and sense of place [66,67], this study added two antecedent variables, connectedness beliefs and temporal valuations, based on the TST framework. Recreation involvement served as a significant predictor of sense of place and also as a mediating variable among connectedness beliefs, temporal valuations and sense of place. In recreational activities, the stronger the connectedness between present actions and anticipated outcomes, and the closer the values attached to temporally dispersed outcomes, the greater the sense of involvement in the activity and the more likely it is to generate a sense of place.[2]Recreation benefits could significantly predict and positively influence sense of place. Greater recreation benefits increase the probability and degree of sense of place. Recreation benefits, as subjective evaluations of the individuals’ perceptions about the degree of satisfaction they achieve from recreational activities, can significantly influence participants’ attitudinal dispositions and behavioral performance. As the quality of recreation improves, residents feel a greater sense of dependence and identification with the recreation site, and they give positive feedback to the sense of place through feedback mechanisms, which will eventually manifest itself in residents’ attitudes towards choosing this place for ongoing recreation behavior.[3]The moderating role of recreation benefits. This study examined the moderating role of recreation benefits in the paths, which could strengthen the positive relationship between recreation involvement and sense of place, that is, the more recreation benefits recreationists received, the stronger the positive effect of recreation involvement on sense of place. In addition, recreation benefits mediate the role of recreation involvement in mediating between connectedness beliefs, temporal valuations and sense of place, and the more recreation benefits recreationists received, the stronger the mediating role of recreation involvement in the relationship among connectedness beliefs, temporal valuations and sense of place. ## 6.2. Theoretical Implications The theoretical contributions of this study were manifested as follows: First, a TST model was first introduced into the research of recreation behavior, and recreation involvement, recreation benefits and sense of place variables were applied to the context of localization of urban residents’ recreation, enriching the TST empirical research results. On the one hand, individuals usually believe that recreation in parks can help individuals to improve their sense of well-being, belonging and attachment, which implies connectedness beliefs about park recreation. However, such behavior must last for a relatively long time in order to obtain these values or benefits, and individuals have to suffer from problems such as long travel distance, inconvenient transportation and lack of landscapes. As the values or benefits are not close in time, temporal valuations decrease and the degree of individual recreation involvement weakens as well. Past recreation behaviors in parks have brought benefits such as promoting physical and mental health and building social interactions, and these recreation benefits can boost individual recreation involvement to positively influence the formation of sense of place. After measuring the overall benefits and costs of recreation behaviors in parks, individuals reinforce the effects of recreation behaviors by themselves and strengthen the positive influence of each variable on sense of place. On the other hand, the flow of sense of place in TST is from one context to the next. Individuals have focused on temporal valuations, connectedness beliefs and recreation involvement for the next behavior after engaging in a successful recreation activity, and have stored recreation benefits such as pleasure and self-identity for themselves in the form of symbols, thus individuals have acquired and retained a sense of place, which will influence the recurrence of recreation behaviors. Second, urban parks were used as study areas to explore the mechanisms of generating a sense of place for residents. While studies on sense of place based on psychology explore more about people’s relationships with relatively unfamiliar environments such as tourist destinations, studies on sense of place based on phenomenology focus more on people’s relationships with relatively familiar environments such as home and neighborhood communities. However, there are also many specific “places” that are between familiar and unfamiliar to people, which also deserve careful study, and urban parks are such special places, which are currently the subject of very few studies [68]. The process of localization takes place in the context of the extensive connection between people and places, whose abundant habitual practices constitute substantial internal diversity, while the habitual practices based on places are intimately embedded in a broad spatial structure of social relations. This brings up an implicit question of the relationship between people and place: what sense of place is created in public recreational spaces such as urban parks? Based on the path of an empirical psychological approach, this study explores the mechanisms influencing the localization of recreation and the generation of sense of place among residents in five representative urban parks in Beijing, providing a powerful complement to urban park research from the perspective of demand. Finally, using recreation benefits as a moderating variable, the mediation process between recreation involvement and sense of place was explained based on the perspective of recreation benefits. Recreation benefits, as the subjective evaluation of recreationists’ perceptions about their experience during recreation activities, are not only closely related to recreation involvement, but also have a positive impact on the sense of place. While existing studies tend to construct causal models with recreation benefits as dependent or mediating variables, this study argues that the moderating effect of recreation benefits needs to be further explored. Empirical evidence has found that recreation benefits could strengthen the positive relationship between recreation involvement and sense of place, and the more recreation benefits recreationists received, the stronger the mediating role of recreation involvement in the relationship among connectedness beliefs, temporal valuations and sense of place. This study uses recreation benefits as a moderating variable in the empirical analysis, reconstructing the connotation and extension of recreation benefits and providing new ideas for the boundary limitation of subsequent recreation benefits research. ## 6.3. Planning and Management Implications Based on the TST framework, this study discusses the practical implications of localized recreation for park and city management. First, as for park management, it is necessary [1] to innovate the interactive design of recreation space and set up interactive activities, such as horticulture, plant maintenance, landscape engineering and other gardening activities that recreationists can participate in. This can increase the degree of involvement and enthusiasm for interaction, and guide individuals to experience physical senses and internal emotions, thus enhancing recreation benefits for recreationists; [2] to integrate the unique local cultural elements and connotations into the renewal of public space through the construction of microspaces, creating a landscape image with local characteristics; [3] to focus on the deep emotional connection between people and places on the basis of meeting people’s basic physiological and psychological needs, developing a sense of place among recreationists; [4] to improve the functional division of parks, enhance the rationality of the layout and meet the needs and preferences of different groups for recreational activities, in order to ensure that recreationists have a pleasant experience in the park and extend their duration of stay; [5] to improve the infrastructure and service management of parks to enhance the willingness of recreationists to revisit and recommend the places. Second, as for urban management, it is recommended [1] to adopt relevant policies on ticket reduction and exemption, and open the parks to the society for free or at preferential prices to reduce the cost of recreation, thus promoting the positive moderating effect of temporal self-regulation in residents’ travel; [2] to improve the level of public transportation services and optimize the connectivity between various parks and traffic arteries, thus enhancing the convenience and accessibility, increasing the frequency of trips; [3] to pay attention to public mental health, encourage residents to take local trips, introduce preferential measures such as cultural and travel consumption vouchers and advocate restoring individual emotions and energy through recreation in parks; [4] to enhance the openness of urban public spaces, create open green spaces with various characteristics, guide community activities and promote the popularization and normalization of outdoor recreation; [5] to construct a perfect urban nature system and build a network of green spaces to satisfy the requirements of residents to interact with nature and feel more connected to the environment. ## 6.4. Limitations and Future Research As an exploratory study, the following three aspects can be investigated in future research, due to the limitations of the current research: First, the data collection was conducted during the comprehensive epidemic prevention and control phase, and the epidemic management requirements and the need to maintain social distance may have an impact on the localization of recreation. The sample should be increased in the future after the end of the epidemic so that the generalizability of the study findings can be improved by comparing data before and after the pandemic. Second, there is another situation for the localization of recreation: the “residentization” of foreign tourists, which means that foreign tourists behave more like residents in terms of tourism consumption patterns and behavioral preferences. In future research, the differences in behavioral characteristics of localization of recreation and the formation mechanisms of sense of place between foreign tourists and local residents can be compared to enhance the integrity of the theory. Third, in the design and test of the model, this study creatively drew on the TST framework model proposed by Hall and Fong, which is an extension of the application of TST in the field of recreational behavior, but it failed to comprehensively apply the TST model. In the future, a combination of multiple research methods will be used to verify the applicability of TST in more fields. Fourth, this study explored the formation mechanisms of sense of place and provided a new opportunity to better understand it, but still failed to fully open the black box of psychological mechanisms of sense of place. Sense of place is a complex theory and future research should add more psychological derivative structures to the existing model in order to explore the inner workings of sense of place to a greater extent. In a sense, urban recreation space is shaped by globalization and localization, and with the coexistence and interaction of the global and the local, new social and cultural practices emerge, all of which point to the individual’s imagination of “localization” and important formative factors. Therefore, when recreation space becomes uniform due to globalization, the concept of “localization” needs to be reconsidered and reinterpreted. “ Localization” should be extended to urban residents’ recreation decisions, based on the needs of residents for local recreation, to explore the spirit of place and the way of self-rest in urban parks, then interpret it in a completely new way and maximize its value, so that recreation can return to the essence of “life”. ## References 1. 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--- title: 'Mechanism Repositioning Based on Integrative Pharmacology: Anti-Inflammatory Effect of Safflower in Myocardial Ischemia–Reperfusion Injury' authors: - Feng Zhao - Hong Jiang - Tong Zhang - Hong Chen - Weijie Li - Xin Li - Ping Wang - Haiyu Xu journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048972 doi: 10.3390/ijms24065313 license: CC BY 4.0 --- # Mechanism Repositioning Based on Integrative Pharmacology: Anti-Inflammatory Effect of Safflower in Myocardial Ischemia–Reperfusion Injury ## Abstract Safflower (Carthamus tinctorius. L) possesses anti-tumor, anti-thrombotic, anti-oxidative, immunoregulatory, and cardio-cerebral protective effects. It is used clinically for the treatment of cardio-cerebrovascular disease in China. This study aimed to investigate the effects and mechanisms of action of safflower extract on myocardial ischemia–reperfusion (MIR) injury in a left anterior descending (LAD)-ligated model based on integrative pharmacology study and ultra-performance liquid chromatography–quadrupole time-of-flight-tandem mass spectrometer (UPLC-QTOF-MS/MS). Safflower (62.5, 125, 250 mg/kg) was administered immediately before reperfusion. Triphenyl tetrazolium chloride (TTC)/Evans blue, echocardiography, terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) assay, lactate dehydrogenase (LDH) ability, and superoxide dismutase (SOD) levels were determined after 24 h of reperfusion. Chemical components were obtained using UPLC-QTOF-MS/MS. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting were used to analyze mRNA and protein levels, respectively. Safflower dose-dependently reduced myocardial infarct size, improved cardiac function, decreased LDH levels, and increased SOD levels in C57/BL6 mice. A total of 11 key components and 31 hub targets were filtered based on the network analysis. Comprehensive analysis indicated that safflower alleviated inflammatory effects by downregulating the expression of NFκB1, IL-6, IL-1β, IL-18, TNFα, and MCP-1 and upregulating NFκBia, and markedly increased the expression of phosphorylated PI3K, AKT, PKC, and ERK/2, HIF1α, VEGFA, and BCL2, and decreased the level of BAX and phosphorylated p65. Safflower shows a significant cardioprotective effect by activating multiple inflammation-related signaling pathways, including the NFκB, HIF-1α, MAPK, TNF, and PI3K/AKT signaling pathways. These findings provide valuable insights into the clinical applications of safflower. ## 1. Introduction Acute myocardial ischemia (AMI) is a leading cause of morbidity and mortality worldwide, and it arises due to the disruption of a vulnerable atherosclerotic plaque or erosion of the coronary artery endothelium in most cases [1]. Timely myocardial reperfusion through percutaneous coronary intervention (PCI) or thrombolysis is the most effective method for treating myocardial infarction injury. However, it is associated with serious adverse outcomes, including arrhythmias, reversible contractile dysfunction, endothelial and microvascular dysfunction, and lethal cell damage, which are collectively termed “myocardial ischemia–reperfusion (MIR) injury” [2,3]. MIR injury contributes to $50\%$ of the final infarct size in AMI. Excessive inflammatory responses due to infiltration of circulating leukocytes following reperfusion aggravate cardiomyocyte death, resulting in the development of heart failure. Unfortunately, clinical trials have reported only few successful interventions for reperfusion injuries [4]. Therefore, the development of pharmacological interventions is a promising strategy for reperfusion injury therapy. Traditional Chinese medicine (TCM) has more than 2000 years of history and widespread clinical applications. TCM therapies consider multi-ingredients and multi-targets, lead to few adverse reactions, and are cost-effective. TCM might be used as a complementary and alternative approach to the primary and secondary prevention of cardiovascular disease [5,6]. TCM syndromes of AMI refer to thoracic obstruction, which was considered as qi and blood deficiency, liver depression and spleen deficiency (transmural ischemia), blood stasis and obstruction collaterals, and qi stagnation and blood stasis (thrombotic occlusion) [7,8]. The traditional effects of safflower (Carthamus tinctorius L.) include the regulation of blood stasis and improvement of blood circulation. It is commonly used as herbal medicine and has been for more than 1000 years [9]. Modern pharmacological studies have demonstrated that safflower possesses anti-tumor, anti-thrombotic, anti-oxidative, immunoregulatory, and cardio-cerebral protective effects [10,11,12]. Clinical preparations of safflower, such as safflower injection and safflower yellow injection, have been used clinically for the treatment of cardio-cerebrovascular disease in China for many years [5,13,14,15]. As one of the important components of Danhong injection, it has been used in the clinical therapy of cardiovascular and cerebrovascular diseases in China for several years [10]. In addition, studies also indicated that safflower reduces MIR injury by increasing left ventricular systolic pressure (LVSP), rate of left ventricular pressure change (+dp/dtmax and -dp/dtmax), and by decreasing the levels of malonaldehyde [11,12]. However, there is little in-depth and comprehensive evidence on the mechanisms of safflower against MIR injury. Traditional Chinese medicine integrative pharmacology (TCMIP) is an interdisciplinary subject based on the theory of TCM that comprehensively explores the interaction between various components of TCM and the body [16,17]. Briefly, by predicting the targets and pharmacological effects of herbal medicine, it enables the revelation of the association of the drug–gene–disease synergistic module, screens the synergistic multi-components, and clarifies the herbal ingredients and their related characteristics, as well as the relationship between the compound–target and target–disease [18]. This approach will be the next promising paradigm shift, from “one target, one component” to “network targets, multi-component” [19]. Ultraperformance liquid chromatography–quadrupole time-of-flight–tandem mass spectrometer (UPLC-QTOF-MS/MS) is a powerful tool for the qualitative characterization of chemical components in herbs by providing high chromatographic and mass resolution, accurate mass measurement, and abundant fragment ion information [20,21]. In this study, we used UPLC-QTOF-MS/MS for chromatographic separation and structural elucidation. In the present study, we employed the Integrative Pharmacology-based Network Computational Research Platform of TCM (TCMIP v2.0, http://www.tcmip.cn/, accessed on 3 January 2022) to evaluate the efficacy of safflower in the treatment of MIR injury by using a classical mouse model of ligation of the left anterior descending (LAD) coronary artery, described the chemical fingerprint of safflower by UPLC-QTOF-MS/MS, explored the mechanism of action of safflower against MIR injury verified the key genes by qRT-PCR and Western blotting. The present study provides a comprehensive understanding of the multi-target regulation induced by safflower against MIR injury, and complements the discovery of key ingredients and targets (Figure 1). ## 2.1. Effect of Safflower for Protection against MIR Injury in Mice Mice were randomly divided into six groups, including the sham group, control group, safflower low-dose group, medium-dose group, high-dose group (62.5 mg/kg, 125 mg/kg, 250 mg/kg, 4 times/24 h, i.v.), and positive control metoprolol group (12.5 mg/kg/d, i.p.). Cardiac infarct size was determined by triphenyl tetrazolium chloride (TTC)/Evans blue staining in mice who had undergone 30 min ischemia and 24 h reperfusion (Figure 2A). Based on the similar area at risk (AAR) (Figure 2B), the control group had a higher infarct size than the other groups following MIR injury, suggesting that the MIR model was established successfully. Safflower treatment (62.5, 125, 250 mg/kg, 4 times/24 h, i.v.) and metoprolol treatment (12.5 mg/kg, once for 24 h, i.p.) significantly reduced the percentage of infarct area (IS) in AAR (Figure 2C). Cardiac function was analyzed using echocardiography 24 h after reperfusion (Figure 2F–L). As shown in Figure 2H,L, the left ventricular ejection fraction (EF%) and left ventricular shortening fraction (FS%) values in the control group were significantly lower than those in the sham group ($p \leq 0.001$). Safflower (62.5, 125, and 250 mg/kg, 4 times/24 h) significantly improved cardiac contractile function, as reflected by increasing in EF% and FS% ($p \leq 0.05$). Safflower (62.5, 125, and 250 mg/kg, 4 times/4 h) also improved the left ventricular end-diastolic anterior wall thickness (LVAWs), left ventricular internal diameter systolic (LVIDs), and left ventricular volume systolic (LV Volume s) (Figure 2J–L, $p \leq 0.05$). Safflower in the high-dose group (250 mg/kg, 4 times/24 h, i.v.) showed an effect equivalent to that observed in the positive control metoprolol group (12.5 mg/kg, once for 24 h, i.p.). Similarly, to verify the effect of safflower on myocardial necrosis and oxidative stress in the serum, we measured the levels of LDH and SOD (Figure 2D,E). We found that LDH activity in the control group was higher than that in the sham group, while the SOD activity was lower in the control group than that in the sham group. LDH activity in the groups treated with safflower profoundly reduced in a dose-dependent manner, whereas the level of SOD increased after treatment with safflower and metoprolol in MIR-injured mice. Furthermore, we determined cardiomyocyte death through TUNEL staining (Figure 2M,N). The TUNEL-positive cells increased in the control group. Safflower and metoprolol treatment markedly reduced apoptosis in myocardial tissue (Figure 2N). No apoptosis was observed in the sham group (Figure 2M). ## 2.2. Identification and Characterization of Chemical Constituents in Safflower The base peak intensity (BPI) chromatograms of the water extraction of safflower in the positive and negative ion modes detected by UPLC-QTOF-MS/MS are shown in Figure 3. A total of 79 chemical compounds (51 in electrospray ionization [ESI+] and 51 in ESI-) in safflower were identified or tentatively characterized using UNIFI 1.8 software, and they included flavonoids, phenylpropanoids, alkaloids, lignans, and fatty acids. Among them, flavonoids accounted for $67\%$ (Figure 3E) and glycosides accounted for $57\%$ of the total phytoconstituents (Figure 3F). Detailed information on the chemical compounds is listed in Supplementary File S1, including the component name, half relaxation time (tR, min), measured value (m/z), theoretical value (m/z), error (ppm), formula used, response value, and fragment. Hydroxysafflor yellow A (HSYA) and N1,N5,N10-(Z)-tri-p-cou-maroylspermidine were used as examples to illustrate the identification process in detail. As shown in Figure 3C, the ion [M + H]+ 613.17534 was mainly determined as the molecular ion peak of HSYA, and representative fragments were m/z 451.12319 [M + H-Glc]+, 433.1127 [M + H-Glc-OH]+, 415.10177 [M + H-C6H14O7]+,and 235.08432 [M + H-C11H7O2]+, all of which were identified as HSYA [22]. As For N1,N5,N10-(Z)-tri-p-cou-maroylspermidine [23], the most indicative fragments were identified at m/z 582.2624 [M-H]-, and the other fragments were 316.16630 [M-H-C9H7O2-C8H7O]-, 145.02862 [M-H-C9H7O2-C8H7O-C8H17ON3]-, and 119.04965 [M-H-C9H7O2-C8H7O-C8H17ON3-CO]- (Figure 3D). ## 2.3. Target Prediction and Functional Enrichment The TCMIP v2.0 database was used to search for candidate targets of safflower. A total of 486 major candidate human genes corresponding to 79 chemical compound identifications were collected with similarity scores of 0.7 (Supplementary File S3). A total of 4579 MIR-related targets were obtained from three databases, including the GeneCard database (2341 genes), DisGeNET database (2173 genes), and TCMIP v2.0 database (65 genes), by searching for the keywords “myocardial ischemia–reperfusion injury,” “myocardial ischemia injury,” and “myocardial infarction injury.” A total of 859 targets were filtered for the next analysis with scores >10 for the GeneCard database, >0.1 for the DisGeNET database, and all the genes of the TCMIP v2.0 database. To identify the common genes between safflower and MIR injury, a Venn diagram was developed, which screened 105 overlapping genes between MIR targets [859] and safflower-related genes [486] (Figure 4A). A total of 105 overlapping genes were subjected to Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID v6.8). The GO terms included 329 biological processes (BPs), 59 cellular components (CCs), and 72 molecular functions (MFs) (Supplementary File S4, $p \leq 0.05$). The top 30 BP terms closely related to MIR injury are shown in Figure 4B, which suggests that biological processes mainly focus on the negative regulation of inflammation, hypoxia, and apoptosis. One hundred and forty-nine signaling pathways of safflower in MIR injury were enriched based on the KEGG database, 34 of which were optimally related to the corresponding pathological events involved in MIR injury (Figure 4C, Supplementary File S5). These pathways could be divided into three functional modules: ① Inflammation, which includes the TNF signaling pathway, NFκB signaling pathway, natural killer cell-mediated cytotoxicity, T-cell receptor signaling pathway, Toll-like receptor signaling pathway, and NOD-like receptor signaling pathway. The inflammation module, compared with the others, was the primary module; ② Metabolism, which includes the AMPK signaling pathway, cAMP signaling pathway, and arachidonic acid metabolism. This module contributes to cell metabolism, ③ apoptosis, Ca2+ overloading, and oxidant stress. ## 2.4. Screening of Key Components and Hub Genes To screen hub components and key targets, we constructed a multi-dimensional network using CytoScape 3.7. Based on the results of GO and KEGG analyses, 71 important targets were screened from the 34 pathological event pathways of MIR injury, which were regarded as effective targets in the therapeutic effect of safflower on MIR injury. The 71 important targets were discovered in 55 chemical compounds. Coincidentally, most of these were flavonoids, flavonoid glycosides, and other glycosides. The 71 key targets, 55 active ingredients, and 34 KEGG pathways were entered into CytoScape 3.7, and a correlative network of “compounds-targets-pathways-symptoms” was constructed (Figure 5A). The active ingredients marked in blue were divided into three categories: flavonoids, flavonoid glycosides, and other compounds. Notably, flavonoids played a critical role in the treatment of MIR injury. The targets and pathways marked in orange and pink, respectively, were distributed into three groups based on pharmacological activities, including immune inflammation, metabolism, and others. In addition, gene classification was dependent on the frequency of gene targets in different pathways. As shown in Figure 5A, immune inflammation accounted for the largest proportion. The interactive network consisted of 160 nodes and 729 edges, and detailed information is provided in Supplementary File S6. The network was divided into three parts: components, targets, and pathways. The topological feature “degree” was chosen to identify topologically important nodes. Nodes with degree values greater than the median were filtered out as hub targets. For genes, the median value of “degree” was 6. Thirty-two nodes were selected as the hub genes (Table 1). The top ten genes were PRKCA, AKT1, CSNK2A1, CSNK2B, PIK3CG, HSPA2, PTGS1, MAPK1, HK1, and TNF. The 31 hub genes were subjected to functional enrichment analysis again. The main functional module was significantly associated with immune inflammation and included the NFκB, PI3K/AKT, MAPK, HIF1α/VEGFA, TNF, and IL-17 signaling pathways (Figure 5B, Supplementary File S7), which are marked in blue, and the size of the node represents its importance in the network. The five most significantly regulated pathways were chosen to be verified. Homogenization is a challenging problem in the network pharmacology of TCM. To overcome the bottleneck problem, an integrative pharmacology strategy has been firstly applied for the effects of safflower on MIR injury by network analysis of Component–Target–Disease weighted by chemical composition content, literature mining based on key active components and bioactivities, and quantitative analysis [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]. A total of 19 chemical components with a degree greater than the median of 8 were selected. Literature mining was performed to verify the reliability of the key components prediction results, which were filtered by the median of 8. As shown in Supplementary File S8, the literature based on animal studies or cell experiment with the model of hypoxia/reoxygenation or myocardial ischemia/reperfusion injury. In addition, the compound content was considered an important filter condition. As shown in Figure 3 and Supplementary File S1, we listed the compounds in descending order of response value and screened the compounds with a median response value of 67667. A total of 11 compounds were filtered by the three above-mentioned factors, including quercetin (+40), luteolin (−44, +55), apigenin (−47, +58), rutin (+33), hydroxysafflor yellow A (−11, +17), kaempferol (−42, +44), baicalin (−40, +51), eriodictyol (+23), 6-hydroxyapigenin (−48, +46), 6-hydroxykaempferol (−32, +35), and 6-hydroxykaempferol 3-rutinoside-6-glucoside. Quercetin (+40), luteolin (−44, +55), apigenin (−47, +58), kaempferol (−42, +44), baicalin (−40, +51), and eriodictyol (+23) had higher degree values and higher response values. Rutin (+33) and HSYA (−11, +17) had a lot of literature evidence and higher response values. 6-hydroxyapigenin (−48, +46), 6-hydroxykaempferol (−32, +35), and 6-hydroxykaempferol 3-rutinoside-6-glucoside had high degree values and higher response values. However, they have not been reported thus far (Table 2). ## 2.5. The Mechanism and Molecular Docking A diagram of the mechanism, including the hub targets, key components, and crucial signaling pathways, is shown in Figure 6A. Molecular docking was also performed to determine the binding affinities of key ingredients and hub targets. The crucial proteins, including PRKCA, PIK3CG, and AKT1, were filtered for molecular docking. The results showed that PRKCA, PIK3CG, and AKT1 had a good affinity with 11 hub compounds with stable conformations and high binding activity. As for PRKCA, a total of 10 of 11 hub compounds bind with binding affinities of less than -7.8 kcal/mol (Figure 6B, Supplementary File S10), including 6-hydroxykaempferol-3-rutinoside-6-glucoside, 6-hydroxykaempferol, apigenin, baicalin, eriodictyol, HSYA, kaempferol, luteolin, quercetin, and rutin. And the five hub compounds, including eriodictyol, HSYA, kaempferol, luteolin, quercetin, and rutin, can bind to PIK3CG and AKT1 with the binding affinities of less than −5.9 (C) and −7.5 (D) kcal/mol, respectively, respectively (Figure 6C,D, Supplementary File S10). In this study, the binding affinities of less than –5.0 kcal/mol were employed as screening criteria [97], and PyMOL 2.5 was used for visualization. ## 2.6. Safflower Inhibits Inflammation-Related Factors in MIR Mice The accuracy of the network pharmacology prediction results was verified by performing qRT-PCR and Western blot. The NFκB, HIF-1α, MAPK, TNF, and PI3K/AKT signaling pathways are all involved in the activation of inflammation-related factors. The upstream hub genes were quantified through Western blotting, and the downstream inflammatory factors were detected by using qRT-PCR. Safflower treatment increased the expression of phosphorylation of PI3K and AKT, HIF1α, VEGFA, ERK$\frac{1}{2}$, and PKC, and decreased the level of phosphorylated p65 (Figure 7A–F). Safflower also inhibited the MIR-injured cardiomyocyte apoptosis, inhibited the expression of BAX, and promoted the expression of BCL2 (Figure 7G). As shown in Figure 7H–N, the expression of inflammation-related factors, including NFκB-1, IL-6, IL-1β, IL-18, MCP-1, and TNF-α, in the heart tissue of the mice in the control group was significantly upregulated compared to that in the sham group ($p \leq 0.01$), whereas the expression of NFκBia was downregulated. A dose-independent decrease in NFκB-1, IL-6, IL-1β, IL-18, MCP-1, and TNF-α expression was observed in safflower-treated mice ($p \leq 0.05$), and NFκBia mRNA expression significantly increased ($p \leq 0.01$). However, there were no significant differences in the mRNA levels of PI3K or HIF1α (Supplementary File S11). ## 3. Discussion In the present study, we identified 11 key components and 31 hub targets of safflower treatment in MIR injury using an integrative pharmacological strategy and focused on the mechanism of inflammatory response mediated by the NFκB, HIF-1α, MAPK, TNF, and PI3K/AKT signaling pathways. According to Network Pharmacology Evaluation Method Guidance, the network pharmacology evaluation is conducted from reliability, standardization, and rationality [98]. In our study, UPLC-QTOF-MS/MS, literature mining, and experimental verification ensured the reliability, and TCMIP v2.0 supplemented the standardization and rationality. Inflammation plays a prominent role in MIR injury. During the initial stages of AMI, an acute inflammatory response is evoked, in which neutrophils infiltrate the myocardium via chemotactic attraction and aggravate the state of the already injured tissue. When neutrophils reach the reperfused tissue, they are exposed to chemotactic agents that are mainly released from endothelial cells and activated in their normal systemic circulation path [99]. This pro-inflammatory response is exacerbated and continues to cause cardiomyocyte death 6–24 h post-reperfusion [100]. Necrotic cardiac cells release nuclear factors, such as TLR4 and MCP-1, to activate the HIF-1α, MAPK, and PI3K/AKT signaling pathways, further promoting the NFκB signaling pathway [22,23,101]. Then the signaling pathways mediate the release of inflammatory cytokines, such as IL-6, IL-1β, IL-18, TNF-α, etc., leading to neutrophil attraction, sequestration, and adhesion [100]. Accompanied by the release of these cytokines, pro-inflammatory signaling pathways are activated again. Therefore, the inhibition of inflammatory factors during MIR injury effectively inhibits myocardial injury. β-blocker metoprolol administered before reperfusion can reduce myocardial infarct size in mice, pigs, and humans by eliminating exacerbated inflammation [102]. PKC also plays an important role during myocardial I/R in redox regulation (redox signaling and oxidative stress), cell death (apoptosis and necrosis), Ca2+ overload, and mitochondrial dysfunction [23]. In the present study, the mechanism of safflower treatment in MIR injury was an inflammatory response mediated by the NFκB, HIF-1α, MAPK, TNF, and PI3K/AKT signaling pathways. Safflower markedly inhibited the expression of inflammatory factors, including IL-6, IL-1β, IL-18, MCP-1, and TNF-α. In the present study, we identified 79 compounds in safflower using UPLC-QTOF-MS/MS. UPLC-QTOF-MS/MS is a powerful tool for the qualitative characterization of chemical components in herbs because it provides high chromatographic and mass resolution, accurate mass measurement, and abundant fragment ion information [20]. Compared with other mass spectrometers, the TOF analyzer has a higher mass resolution, sensitivity, and accuracy; in addition, it can provide accurate ion mass and molecular formulas. MSE technology is a new data acquisition method. It is helpful for the comprehensive analysis of complex samples, and can obtain accurate mass measurements of precursors and product ions at a significant speed [21]. For the first time, we used the UPLC-QTOF-MS/MS strategy to identify safflower phytochemicals. Safflower is widely used in the clinical treatment of cardio-cerebrovascular diseases. The commonly used safflower preparations include safflower yellow injection, safflower injection, and safflower oil (p.o.). Safflower preparations show excellent protection and safety in treating coronary heart disease, angina pectoris, obesity, and blood pressure [103,104,105,106]. Safflower, as the gentleman medicine of Xuebijing injection, has apparent clinical effects on sepsis [107]. Danhong injection is a medicinal preparation based on Salviae Miltiorrhizae and Flos Carthami (safflower) and has also been used in the clinical therapy of cardiovascular and cerebrovascular diseases in China for many years [10]. Flavonoids are the main active components of safflower [108]. In the present study, we identified 53 flavonoids out of the 79 compounds in safflower. Flavonoids possess anti-oxidant, anti-microbial, and anti-platelet aggregation effects; they are also recognized as excellent anti-inflammatory agents [109,110]. Flavonoids can inhibit the activation of inflammatory pathways, such as the NFκB, MAPK, and bone morphogenetic protein 2/small mothers against decapentaplegic (BMP2/SMAD) signaling pathways. In addition, they inhibit the expression of pro-inflammatory enzymes, such as activating protein-1, cyclooxygenase-2, lipoxygenase, and inducible nitric oxide, and decrease the expression of various pro-inflammatory cytokines [111,112]. In the present study, we filtered out 11 core anti-MIR injury compounds, including quercetin, luteolin, apigenin, rutin, HSYA, kaempferol, baicalin, eriodictyol, 6-hydroxyapigenin, 6-hydroxykaempferol, and 6-hydroxykaempferol 3-rutinoside-6-glucoside. These compounds belong to the family of flavonoids and flavonoid glycosides. Quercetin is a characteristic flavonoid that has been extensively studied. Quercetin exhibits significant pharmacological effects such as anti-inflammatory, anti-oxidant, anti-viral, and cardioprotective effects [113,114]. Furthermore, quercetin is involved in the NFκB and MAPK signaling pathways and inhibits pro-inflammatory factors. Luteolin is present in vegetables and fruits and is known to be responsible for its anti-inflammatory activity [115,116]. Luteolin inhibits the expression of IL-1β, IL-2, IL-6, IL-8, IL-12, IL-17, TNF-α, and interferon (IFN)-β. Kaempferol also potently inhibits pro-inflammatory proteins such as PKC, NFκB, and MAPK (ERK, p38, and JNK) [117]. Apigenin promotes different anti-inflammatory pathways, such as p38/MAPK and PI3K/AKT [118]. Other flavone compounds also show excellent anti-inflammatory activities [119,120]. HSYA, rutin, and 6-hydroxykaempferol 3-rutinoside-6-glucoside are flavonoid glycosides (quinochalcone C-glycosides) that are characteristic ingredients of safflower [121,122,123]. HSYA, a quality marker (Q-marker) for safflower, is a representative quinochalcone C-glycoside. It exerts anti-oxidant, anti-inflammatory, anti-coagulant, anti-cancer, and cardio-cerebrovascular protective effects. HSYA attenuates the activation of NFκB, MAPK, and Nrf-2/HO-1 signaling pathways [124,125]. Rutin is involved in p53 expression and in the PI3K/AKT signaling and NFκB signaling pathways [126]. To date, 23 quinochalcone C-glycosides have been isolated from safflowers [6,123,125], including safflower yellow B, carthamin, hydroxyethylcarthamin, safflomin A, safflomin B, safflomin C, isoflurane C (isosafflomin C), pre-carthamin, anhydrosafflor yellow B (AHSYB), nitrogen-containing quinochalcone C-glycoside tinctormin and cartormin, saffloquinoside A, saffloquinoside B, saffloquinoside C, methylsafflomin C, methylisosafflomin C, hydroxysafflor yellow B (HSYB), hydroxy red anthocyanin C (HSYC), carthorquinoside A, carthorquinoside B, and isocartormin, all of which possess significant therapeutic potentials [127,128]. However, flavonoids are poorly absorbed, with an extremely low oral bioavailability and a short half-life. Their plasma concentrations in the human body are usually < 1 μmol/L, which presents great challenges for clinical application [129,130,131]. We predicted the mechanism of action of safflower by using TCMIP v2.0. The advantages of the TCMIP are summarized in three aspects. The first is a combination of computational biology and network pharmacology. TCMIP is carried out from the perspective of computer virtualization for the interaction among big data. The second is experimental verification. TCMIP pays more attention to pharmacological evaluation to verify it from a “practical” perspective. Thirdly, the integration of pharmacokinetics and pharmacodynamics. To study the interaction between TCM prescriptions and the body from multiple levels and links and systematically and comprehensively reveal the pharmacodynamic material basis and mechanism of the efficacy of TCM prescriptions, we performed a comprehensive and systematic evaluation by integrating virtual prediction and experimental verification to increase data accuracy [13,132]. To avoid the homogenization of key components screening of “different diseases and different prescriptions”, we addressed the current gaps in the literature of integrated pharmacology, and used UPLC-QTOF-MS/MS to supplement the deficiencies of the database. Integrated pharmacology is a qualitative analysis based on the “component–target–disease” network that ignores the influence of component quantifications. Therefore, we increased the screening of component contents. ## 4.1.1. Preparation of Safflower Safflower (210616z11) was purchased from Beijing Shengshilong Pharmaceutical Co., Ltd., (Beijing, China). The safflower powder was sieved through a 50-mesh sieve and 4.0 g of it was soaked in 50 mL of $25\%$ methanol before ultrasonic extraction for 40 min (power 300 W, frequency 50 kHz). After the supernatant was centrifuged at 3000× g rpm for 10 min, it was filtered through a 0.22 μm filter (Pall Corporation, Beijing, China). Then, 2 μL aliquots were injected into the UPLC-QTOF-MS/MS system. Metoprolol (H32025391; AstraZeneca, Switzerland) was used as the positive control. ## 4.1.2. Animals All procedures were approved by the Medicine Ethics Review Committee for Animal Experiments at the Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences. C57/BL6 male mice (specific pathogen-free (SPF) grade, Certification No. 111251220100022578, Ethical No. 2022B039), weighing 20 ± 2 g and eight weeks old, were purchased from Beijing Huafukang Bioscience Co. (Beijing, China). The mice were housed in a controlled environment (24 ± 1 °C temperature, 50 ± $10\%$ relative humidity) with a $\frac{12}{12}$ h light/dark cycle and free access to water and standard diet under specific pathogen-free (SPF) conditions. ## 4.2.1. Induction and Treatment of MIR-Injured Mice A mouse model of MIR injury was established by LAD ligation as previously described [15]. The mice were anesthetized with $3\%$ isoflurane (Beijing ZS Dichuang Technology Development Co., Ltd., Beijing, China) inhalation using a respiratory anesthesia machine (ZS-MV, Beijing ZS Dichuang Technology Development Co., Ltd., Beijing, China). Afterward, the mice were transferred to $1\%$ isoflurane for maintenance anesthesia. After the pericardium was opened, the heart was exposed between the third and fourth intercostal space on the left, and 2–3 mm of the coronary artery was ligated using a 7–0 silk suture. After 30 min of ligation, reperfusion was performed for 24 h. Mice in the sham group without LAD ligation were also subjected to reperfusion as described above. A total of 126 mice were randomly divided into six groups, including the sham group, control group, safflower low-dose group, medium-dose group, high-dose group (62.5 mg/kg, 125 mg/kg, 250 mg/kg, 4 times/24 h, i.v.), and positive control metoprolol group (12.5 mg/kg/d, i.p.) [ 11,133]. Among them, 36 mice (six in each group) were used to detect echocardiography, biochemical markers, and qRT-PCR. A total of 54 mice (9 from each group) were assigned to the TTC/Evans blue staining experiment. Western blot and TUNEL experiments (three in each group) included 18 mice in each experiment. Safflower extract and metoprolol were all dissolved in $0.9\%$ NaCl. It was immediately injected into the tail vein injection after ischemia and before reperfusion. Because of rapid elimination within 6 h of safflower [134], it was administered every 6 h. Metoprolol was administered via intraperitoneal injection (12.5 mg/kg/d) [102]. Mice in the sham and control groups were treated with the solvent carrier. ## 4.2.2. Echocardiography After 24 h of reperfusion, the mice were transferred to $1\%$ isoflurane for maintenance anesthesia. Cardiac function was evaluated by echocardiography (VisualSonics VeVo 2100 Imaging System). Each group had six mice. ## 4.2.3. TTC/Evans Blue Staining Mice were anesthetized with $1\%$ pentobarbital sodium after 24 h reperfusion. 200 μL of $2\%$ Evans blue (Sigma, E2129, Steinheim, Germany) was perfused through thoracic aorta for 30 s. Subsequently, the heart was immediately harvested and frozen in a −80 °C refrigerator for 30 min. Afterward, the heart was sectioned into 2 mm thick slices below the ligation position using heart mold and stained with $1\%$ TTC (Sigma, T8877-100G, USA) at 37 °C for 10 min. Each group had nine mice. ## 4.2.4. Detection of Biochemical Markers in the Blood After 24 h of reperfusion, the mice were anesthetized with $1\%$ pentobarbital sodium. Blood was collected from the inferior vena cava and centrifuged at 8000× g rpm under 4 °C for 5 min. The plasma in the upper layer was harvested for the detection of SOD (Beyotime, S0101S, Beijing, China) and LDH (Solarbio, BC0685, Beijing, China) activities. ## 4.2.5. TUNEL Assay After 24 h of reperfusion, the mice were anesthetized with $1\%$ pentobarbital sodium. The hearts were harvested after perfusing with 5 mL PBS. TUNEL staining was performed using the In Situ Cell Death Detection Kit, Fluorescein (Roche, Beijing, China). The sections (4 µm) were deparaffinized using xylene, rehydrated in $100\%$, $90\%$, $80\%$, and $70\%$ ethanol, and permeabilized in $0.1\%$ Triton-X-100 for 8 min at 37 °C. We mixed 50 μL terminal deoxynucleotidyl transferase (TdT) and 450 μL fluorescein-labeled dUTP solution. A 20 µL volume of staining solution was added per sample. Apoptotic cells were detected after incubation with the mixture for 30 min at 37 °C. The nuclei were labeled with DAPI (ZSGB-BIO, ZLI-9556, Beijing, China). ## 4.3.1. Component Identification Chromatography was performed using a Waters UPLC I-Class system (Waters Corp., Milford, MA, USA) equipped with a binary pump, online vacuum degasser, autosampler, and automatic thermostatic column oven coupled with a quadrupole-time-of-flight mass spectrometer. A Waters Xevo G2-S Q-TOF Mass System (Manchester, United Kingdom) equipped with electrospray ionization (ESI). Data were recorded using Masslynx V4.1 (Waters Corporation, Milford, MA, USA). The UNIFI software 1.8 (Waters Corporation, Milford, MA, USA) was used for peak detection and preliminary compound identification. Chromatographic separation was performed on a Waters Acquity UPLC HSS T3 column (100 mm × 2.1 mm, i.d., 1.8 μm) maintained at 30 °C, and a linear gradient of $0.1\%$ formic acid–water (A) and $0.1\%$ formic acid–acetonitrile (B) was used for the elution procedure, as follows: 0–2 min, 5–$90\%$ B; 2–10 min, 90–$80\%$ B; 10–16 min, 80–$60\%$ B; 16–20 min, 60–$5\%$ B. The flow rate was set at 0.2 mL/min, and a 2.0 µL aliquot was set as the injection volume. ## 4.3.2. Mass Spectrometry Conditions The UPLC-QTOF-MS/MS data were collected in full-scan auto mode in positive and negative ion modes. The optimal parameters for the best response for most of the compounds were set as follows: ESI+ capillary voltage, 0.5 KV; ESI- capillary voltage, 2.5 KV; sampling cone, 40 V; source temperature, 100 °C; desolvation temperature, 450 °C; gas temperature of atmospheric gas, 450 °C; cone gas flow, 50 L/h; desolvation gas flow, 900 L/h; mass range, 50–1, 500 m/z. The collision energies were 40–60 V for ESI+ and 60–80 V for ESI−. ## 4.3.3. Data Processing Waters UNIFI 1.8 data processing software was used to process the quasi-molecular ion peaks collected using the UPLC-QTOF-MS/MS system. The MSE data collected in a continuum mode were processed and matched to a customized library based on the Encyclopedia of Traditional Chinese Medicine (ETCM, http://www.nrc.ac.cn:9090/ETCM/, accessed on 2 January 2022) using the Waters UNIFI system with an error of 1 × 10−5 ppm. The analysis process included data acquisition, data mining, library searching, and report generation. ## 4.4.1. Prediction of the Targets of Safflower The chemical structural formula (sdf.) of identified compounds in safflower were collected from PubChem (https://pubchem.ncbi.nlm.nih.gov/, accessed on 20 January 2020), which were transformed into mol. formula by OpenBabel GUI 2.4.1 (last update on 21 September 2016, version 2.4.1, http://openbabel.org/wiki/MainPage, accessed on 20 January 2022). And then uploaded them to TCMIP v2.0 (http://www.tcmip.cn, accessed on 20 January 2020) to predict putative targets with a Tanimoto score of 0.7. ## 4.4.2. Collection of MIR-Related Targets MIR-related targets were collected from three well-known databases with the keywords of “Myocardial ischemia–reperfusion injury,” “Myocardial ischemia injury,” “Acute myocardial ischemia injury,” and “Myocardial infarct injury”: the DisGeNET database (http://www.disgenet.org, accessed on 20 January 2022), GeneCards: The Human Gene Database (https://www.genecards.org/, accessed on 20 January 2022), and TCMIP v2.0. Scores > 0.1 for DisGeNET, scores > 10 for GeneCards, and no threshold for TCMIP v2.0. We combined all the genes, removed duplicates, and obtained MIR-related targets. Detailed information is provided in Supplementary File S2. ## 4.4.3. Functional Analysis and Network Construction To better demonstrate the common targets of safflower and MIR injury, the predictive targets of safflower and MIR-related genes were uploaded to the website of the Venn diagram (Supplementary File S9) (http://bioinformatics.psb.ugent.be/webtools/Venn/, accessed on 22 January 2022). Overlapping genes from the two groups were used for further analysis. The potential biological function of safflower was analyzed by KEGG and GO enrichment using the DAVID v6.8 database (Supplementary Files S4 and S5) (https://david.ncifcrf.gov/, accessed on 30 January 2022). The top 30 BP terms and 34 KEGG terms were shown in Figure 4. To clearly explain the complex relationships between components of safflower, known MIR-related genes, and predicted signaling pathways, network visualization was performed using the CytoScape platform (version 3.9.0, https://cytoscape.org/, accessed on 30 January 2022). Components, genes, and pathways were all present as independent nodes (Supplementary File S11). CytoScape 3.9.0 calculated three topological parameters for each of these nodes, including “degree”, “betweenness”, and “closeness”. ## 4.4.4. Hub Target and Key Component Screening Hub target screening was performed using network topological analysis. The hub genes were defined as having higher degree values than the median. For genes, the median degree value is 6. To avoid homogenization, key component screening was subjected to the three screening criteria described below, and the components that met two of the three screening criteria are regarded as core compounds. The details were listed as follows:①Network topological analysis: The degree value of the network was calculated to screen for core compounds. The degree values of the core compounds were higher than the median. For components, the median degree value is 8;②Literature mining: A systematic search was performed on PubMed using the following sets of keywords: the “name” of the ingredients, “myocardial ischemia–reperfusion,” and “cardiac ischemia reperfusion.” Studies included in this search were those published from 1967 to November 2022. The literature mainly focuses on animal studies, and none of the ingredients has been found to be used clinically for the treatment of MIR injury. Articles related to the combinations of ingredients were also included. In the selected studies, the following data were meticulously reviewed and extracted: “infarct size detection,” “cardiac function detection” or “serum parameters.” The median of the number of studies was calculated to filter the key compounds;③Quantitative analysis: The response values of the compounds were ranked according to UPLC-QTOF-MS/MS to screen for components with response values above the median. ## 4.4.5. Molecular Docking Simulation The molecular docking and virtual screening program were carried out to investigate the direct binding efficiencies of hub targets and key components. PRKCA (PDB ID: 3iw4), AKT1 (PDB ID: 3qkm), and PI3K (PDB ID: 1e7u) were collected from the Protein Data Bank (https://www.rcsb.org/, accessed on 20 January 2023). The ligands of 6-hydroxyapigenin, 6-hydroxykaempferol-3-rutinoside-6-glucoside, 6-hydroxykaempferol, apigenin, baicalin, eriodictyol, HSYA, kaempferol, luteolin, quercetin, and rutin were downloaded from the PubChem database [31] in sdf. format, which were converted to pdb. format using OpenBabel GUI 2.4.1. AutoDock Tools 1.5.6 (https://ccsb.scripps.edu/mgltools/, accessed on 22 January 2023) was used for dehydration, hydrogenation, and charging. Docking calculations were performed using AutoDock Vina 1.1.2 (The Scripps Research Institute) and AutoDock 4.2.6 (The Scripps Research Institute). The visualization and analysis of the results were used by PyMOL 2.5 (https://pymol.org/2/, accessed on 22 January 2023). ## 4.5.1. Quantitative Real-Time Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) qRT-PCR was performed using the SYBR® Select Master Mix (Toyobo, Tokyo, Japan). A total RNA was extracted using TRNzol Universal Reagent (Tiangen, DP430, Beijing, China), and 300 ng RNA was reverse-transcribed into cDNA using ReverTra Ace qPCR RT Master Mix (TOYOBO, FSQ-301, Osaka, Japan). Single-stranded cDNA was amplified by PCR with primers for NFκBia, NFκB1, IL6, IL1β, MCP-1, IL-18, TNF-α, and β-actin using Taq pro Universal SYBR qPCR Master Mix (Vazyme, Q712-02, Nanjing, China); the primer sequences are shown in Table 3. Primers were synthesized by Tsingke Biotechnology Co., Ltd. (Beijing, China). ## 4.5.2. Western Blot After 24 h of reperfusion, the hearts of mice were collected for Western blot analysis. The total protein content of the supernatant was quantified using Bicinchoninic acid (BCA) protein assay kit (Beyotime, P0010S, Nanjin, China). Protein samples were boiled at 100 °C in 5 × SDS loading buffer (Beyotime, P0015) for 10 min. The proteins of 30 μg were run in $8\%$, $10\%$, and $12\%$ gradient SDS-PAGE at 80 V for 30 min, then converted to 120 V for 60 min; afterward, they were transferred onto 0.45 μm PVDF membranes (Sigma, HVLP02500) under 200 mA for 1 h, blocked in $5\%$ milk (Sigma, 20-200) for 1.5 h, incubated in primary antibodies overnight at 4 °C, then incubated with secondary biotinylated antibodies for 2 h at room temperature. Proteins were detected with ECL (32132, Thermo Fisher, Carlsbad, CA, USA). The antibodies were listed as follows: HIF1α (BIOSS, bs-20399R), VEGFA (BIOSS, bs-0279R), PI3K (Proteintech, 60225-1-Ig, Rosemont, IL, USA), phospho-PI3K (Cell Signaling Technology, 4228, Danvers, MA, USA), AKT (BIOSS, bs-0115R), phospho-AKT(Cell Signaling Technology, 4060), PKCε (Abclonal, A4998, Woburn, MA, USA), ERK$\frac{1}{2}$ (Abclonal, A10613), phospho-ERK$\frac{1}{2}$ (Abclonal, AP0472), p-NFkB-p65 (Cell Signaling Technology, 3033), NFkB-p65 (Abclonal, A18210), BAX (Proteintech, 50599-2-Ig, Rosemont, IL, USA), BCL2 (Proteintech, 68103-1-Ig), β-tubulin (Proteintech, 10094-1-AP), anti-rabbit IgG, HRP-linked antibody (Cell Signaling Technology, 7074), and anti-mouse IgG, HRP-linked antibody (Cell Signaling Technology, 7076). ## 4.6. Statistical Analysis Data were analyzed by one-way analysis of variance (ANOVA) using GraphPad Prism 8.0.1 software. Statistical significance was set at $p \leq 0.05.$ Data are shown as mean ± SEM. ## 5. 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--- title: Glycosylation Analysis of Urinary Peptidome Highlights IGF2 Glycopeptides in Association with CKD authors: - Sonnal Lohia - Agnieszka Latosinska - Jerome Zoidakis - Manousos Makridakis - Harald Mischak - Griet Glorieux - Antonia Vlahou - Vera Jankowski journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048973 doi: 10.3390/ijms24065402 license: CC BY 4.0 --- # Glycosylation Analysis of Urinary Peptidome Highlights IGF2 Glycopeptides in Association with CKD ## Abstract Chronic kidney disease (CKD) is prevalent in $10\%$ of world’s adult population. The role of protein glycosylation in causal mechanisms of CKD progression is largely unknown. The aim of this study was to identify urinary O-linked glycopeptides in association to CKD for better characterization of CKD molecular manifestations. Urine samples from eight CKD and two healthy subjects were analyzed by CE-MS/MS and glycopeptides were identified by a specific software followed by manual inspection of the spectra. Distribution of the identified glycopeptides and their correlation with Age, eGFR and Albuminuria were evaluated in 3810 existing datasets. In total, 17 O-linked glycopeptides from 7 different proteins were identified, derived primarily from Insulin-like growth factor-II (IGF2). Glycosylation occurred at the surface exposed IGF2 Threonine 96 position. Three glycopeptides (DVStPPTVLPDNFPRYPVGKF, DVStPPTVLPDNFPRYPVG and DVStPPTVLPDNFPRYP) exhibited positive correlation with Age. The IGF2 glycopeptide (tPPTVLPDNFPRYP) showed a strong negative association with eGFR. These results suggest that with aging and deteriorating kidney function, alterations in IGF2 proteoforms take place, which may reflect changes in mature IGF2 protein. Further experiments corroborated this hypothesis as IGF2 increased plasma levels were observed in CKD patients. Protease predictions, considering also available transcriptomics data, suggest activation of cathepsin S with CKD, meriting further investigation. ## 1. Introduction Chronic Kidney Disease (CKD) is prevalent in $10\%$ of the world’s adult population and is increasingly considered as a silent epidemic, due to its eventual progression to end-stage renal disease (ESRD) [1,2]. CKD is defined by the presence of abnormalities in kidney function and/or structure, for a duration of three or more months. Three aspects: cause, glomerular filtration rate (GFR) and Albuminuria, are important in the classification of CKD [3], with the latter two factors (estimated GFR (eGFR) and levels of Albuminuria) also employed in the diagnosis of CKD [4,5]. However, these parameters have an increased diagnostic value in advanced disease stages, rendering treatment of CKD patients highly challenging. Despite its complex composition and unlike other biological fluids, urine is highly stable [6] consisting of proteins and peptides from glomerular filtration, shed epithelial cells (kidney and urinary tract), tubular and seminal secretions, and secreted exosomes [7,8,9]. Urinary proteomics studies have gained momentum in recent years, as they provide opportunities for a non-invasive biopsy. In the case of CKD, urine proteomics have highlighted biomarkers for disease diagnosis and progression; guiding therapeutic strategies [10,11]. Protein glycosylation is the most common post translational modification (PTM) [12]. A glycoproteomic study predominantly focuses on the identification of glycoproteins, glycan structures and their sites of attachment in the protein [13,14]. Irrespective of the tremendous advancements in MS/MS-based technologies and respective data analysis solutions, the identification of PTMs, especially glycosylation, continues to be taxing. This can be attributed mainly to the complex and heterogenous nature of glycan structures linked to either Asparagine (N-linked) or Serine/Threonine (O-linked) amino acid residues [15]. The importance of different types of glycosylation (N-linked and O-linked) has already been highlighted in numerous physiological and pathological mechanisms related to inflammation, angiogenesis and cancer [16,17,18]. Along these lines, this study was carried out under the hypothesis that characterization of glycopeptides in the urine can reveal pathological mechanisms in CKD. Urinary proteomics to date has identified more than 5000 proteins [19,20,21], while the analysis of urinary glycoproteins is still at its initial stage with the identified number being in the hundreds [22,23]. In the context of CKD, only a handful successful studies on urinary glycopeptidomics exist [24] in their vast majority, focusing on N-linked glycopeptides. Due to the availability of fragmentation knowledge on O-linked glycosylation, an in-depth characterization of O-linked glycans, their attachment sites and protein carriers is warranted. To the best of our knowledge, this is the first study that focuses on identifying “intact” urinary O-linked glycopeptides and exploring their association with CKD. Specifically, in this study, urinary glycopeptides were identified by a combination of Capillary Electrophoresis-Tandem Mass Spectrometry (CE-MS/MS) and glycopeptide-specific MS data analysis. The CE-MS technology presents many advantages including high resolution, fast separation, use of inexpensive and robust capillaries, compatibility with common volatile buffers and analytes, and a stable and constant flow, while allowing an easy coupling with Higher-energy C-trap dissociation MS (HCD-MS) for peptide sequencing [25,26,27,28]. The applied approach (summarized in Figure 1) targeted the identification of “intact” naturally occurring urinary glycopeptides in humans, along with information on the glycan structure, composition and attachment sites; with the ultimate aim to identify urinary O-linked glycopeptides in association to CKD and contribute to better characterization of its molecular manifestations. ## 2.1. Identification of Urinary Glycopeptides To identify naturally occurring urinary glycopeptides, urine samples from eight CKD patients and two healthy subjects were analyzed using CE-MS/MS. The MS/MS output datafiles were then processed by Proteome Discoverer 1.4 (the output of protein identifications is provided in Table S1 and peptide identifications in Table S2), that considered O-linked glycosylation as a variable modification, yielding a list of 120 possible glycopeptides. Further correlation of experimental and theoretically predicted CE migration times based on a regression equation described in detail by Zurbig et al. [ 29] yielded a shortlist of 65 possible glycopeptides which matched the predicted m/z and migration time location. The MS/MS spectra outputs of the resulting 65 possible glycopeptide peaks were further manually analyzed, including a comparison of the experimental and theoretical isotopic distributions of the glycopeptide peaks (examples are shown in Figure 2). This manual spectral analysis step resulted in a final list of 17 high confidence glycopeptide identifications (Table 1). As shown (Table 1), the 17 glycopeptides were generated from seven different glycoproteins: six out of seventeen glycopeptides were derived from Insulin-like growth factor II [IGF2], three glycopeptides originated from Vitamin K-dependent protein C [PROC], three from Inter-alpha-trypsin inhibitor heavy chain H1 [ITIH1], two from Fibrinogen alpha chain [FGA] and one glycopeptide each of CD99 antigen [CD99], Fibronectin [FN1] and Tumor necrosis factor receptor superfamily member 10D [TNFRSF10D]. The detection frequency and abundance of these glycopeptides was then investigated in the Human Urinary proteome database ($$n = 3810$$) [21,30,31], as also depicted in Table 1. ## 2.2. IGF2 Glycopeptides in Association to CKD As the IGF2 glycopeptides were represented at the highest frequency and abundance in comparison to the rest of the identified glycopeptides (Table 1), a more detailed analysis was pursued to investigate their relevance to CKD. Further investigation of the Human urinary proteome database [21,30,31] also revealed three non-glycosylated peptides of IGF2 previously identified (Table S3). Interestingly, all the detected IGF2 peptides belonged to the E-domain of the protein, as illustrated in Figure 3. Four IGF2 glycopeptides were observed in at least $10\%$ of subjects from the Human urinary proteome database (Table 1, first four rows). Given that this high frequency would allow for reliable statistical analyses, these four glycopeptides were considered for further investigation in association with CKD. A correlation of their abundance with clinical parameters (Age, eGFR and Albuminuria) was then performed in the extracted urinary peptide profiles from the database ($$n = 3810$$), as allowed per data availability. A detailed correlation report is provided in the Supplementary information (Table S4). As shown (Figure 4a), all peptides exhibited positive correlations with Age, with three of them also exhibiting negative correlations with eGFR based on Spearman’s rank correlation analysis. Associations with Albuminuria could also be occasionally observed suggesting collectively an interplay of all clinical parameters in the determination of peptide abundance. To better characterize this interplay, multiple linear regression analyses were also conducted (Table S4), with results being depicted in Figure 4b. As shown, 3 out of 4 glycopeptides (DVStPPTVLPDNFPRYPVGKF; DVStPPTVLPDNFPRYPVG and DVStPPTVLPDNFPRYP) retained an independent association to Age, with DVStPPTVLPDNFPRYPVG also retaining association to Albuminuria. Interestingly one of the peptides (tPPTVLPDNFPRYP) was significantly and independently associated with eGFR [β = −0.014, $p \leq 0.0001$]. To further enhance the validity of this observation related to the specific association of the glycopeptide (tPPTVLPDNFPRYP) with eGFR, correlation analysis of its abundance with eGFR was also performed, following stratification of the available cohort (urinary peptide profiles extracted from the database) in different age groups. In line to the multiple regression analysis, the glycopeptide tPPTVLPDNFPRYP retained a statistically significant and negative correlation with eGFR in five out of six age groups, 18–25 years [$p \leq 0.001$, Rho = −0.69], 41–50 years [$p \leq 0.001$, Rho = −0.43], 51–60 years [$p \leq 0.001$, Rho = −0.56], 61–70 years [$p \leq 0.001$, Rho = −0.56] and 71–100 years [$p \leq 0.001$, Rho = −0.54] with the other 3 glycopeptides showing no significant correlation with eGFR. These results for the peptide tPPTVLPDNFPRYP are also illustrated in Figure 5, where the data were fit using a generalized additive model (GAM), with the detailed correlation test report being provided in Table S4. Given the interesting abundance pattern of the tPPTVLPDNFPRYP glycopeptide, a further analysis was performed targeting its investigation per disease etiology. As summarized in Table 2, the glycopeptide (tPPTVLPDNFPRYP) abundance exhibited statistically significant and negative correlations with eGFR in IgAN, CKD, DKD, Nephroscelrosis, FSGS, tubular nephritis as well as healthy control datasets. For comparison, correlations could occasionally also be observed for two of the other glycopeptides (DVStPPTVLPDNFPRYPVGKF and DVStPPTVLPDNFPRYP), which given the combined results of the Spearman’s correlation test and multiple linear regression analysis (Figure 4), may be related to Age and/or Albuminuria correlations of these peptides, respectively. Along the same lines, the urinary IGF2 glycopeptide tPPTVLPDNFPRYP was found at a statistically significant increased abundance in CKD ($$n = 686$$) urinary peptide profiles in comparison to those of healthy controls ($$n = 229$$) (Figure 6). ## 2.3. Increased IGF2 Abundance in Plasma of CKD Patients The specific association of the urinary glycopeptide tPPTVLPDNFPRYP with eGFR generated the hypothesis that this may be reflective of increased IGF2 levels in CKD. To explore this hypothesis, a pilot study was conducted, targeting quantification of IGF2 protein in plasma. A total of 24 samples of well-matched CKD samples and controls were analyzed corresponding to eGFR < 30 mL/min/1.73 m2 ($$n = 12$$) and eGFR > 90 mL/min/1.73 m2 ($$n = 12$$). Figure 7 shows graphically the results of the enzyme-linked immunosorbent assay (ELISA) analysis supporting a statistically significant increase in plasma levels of the IGF2 protein in the eGFR < 30 mL/min/1.73 m2 group when compared with the eGFR > 90 mL/min/1.73 m2 group [$t = 2.1676$, df = 20.294, $$p \leq 0.04225$$]. The detailed ELISA analysis report is presented (Table S5). ## 2.4. Protease Prediction To further predict proteases potentially involved in the cleavage of the CKD-specific IGF2 glycopeptide tPPTVLPDNFPRYP (at Serine 95-glycosylated Threonine 96 site), the Proteasix tool was used. The analysis yielded a list of six endopeptidases putatively associated with the cleavage of this peptide at N′ terminal and two endopeptidases responsible for cleavage at C′ terminal (Table S4). Proteases cleaving at the N′ terminal belonged to the Cathepsin (CTSL, CTSS, CTSK) family, Calpains (CAPN1, CAPN2) and meprin A subunit alpha (MEP1A). Neprilysin protease or membrane metallo-endopeptidase (MME) and matrix metalloproteinase (MMP) were predicted to cleave the C′ terminal of the glycopeptide. Given the specific association of only the IGF2 glycopeptide tPPTVLPDNFPRYP with CKD, predictions that were shared amongst the different IGF2 glycopeptides were disregarded, highlighting Cathepsins, as specifically cleaving glycopeptide tPPTVLPDNFPRYP at the N terminus. Of note, statistically significant increased expression of Cathepsin S (CTSS) in CKD in comparison to Normal Kidney was observed based on transcriptomic datasets as available from the Nephroseq database [$$p \leq 0.002$$, t-test = 5.797, fold change = 12.099; Table S6]. ## 3. Discussion Chronic kidney disease (CKD) is the twelfth most common cause of mortality in the adult population worldwide, with a projection to raise in rank to the fifth position by 2040 [32]. Despite glycosylation being considered a frequent protein modification, its role in CKD is largely unknown. This may be attributed to the highly complex and heterogeneous nature of the glycan structures, especially in the case of O-linked glycosylation, where no evident consensus motif exists. Aiming to fill this gap with this study, naturally occurring “O-linked” urinary glycopeptides were investigated and their association with clinical parameters (Age, eGFR and Albuminuria) relevant to the diagnosis of CKD was established. In total, 17 glycopeptides deriving from 7 glycoproteins were identified at high confidence (Table 1). Here, IGF2 derived glycopeptides were observed at the highest frequency and abundance, followed by glycopeptides of PROC, ITIH1 and FGA. The generated glycoforms per protein in the clear majority of cases belonged to consistently similar regions of the protein chain with the different glycopeptides per protein varying amongst each other by one to three amino acids. Along these lines, 5 out of 6 IGF2 glycopeptides exhibited the presence of a glycan structure at the same Threonine 96 position, rendering the latter a highly confident site of glycosylation in the IGF2 protein chain. Similarly, in the case of FGA, a glycosylation site at Serine 609; for PROC, on Threonine 19; and for ITIH1 on Serine 651 could consistently be observed. In the case of IGF2, glycoforms of the same peptide DVSTPPTVLPDNFPRYPVGKF (IGF2) with different glycan positions (S3 and T4) and glycan structures [Hex[1]HexNAc[1]NeuAc[1] for S3 and Hex[1]HexNAc[1]NeuAc[2] for T4] were also observed. The identification of different glycoforms of the same glycopeptide confirms the complex microheterogeneity of O-linked glycosylation and the need for more site-specific glycosylation studies [33]. The narrow CE-migration time (mins) frame observed in between the glycoforms of IGF2 glycoprotein (Table 1), may also be reflective of this O-linked microheterogeneity. The reliable identification of these glycoforms can be associated with the fact that CE-MS/MS recognizes features based on their peptide composition (amino acid sequences) and not the glycan structure. In addition, peptides exhibit an affinity to protons, which further results in identification of [M + nH]n+ molecular ions of the glycopeptides [34]. All the glycoforms in this study were identified as singly and/or doubly charged and their isotopic distribution were compared to the theoretical distribution for validation. Analysis of O-linked “intact” glycopeptides, has also been conducted by Belczacka et al. and Halim et al. [ 23,35]. In an analogous manner, these authors also focused on identification of intact urinary glycopeptides (both O-linked and N-linked) in healthy [23] or in association to cancer [35]. Some of the glycosylation sites highlighted in our study have also been reported in these and other studies: Threonine 19 in PROC protein was also identified as a highly confident O-linked glycosylation site in Belczacka et al. and Halim et al. [ 23,35]. Along the same lines, Darula et al. [ 36] and Campos et al. [ 37] detected CD99 as an O-linked glycoprotein with Threonine 41 as the glycan attachment site, a finding which was also supported in our study. N- and O-linked glycosylation are widely stated in the literature for FN1 [38] and TNFRSF10D [37] proteins; nevertheless, no previous reports for the presently shown O-linked glycosylation sites at Threonine 19 and Threonine 69, respectively, exist. The IGF2 peptides exhibited high frequency, high abundance, and predominance in comparison to glycopeptides originating from other urinary proteins, which allowed for their further statistical analysis. Translated IGF2 is produced in the endoplasmic reticulum, in the pre-pro-protein form consisting of a 180 amino acid chain, divided into 6 domains (A to E) plus a 24 amino acid signaling peptide. In the post-translational process of pre-pro- IGF2, the E-domain undergoes O-linked glycosylation in 12 plausible sites, by addition of N-acetyl galactosamine residues, and in the trans-Golgi compartment sialic acid side chains are added to N-acetyl galactosamine. Glycosylation stimulates the next step in the processing of pro-IGF2, where the E-domain is cleaved by PCSK proteases, yielding the “mature IGF2” consisting of 67 amino acids and a molecular weight of 7.5 kDa. This (mature IGF2) is then released in the blood stream by exocytosis, where its pleiotropic roles associated with growth and development are carried out in an auto-/para-/endocrine mode [39,40,41,42]. Based on the abovementioned post-translational processing, numerous proteo-forms of IGF2, differing in size and/or glycosylation pattern exist, of varied affinity to the same targets (IGF2 or IGF1 receptors, insulin receptors). In a healthy adult, IGF2 (or “mature” IGF2) devoid of any glycosylation is easily degradable in the bloodstream [43]. Several IGF binding proteins (IGFBPs) exist in the extracellular matrix, forming IGF2 binary or ternary structures (IGF2-IGFBPs-acid labile subunit (ALS)) to increase stability of the protein and regulate its concentration in the bloodstream, prior to its degradation that occurs upon binding with the IGF2 receptor (IGF2R). O-linked glycosylation has been linked to inhibition in the formation of such ternary protein complexes, resulting in increased affinity for IGF1R, IR-A and IR-B [44,45]. Collectively, it is suggested that the “mature IGF2” protein form, important for maintaining homeostasis in adults, is most likely present in inactive ternary complexes, while detectable IGF2 proteins are in their vast majority proteo-forms of IGF2 consisting of O-linked glycosylation [46,47]. Along these lines in our study, all identified IGF2 peptides belonged to E-domain of IGF2, i.e., 92–180 aa, outside the “mature IGF2” (25–91 aa). Of note, and in agreement to the abovementioned evidence, in the database, only three unglycosylated urinary peptides of IGF2 could be identified, which were observed at significantly lower abundance and frequency in comparison to the glycosylated forms (Table S3). Correlation analysis indicated moderate to strong [Rho = +0.27 (mean)], positive and statistically significant [$p \leq 0.0001$] correlation of all four IGF2 glycosylated peptides with Age with three of the glycopeptides (DVStPPTVLPDNFPRYPVGKF, DVStPPTVLPDNFPRYPVG and DVStPPTVLPDNFPRYP) maintaining this association after multiple linear regression analysis. This result enhances the biological relevance of IGF2 glycoforms and a need for their individual study. Importantly, one of the peptides (tPPTVLPDNFPRYP), showed strong association with eGFR independent of Age and Albuminuria, as supported by all applied analyses (Spearman’s rank correlation, multiple linear regression analysis and Wilcoxon rank sum test). Collectively, this finding indicates that with aging and deteriorating kidney function, alterations in IGF2 proteoforms take place, which may be reflective of respective changes in the mature IGF2 protein abundance and function as well as associated protease activity. Our results corroborate to some extent this hypothesis, as increased IGF2 plasma levels were observed in CKD patients versus the controls. In addition, the protease predictions, also combined with available transcriptomics data, suggest an activation of cathepsin S with CKD, meriting further investigation through future studies. Increased levels of IGF2 in comparison to controls, in multiple diseases including cancer and diabetes have been detected [47]. In brief, associations of increased serum levels of IGF2 with hypoglycaemia [48] as well as of total and free IGF2 (defined as bound or unbound fractions of IGF2 with IGFBPs, respectively) with obesity accompanying Type 2 Diabetes mellitus (T2DM) have been suggested [49]. The E-domain of the IGF2 protein, also known as preptin has been further associated with obesity [50] and T2DM [51]. Interestingly, the upregulation of IGF2 in diabetic nephropathy (DKD) has also been proposed following analysis of renal biopsies from normal ($$n = 9$$) and early T2DM patients with ($$n = 9$$) or without ($$n = 11$$) histopathological characteristics of DKD [52]. In our study, associations to different disease etiologies including DKD could be observed for the tPPTVLPDNFPRYP glycopeptide. As suggested [53], any disturbance occurring in the somatotropic growth hormone axis can contribute to causal mechanisms in CKD. Interestingly, Fan et al. [ 54] recently reported a metabolic pathway that may also explain the increase of IGF2 in CKD. The study highlighted the important role of an endoplasmic reticulum protein RTN3 in the IGF2-JAK2-STAT3 pathway, where the reduction in RTN3 in the kidneys results in increased transcription of IGF2, which in turn activates the JAK2-STAT3 pathway, ultimately inducing kidney fibrosis and CKD. This pathway was proposed using animal (mice) models and cell lines, hence its translatability to humans is pending. In summary, with this study naturally occurring “intact” urinary glycopeptides were identified highlighting IGF2 O-glycosylated proteoforms correlating with pathophysiological parameters, such as aging and kidney function, and apparently reflecting at least in part changes in IGF2 levels. The ability to probe these features simultaneously, i.e., site occupancy and O-glycan microheterogeneity, offers a unique opportunity towards untangling and identifying isoform-associated unique functions (similar to the observed specific association of the IGF2 glycopeptide tPPTVLPDNFPRYP with CKD). The results thus open up multiple questions meriting further investigation, including understanding the mechanism of this IGF2 peptide specific association and likely proteolytic cleavage by Cathepsins and their overall impact on CKD progression. ## 4.1. Study Population–Urine Samples For identification of naturally occurring intact urinary glycopeptides, urine samples from eight CKD patients and two healthy volunteers were analyzed by CE-MS/MS, under ethics-approved protocols (Hannover Medical School, Ref. No. 3115-2016). The respective clinical information is presented in Table 3. For the association of the identified glycopeptides with Age, eGFR and Albuminuria, anonymized CE-MS urinary peptide profiles from the Human Urinary Proteome database [21,30,31] were considered. These peptide datasets generated in previous studies [55,56,57,58,59,60,61] were selected based on availability of information on their diagnosis (control or CKD etiology), Age, eGFR and Albuminuria values. The distribution of disease etiology for the retrieved 3810 datasets is presented in Table 4. ## 4.2. Sample Preparation Standard operating procedures (SOPs) were followed for urine sample collection, storage and further processing, as described in previous publications [62,63,64]. Briefly, 700 µL of an aqueous solution consisting of 2 M urea, 10 mM NH4OH and $0.02\%$ sodium dodecyl sulfate (SDS) was used to dilute 700 µL of the thawed urine sample. To discard high molecular weight proteins, the sample was subjected to ultrafiltration using a Centristat (20 kDa molecular mass cut-off) centrifugal device (Sartorius, Göttingen, Germany) and centrifugation speed was set to 3000 rcf, to obtain approximately 1.1 mL filtrate. To eliminate urea, salts and electrolytes from the filtrate, a desalting process was carried out utilizing a PD-10 column gel (GE Healthcare Bio Sciences, Uppsala, Sweden) which was pre-equilibrated with $0.01\%$ NH4OH in HPLC-grade water. The processed urine samples were then lyophilized and stored at 4 °C. Prior to the CE-MS/MS analysis, the samples were reconstituted in 10 µL of HPLC-grade water. ## 4.3. CE-MS/MS Analysis The CE-MS/MS instrumental set up comprised a P/ACE MDQ capillary electrophoresis system (Beckman Coulter, Fullerton, CA, USA) connected to an Orbitrap Velos Fourier Transform (FT) MS (Thermo Finnigan, Bremen, Germany). The protocol for CE-MS/MS analysis and peptide sequencing based on data-dependent high ($40\%$) energy collision dissociation (HCD) for the top 20 ions, has been previously described [65]. Briefly, Proxeon nano spray fitted with Agilent ESI sprayer (operated in positive ion mode) was utilized to ionize 230 µL-aliquot (1:50) of the processed urine sample. The ionization occurred at a voltage of 3.4 kV with 275 °C as the capillary temperature. Operating conditions were set to MS/MS mode that scanned from 350 to 1500 amu and triggered sequencing at a set threshold of 5000 counts. Ion resolution was 60,000 for MS1 and 7500 for MS2, with a detection limit of 0.05–0.2 fmol. Finally, full-scan MS spectra were acquired in the range of 300–2000 m/z, depicting the sequentially isolated fragmentation ions. The CE-MS/MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD039829 and 10.6019/PXD039829. ## 4.4. Glycopeptide Analysis Identification of glycopeptides was carried out using Proteome Discoverer 1.4 software. The SEQUEST search engine was applied for analysis of the CE-MS/MS raw files against the entire reviewed non-redundant human database downloaded as FASTA from UniProt (May 2022). The search parameters were set to no enzyme, HCD as the activation type, precursor mass tolerance at 10 ppm and fragment mass tolerance at 0.02 Da. In addition, only 2 maximum missed cleavage sites were allowed along with the peptide length set to 6–144 amino acids. There were no fixed modifications selected, while the variable modifications included oxidation of proline (+15.995 Da), Hex[1]HexNAc[1]NeuAc[1] of serine, threonine (+656.228 Da) and Hex[1]HexNAc[1]NeuAc[2] of serine, threonine (+947.323 Da). The latter two accounts for the most commonly occurring structures of O-linked glycans [66,67,68]. Three dynamic modifications per peptide and a false discovery rate of $1\%$ for peptide identifications were allowed. To eliminate false-positive associations, a selection process based upon correlation between the experimental and theoretically predicted CE-migration time at the working pH of 2.2 of the identified peptides was applied, as described previously [29]. Each of the MS/MS fragment spectra of the resulting glycopeptide peaks were further analyzed manually, including a comparison of the theoretical and observed isotopic distributions. ## 4.5. Study Population–Plasma Samples Plasma samples from CKD patients from the registered Biobank Kidney Ghent (BB190096) at the Ghent University Hospital in Belgium were analyzed under the ethics-approved protocol number (EC$\frac{2010}{033}$; Ghent University Hospital Ethics Committee). Two well-matched groups of 12 samples each were formed corresponding to eGFR > 90 mL/min/1.73 m2 and CKD patients with eGFR < 30 mL/min/1.73 m2, as shown in Table 5. EDTA plasma samples were collected under SOPs. In brief, blood samples were centrifuged at 2095× g for 10 min at room temperature, within 30 min after collection. Samples were then aliquoted into labelled cryovials and stored in an upright position at −80 °C. ## 4.6. ELISA Assay The quantification of Insulin-like growth factor-II (IGF2) protein (7.5 kDa) in plasma samples was performed by enzyme-linked immunosorbent assay (ELISA) using the Quantikine Human IGF-2 ELISA Kit (#DG200, R&D Systems, Minneapolis, MN, USA) as per the manufacturer’s instructions using 10 μL of plasma. ## 4.7. Statistical Analysis Statistical analysis and results presented in this manuscript are based on R programming (R version 3.6.0 with IDE: R Studio Version 1.2.5, Boston, MA, USA). A peptide frequency threshold of $10\%$ was applied, as a pre-requisite for statistical analysis. The peptide intensity values were Log10 transformed and entries with missing/no values were eliminated. Spearman’s rank correlation test and multiple linear regression analysis were used to define associations between abundance of the identified glycopeptides and clinical parameters (Age, eGFR and Albuminuria). In R, a correlation matrix was created with rcorr function of Hmisc package, while the regression analyses were performed with cor.test and lm functions of stats package. Matching for age and sex was performed in R using the MatchIt function where a 1:1 ratio was selected with the ‘nearest neighbor’ method. Wilcoxon Rank Sum test was applied to compare the glycopeptide intensities between groups (healthy subjects, $$n = 229$$ and CKD patients, $$n = 686$$). Welch two sample t-test was applied to compare the IGF2 protein levels between the two eGFR groups ($$n = 12$$ per group of plasma samples). Box and Whisker plots were generated in R using the ggplot2 package and colors in the correlation plot developed using a generalized additive model (GAM) were depicted with the viridis package. ## 4.8. Proteasix Analysis An open-source tool “Proteasix” (http://www.proteasix.org, accessed on 3 October 2022) [69,70] was used to predict proteases involved in the cleavage of the identified glycopeptides. Briefly, Proteasix exploits information from databases such as UniProt Knowledgebase, MEROPs, CutDB and literature. In this study, we employed the “Predicted” tool of Proteasix for increased coverage in proteolysis information, using the default search parameters. ## 4.9. NephroSeq Analysis The transcriptomics data analysis tool Nephroseq v4 (www.nephroseq.org, accessed on 23 December 2022) was utilized to investigate the expression of predicted proteases in existing human transcriptomics datasets. Initially, CKD datasets were identified using the primary filter: Group > chronic kidney disease. The mRNA expression levels of proteases in CKD vs. Normal Kidney were then searched in the available datasets. 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--- title: Muscle Lipid Oxidation Is Not Affected by Obstructive Sleep Apnea in Diabetes and Healthy Subjects authors: - Zuzana Lattova - Lucie Slovakova - Andrea Plihalova - Jan Gojda - Moustafa Elkalaf - Katerina Westlake - Jan Polak journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048979 doi: 10.3390/ijms24065308 license: CC BY 4.0 --- # Muscle Lipid Oxidation Is Not Affected by Obstructive Sleep Apnea in Diabetes and Healthy Subjects ## Abstract The molecular mechanisms linking obstructive sleep apnea (OSA) with type 2 diabetes mellitus (T2DM) remain unclear. This study investigated the effect of OSA on skeletal muscle lipid oxidation in nondiabetic controls and in type 2 diabetes (T2DM) patients. Forty-four participants matched for age and adiposity were enrolled: nondiabetic controls (control, $$n = 14$$), nondiabetic patients with severe OSA (OSA, $$n = 9$$), T2DM patients with no OSA (T2DM, $$n = 10$$), and T2DM patients with severe OSA (T2DM + OSA, $$n = 11$$). A skeletal muscle biopsy was performed; gene and protein expressions were determined and lipid oxidation was analyzed. An intravenous glucose tolerance test was performed to investigate glucose homeostasis. No differences in lipid oxidation (178.2 ± 57.1, 161.7 ± 22.4, 169.3 ± 50.9, and 140.0 ± 24.1 pmol/min/mg for control, OSA, T2DM, and T2DM+OSA, respectively; $p \leq 0.05$) or gene and protein expressions were observed between the groups. The disposition index, acute insulin response to glucose, insulin resistance, plasma insulin, glucose, and HBA1C progressively worsened in the following order: control, OSA, T2DM, and T2DM + OSA (p for trend <0.05). No association was observed between the muscle lipid oxidation and the glucose metabolism variables. We conclude that severe OSA is not associated with reduced muscle lipid oxidation and that metabolic derangements in OSA are not mediated through impaired muscle lipid oxidation. ## 1. Introduction Obstructive sleep apnea (OSA) syndrome is an often-neglected disorder with a prevalence of 5–$15\%$ in the general population of adults that increases to 50–$80\%$ in type 2 diabetes (T2DM) and obese patients [1,2], increasing the cardiovascular and all-cause mortality of affected individuals [3,4]. Based on the clinical features of transient upper airway collapse during sleep, causing repetitive oxyhemoglobin desaturations (intermittent tissue hypoxemia) and sleep fragmentation [5], cross-sectional and prospective studies have identified OSA as a risk factor for the development of glucose intolerance, insulin resistance, and T2DM that was independent of other established risk factors (e.g., age, genetic factors, obesity, and physical inactivity) [6,7,8]. The causal link between OSA and T2DM remains under active investigation, employing in vitro, animal, and human experiments. Despite the methodological challenges hampering in vitro hypoxic studies [9], independent groups have reported using intermittent hypoxia (mimicking the oxygen desaturations present in OSA patients) to reduce insulin signaling in hepatocytes [10] and adipocytes [11], which leads to reduced glucose uptake and augmented lipolysis [12]. Exposing rodents to intermittent hypoxia caused insulin resistance, glucose intolerance, and β-cell dysfunction, and led to increased hepatic glucose output, hyperglycemia, and increased adipose tissue lipolysis in mice [13,14,15,16,17,18,19]. Identical exposure also induced hyperglycemia and a reduced expression of muscle glucose transporters in rats [20,21]. Furthermore, experiments in healthy humans have demonstrated insulin resistance, impaired β-cell function, and elevated glucose levels after acute exposure to intermittent hypoxia [22,23]. Suggested molecular and endocrine mechanisms that could alter the glucose homeostasis in OSA include increased sympathetic activity, inflammatory pathway stimulation, reactive oxygen species generation, elevated plasma corticoid or endothelin-1 levels, or a modified adipokine secretion profile [8,24]. Additionally, an important role for hypoxia-inducible factors (HIF) has also been identified and summarized [25]. More recently, increased levels of circulating free fatty acids (FFAs) have attracted attention as a potential link between hypoxia and impaired glucose metabolism [26,27]. This is due to their ability to impair the glucose uptake and metabolism in muscle through the inhibition of critical glycolytic enzymes [28,29]; i.e., to induce insulin resistance in the liver through modified intracellular signaling and altered gene expression (resulting in excessive hepatic glucose output) [30,31,32], as well as to decrease insulin secretion and stimulate apoptosis in β cells [33,34]. The balance between FFA release into the circulation (adipose tissue lipolysis) and its uptake in the peripheral tissues (oxidation and/or storage) determines the actual plasma FFA concentration [35,36,37]. Previous in vitro [12], rodent [19], and human [26,38] studies have shown that low pericellular oxygen levels upregulate lipolysis, which is also upregulated in OSA patients. In contrast, the crucial factor of net plasma FFA balance and its uptake and utilization in the peripheral tissues under intermittent hypoxia remains to be elucidated. This study investigated whether OSA modifies skeletal muscle oxidation in otherwise healthy participants and T2DM patients. To achieve this goal, we thoroughly characterized a host of metabolic features (using an intravenous glucose tolerance test) and investigated lipid oxidation in muscle biopsies obtained from nondiabetic as well as T2DM patients diagnosed with severe OSA. We then compared them with matched nondiabetic and T2DM controls (with absent or mild OSA). The outcomes of this study could provide a basis for pharmacological or nonpharmacological (exercise) interventions targeting muscle lipid oxidation for treating the metabolic complications associated with OSA. ## 2.1. Anthropometric, Biochemical, and Sleep Characteristics A summary of the basic demographic, anthropometric, metabolic, and sleep characteristics of all the participants is presented in Table 1. The data show successful age, BMI, and adiposity matching between the groups. As expected from the recruitment protocol, the groups affected by severe OSA showed higher indices of sleep-disordered breathing e.g. apnea-hypopnea index (AHI) or oxygen desaturation index (ODI) and spent more time in hypoxia during the night than did the participants without OSA. The presence of OSA worsened whole-body glucose homeostasis, expressed as intravenous glucose tolerance test-derived disposition index (DI), by $39\%$ and $51\%$ in nondiabetic and T2DM diabetes patients, respectively. Progressive worsening of the metabolic variables including DI, acute response to insulin (AIRg), insulin resistance, HOMA-IR (homeostatic model assessment for insulin resistance), fasting insulin, fasting glucose, and HBA1C (glycated hemoglobin) was observed (p for trend <0.05, mixed-model ANOVA). No differences in the plasma FFA levels were observed between the groups. ## 2.2. Oxygen Consumption Rate and Substrate Utilization in Skeletal Muscle Biopsy To investigate the ability of the skeletal muscle cells to utilize palmitate as an energy source, quadricep muscle biopsies were assessed ex vivo with added energy substrates using direct respirometry. As shown in Figure 1A, the spontaneous (basal) O2 consumption rate (OCR) did not differ between the groups. Subsequently, palmitoyl carnitine was added to the incubation buffer to quantify palmitate-induced respiration. Although the addition of the palmitate stimulated respiration by $52\%$, $38\%$, $30\%$, and $56\%$ in control (117.3 ± 33.3 vs. 178.2 ± 57.1 pmol/min/mg, $p \leq 0.05$), OSA (117.5 ± 16.1 vs. 161.7 ± 22.4 pmol/min/mg, $p \leq 0.05$), T2DM (130.5 ± 45.5 vs. 169.3 ± 50.9 pmol/min/mg, $p \leq 0.05$), and T2DM + OSA (89.7 ± 14.3 vs. 140.0 ± 24.1 pmol/min/mg, $p \leq 0.05$), respectively, no differences in OCR in response to palmitate administration were observed between the groups (ANOVA, $p \leq 0.05$) (Figure 1B). Similar responses were demonstrated after the administration of succinate, which increased oxygen consumption by $116\%$, $172\%$, $137\%$, and $80\%$ in control (178.2 ± 57.1 vs. 385.5 ± 140 pmol/min/mg, $p \leq 0.05$), OSA (161.7 ± 22.4 vs. 441.1 ± 51.0 pmol/min/mg, $p \leq 0.05$), T2DM (169.3 ± 50.9 vs. 400.5 ± 140.1 pmol/min/mg, $p \leq 0.05$), and T2DM + OSA (140.0 ± 24.1 vs. 251.4 ± 39.1 pmol/min/mg, $p \leq 0.05$), respectively; however, the magnitude of this response was the same between the groups (ANOVA, $p \leq 0.05$) (Figure 1C). While palmitate-induced respiration was not associated with any of the analyzed anthropometric or biochemical variables, succinate-induced respiration was positively associated with whole-body CO2 production ($r = 0.361$, $p \leq 0.05$) and, to a lesser extent, with oxygen consumption ($r = 0.309$, $$p \leq 0.063$$) and resting metabolic rate ($r = 0.315$, $$p \leq 0.058$$) as determined with indirect calorimetry. ## 2.3. Protein Expression The protein expression of the fatty acid plasma membrane and intracellular transporters (fatty acid transport protein 4, FATP4 and platelet glycoprotein 4, CD36) as well as the critical regulator of fatty acid oxidation in the mitochondria (carnitine-palmitoyl transferase I, CPT1) were determined. Protein expression of FATP4 was not affected by OSA in nondiabetic persons (ratio of FATP4 to β-tubulin band intensity: 3.8 ± 1.1 vs. 4.1 ± 1.9, NS) or in diabetic patients (ratio of FATP4 to β-tubulin band intensity: 2.1 ± 0.9 vs. 4.8 ± 2.0, NS). Similarly, the protein expression of CD36 and CPT1 was stable across the control, OSA, T2DM, and T2DM + OSA groups (ratio of CD36 to β-tubulin band intensity: 1.4 ± 0.4, 2.1 ± 0.7, 1.4 ± 0.3, and 2.4 ± 0.8, respectively, all were NS; the ratio of CPT1 to β-tubulin band intensity: 0.3 ± 0.1, 0.4 ± 0.1, 0.3 ± 0.2, and 0.7 ± 0.3, respectively, all were NS) (Figure 2A–C). The severity of hypoxia, expressed as time spent sleeping with hemoglobin saturation under $90\%$ or $85\%$, was positively associated with protein expression of CD36 ($r = 0.328$ and $r = 0.321$, respectively, $p \leq 0.05$). FATP4 expression was negatively associated with insulin resistance, expressed as the HOMA-IR index (r = −0.335, $p \leq 0.05$). Additionally, the protein expression of CPT1 correlated with the protein expression of FATP4 ($r = 0.519$, $p \leq 0.001$). ## 2.4. Gene Expression The gene expression analysis of the skeletal muscle biopsies focused on the genes coding for the FATP4, CD36, and CPT1 proteins. No differences between the groups were detected in the relative gene expression of [1] FATP4 (0.86 ± 0.06, 0.8 ± 0.07, 0.82 ± 0.05, and 0.79 ± 0.03 for control, OSA, T2DM, and T2DM + OSA groups, respectively, all $p \leq 0.05$), [2] CD36 (41.6 ± 3.1, 40.1 ± 4.0, 39.1 ± 2.7, and 36.3 ± 3.4, for control, OSA, T2DM, and T2DM + OSA groups, respectively, all $p \leq 0.05$), and [3] CPT1 (0.076 ± 0.007, 0.070 ± 0.008, 0.085 ± 0.010, and 0.080 ± 0.006, for the control, OSA, T2DM, and T2DM + OSA groups, respectively, all $p \leq 0.05$). CPT1 expression was negatively associated with Sg (r = −0.169, $p \leq 0.05$) and GEZI (r = −0,237, $p \leq 0.05$), while no association was observed between the gene expressions of FATP4, CD36, or CPT1 and the anthropometric, sleep-related, and biochemical variables. The results are shown in Figure 3A–C. ## 3. Discussion The present study investigated whether OSA syndrome affects skeletal muscle lipid oxidation in matched healthy controls and T2DM patients and is associated with whole-body glucose metabolism parameters. An ex vivo analysis of palmitate oxidation as well as the protein and gene expressions of the key players in fatty acid transport/metabolism (FATP4, CD36, and CPT1) in muscle biopsies demonstrated unequivocally no effect of OSA on lipid utilization or the expression of crucial related proteins. Similarly, no association between muscle lipid oxidation and whole-body insulin sensitivity, insulin secretion, or DI was observed, although the FATP4 protein was negatively associated with insulin resistance (HOMA-IR index). The molecular mechanisms linking OSA with impaired glucose metabolism and increased risk of T2DM remain only partially elucidated [8]. However, elevated plasma FFA levels have attracted attention owing to their demonstrated role in the pathogenesis of insulin resistance and β-cell dysfunction [39,40,41]. Additionally, studies have demonstrated elevated plasma FFA in patients with OSA [38,42,43], particularly during the night (sleep) hours. Considering adipose tissue lipolysis (the source of circulating FFA) and skeletal muscle FFA utilization as the key factors determining fasting FFA levels (with minor contributions from liver FFA metabolism and FFA oxidation by other organs), this study focused on muscle lipid oxidation as a potential mechanism contributing to elevated plasma FFA levels and, thus, metabolic dysregulation. Impaired lipid oxidation in muscle cells could lead to elevated FFA, resulting in an accumulation of intracellular lipid-derived molecules (e.g., ceramides, phospholipids, and diacylglycerol) that have potent signaling properties and could eventually lead to muscle insulin resistance. The trio of investigated molecules, CD36, FATP4, and CPT1, represent key players in fatty acid transport from the extracellular to the intracellular space (regulated mainly by CD36 and FATP4) and subsequently in FFA transport to the mitochondria for oxidation (secured by CPT1). The dominant role of CD36 in FFA transport can be demonstrated by the reduced transmembrane FFA transport after chemical CD36 inhibition [44,45]; unfortunately, much less is currently known about the role and significance of FATP4 in the transport of FFA across the sarcolemma [46]. Furthermore, CD36 has been suggested to work in concert with CPT1 in mediating mitochondrial FFA transport/oxidation. Importantly, obesity, insulin resistance, and type 2 diabetes [47] have been associated with increased FFA muscle plasma membrane transport and elevated CD36 levels [47,48]. They are also combined with reduced mitochondrial FFA oxidation [28,49,50], ultimately leading to increased accumulation of lipids in the muscle tissue. Hence, an overexpression of CPT1 in muscle improved the high-fat-diet-induced insulin resistance in rats [51,52]. For thorough reviews of the key players in muscle FFA transport and their consequences, readers are referred to [52,53,54]. The unique feature of the present study is that all the groups were matched for adiposity-related variables and age, which enabled the assessment of the contribution of OSA to glucose metabolism impairment without obvious confounding factors. Despite striking differences in glucose metabolism indices, time spent in hypoxia, and the number of apneic/hypopneic episodes, no differences in skeletal muscle palmitate oxidation capacity were observed between the patients with and without severe OSA, which was further corroborated by the unchanged protein and gene expressions of the essential proteins involved in FFA cellular transport and oxidation, i.e., CD36, FAPT4, and CPT1. Our data suggest that lipid oxidation in the skeletal muscle is not affected by OSA in humans and is not a significant determinant of impaired whole-body glucose metabolism associated with OSA. This conclusion does not conflict with previous in vitro studies reporting decreased FFA uptake and reduced FATP4 and CD36 protein expression after exposure to prolonged severe hypoxia [55] ($1\%$ O2 for multiple days). Muscle tissue O2 levels during severe OSA were shown to reach ~25 mm Hg (~$5\%$) in a mouse model of OSA [56], which is considerably milder hypoxia than that used in in vitro studies. Similarly, diminished FFA oxidation in muscle was observed as a consequence of environmental hypoxia (high-altitude exposure) [57]; however, it should be noted that the hemoglobin desaturation at high altitude is greater (e.g., $54\%$ at an altitude of 8400 m [58]) and lasts significantly longer (days vs. minutes) than that used in the clinical setting of OSA [59,60]. Our results complement a recent study showing (in agreement with the present study) that OSA worsens glucose homeostasis. However, other authors also observed increased intra- and extramyocellular lipid content in skeletal muscle [61]. An increased triglyceride accumulation and modified lipidomic profile in myocytes after hypoxic exposure has been documented in vitro, presumably partly due to de novo lipogenesis [59,60]. These studies suggest a hypothetical picture of unchanged intracellular lipid oxidation combined with increased lipid synthesis as a consequence of hypoxic exposure. Further studies are needed to address and quantify in detail the plasma membrane FFA transport under hypoxic conditions because extrapolations from gene/protein data on the expression of essential protein transporters are rather imprecise. Rapid changes in plasma FFA levels in response to hypoxia/reoxygenation have been described in chronic obstructive pulmonary disease patients [27] and after oxygen administration in OSA patients [43]. Adipose tissue lipolysis, as well as FFA transport and utilization, may contribute to these rapid changes in plasma FFA levels. Adipose tissue lipolysis is higher in patients with OSA [38] and can be induced by exposure to hypoxia [12,19]. Although the timeline of changes in adipose tissue lipolysis and muscle FFA oxidation remains unclear, this study, combined with previously published data [38], demonstrates that increased adipose tissue lipolysis persists for at least several hours after awakening. In comparison, muscle lipid oxidation is not affected during comparable time periods in those with severe OSA because the tissue has already recovered from the hypoxia-induced suppression of lipid oxidation. This explanation is plausible for studies showing a rapid clearance of plasma FFA with a half-life of 3–4 min [62]; as reported by other researchers [63] and observed in our study, the explanation may also be plausible for observations of unchanged fasting FFA levels across a spectrum of OSA severity [63]. Furthermore, indirect calorimetry measurements performed shortly after awakening suggest an association between reduced lipid utilization and OSA severity [64]; however, whether muscle lipid oxidation is affected during sleep in OSA patients remains to be determined. Such a determination would require isotope techniques and a consideration of their limitations [62]. Sorting out the relative contribution of lipolysis to lipid oxidation has practical and important implications for the design of pharmacological interventions in OSA. A significant proportion of OSA patients do not tolerate the first-choice treatment option (i.e., continuous positive airway pressure therapy); additionally, these patients may be contraindicated for surgical treatment due to their health status [65,66,67]. These patients would strongly benefit from targeted pharmacological treatment, as demonstrated by the improvements in glucose metabolism after lipolysis inhibition in a mouse model of OSA [19]. Several limitations of the present study should be considered when interpreting and extrapolating the results. First, the study used muscle biopsies and analyzed lipid utilization and gene/protein expression ex vivo, without the (patho)physiological milieu of complex neuroendocrine regulation. However, muscle biopsies respond to known metabolic stimuli by increasing respiration in an ex vivo environment, and this method has been extensively used [68,69,70]. Notably, only FFA oxidation (O2 consumption) was investigated in this study; other aspects of lipid metabolism, such as lipid transport, storage, or de novo synthesis, were not analyzed. It should also be noted that three major players in FFA plasma and mitochondrial transport were investigated in this study; however, other proteins and FFA mechanisms were described but not analyzed [71,72]. Second, all measurements and studies were performed after awakening, providing several hours for recovery from OSA-related night factors. In contrast, these two limitations enable the identification of structural or long-term functional maladaptations (not only in muscle tissue) that persist during daytime hours and may represent potential drug targets. Third, our results should be interpreted and generalized cautiously due to the limited sample size and the fact that muscle biopsies and other metabolic parameters were investigated during wakening and not during sleep. Finally, OSA patients exhibit various phenotypes despite identical OSA severity as assessed by AHI, suggesting that other influential variables (e.g., sympathetic activation or stress/endocrine responses) play a role, and definitions based on AHI may not reflect the full extent of OSA [73]. In conclusion, the present study demonstrated that severe OSA did not modify muscle lipid oxidation, including the expression of essential regulatory proteins, in nondiabetic and T2DM individuals. We suggest that increased adipose tissue lipolysis, rather than reduced muscle FFA oxidation, is responsible for the reported elevations in plasma FFA levels in OSA patients and the resulting negative impact on glucose homeostasis. Consequently, adipose tissue metabolism may represent a plausible drug target for treating OSA-related metabolic derangements. ## 4.1. Participants The participants were recruited through referrals from physicians and local media advertisements, as part of the larger FAMOSA study, as described previously [38]. The participants were recruited into four groups: nondiabetic persons with no or mild OSA (control, $$n = 14$$), nondiabetic persons with severe OSA (OSA, $$n = 9$$), patients with T2DM with no or mild OSA (T2DM, $$n = 10$$), and patients with T2DM and severe OSA (T2DM + OSA, $$n = 11$$). The inclusion criteria were age 18–85 years and a body mass index (BMI) of 22–40 kg/m2. T2DM was diagnosed according to the criteria of the European Association for the Study of Diabetes [74]. Notably, all patients with acute illness, decompensated chronic disease, or cardiac or renal insufficiency; as well as those treated with beta-blockers, corticoids, insulin, sulfonylurea, GLP-1 receptor agonists, and gliflozins; and those who had a body weight change of >5 kg over the last three months, were excluded. All participants provided written informed consent before participating in the study. The study was registered at ClinicalTrials.gov (NCT02683616) and approved by the Ethics Committee of Kralovske Vinohrady University Hospital, Prague (EK-VP/$\frac{17}{0}$/2014). ## 4.2. Sleep Study The sleep study was performed using a type III device recording the hemoglobin saturation, heart rate, electrocardiogram, nasal airflow, and chest and abdominal respiratory efforts (Nox T3, Nox Medical, Reykjavik, Iceland) in the home setting. The acquired data were evaluated by a board-certified sleep physician according to the American Academy of Sleep Medicine criteria (apnea was defined as ≥$90\%$ reduction in airflow for at least 10 s, and hypopnea was defined as ≥$30\%$ reduction in airflow for at least 10 s with ≥$4\%$ desaturation). The severity of OSA was stratified by the apnea–hypopnea index (AHI): <5, no OSA; AHI ≥ 5 and <15, mild OSA; AHI ≥ 15 and <30, moderate OSA; AHI ≥ 30, severe OSA. ## 4.3. Biochemical Analysis, Clinical Investigations, and Muscle Biopsy FFA in the serum was determined using the NEFA-HR2 assay (Wako Chemical Inc., Richmond, VA, USA). Other biochemical analyses were performed by the Institutional Department of Laboratory Diagnostics, Kralovske Vinohrady University Hospital, Prague. Patients visited the clinical research center after overnight fasting for metabolic and anthropometric assessments and a muscle biopsy. The measurements included biochemical analysis, blood count, coagulation, urinalysis, multifrequency bioimpedance measurements for body composition (Body Impedance Analyzer NUTRIGUARD-M, Data Input GmbH, Frankfurt, Germany), and measurement of the body weight, height, and waist circumference. Afterward, a frequent-sampling intravenous glucose tolerance test (IVGTT) was performed as previously described [38]. Briefly, two intravenous catheters were inserted into the antecubital vein, and basal sampling at −15, −10, −5, and −1 min was performed, followed by intravenous administration of 0.3 g/kg glucose at 0 min and 0.03 U/kg insulin (Humulin R, Lilly France S.A.S, Fegersheim, France) at 20 min. Blood samples for plasma glucose and insulin determination were collected at 2, 3, 4, 5, 6, 8, 10, 12, 14, 16, 19, 22, 24, 25, 27, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, and 180 min. The values were subjected to a minimal model analysis [75] for insulin sensitivity (SI) and insulin secretion indices, including AIRg, disposition index (DI), SI, glucose effectiveness (SG), β-cell function, and insulin resistance. Muscle biopsies were performed two weeks after the clinical investigation visit (to avoid the possible influence of the IVGTT test on muscle metabolism). Muscle biopsies were performed in a fasted state, and samples were taken from the lateral part of the vastus lateralis muscle of the dominant leg, approximately 10 cm above the knee, under aseptic conditions using a 5-mm Bergstrom needle, as described previously [76]. Fresh biopsy samples were immediately transferred into relaxing BIOPS buffer (10 mM CaK2-EGTA, 7.23 mM K2-EGTA, 20 mM imidazole, 20 mM taurine, 50 mM K-MES [2-(N-morpholino)ethanesulfonic acid], 0.5 mM dithiothreitol, 6.56 mM MgCl2, 5.77 mM ATP, and 15 mM phosphocreatine adjusted to pH 7.1). Biopsies were stored in BIOPS buffer on ice until the lipid oxidation was determined. ## 4.4. Lipid Oxidation Determination Using High-Resolution Respirometry High-resolution respirometry adapted and validated for tissue homogenates obtained from muscle biopsy samples [77,78,79] was performed using an Oxygraph 2 K respirometer (Oroboros Instruments, Innsbruck, Austria). The analysis principle was based on the polarographic measurement of the oxygen consumption rate (OCR) of the analyzed sample using a Clark’s electrode. Before measurements, connective tissue, fat, and blood vessels were removed from the biopsy under a magnifying glass and subsequently homogenized by 4–6 strokes in an Elvehjem–Potter Teflon/glass homogenizer in 1 mL/100 mg biopsy sample in K media (10 mM Tris HCl, 80 mM KCl, 3 mM MgCl2, 5 mM KH2PO4, 1 mM ethylenediaminetetraacetic acid, and 0.5 mg/mL bovine serum albumin) at pH 7.4. Respirometry was performed at 30 °C without preoxygenation using 0.2 mL of $10\%$ biopsy homogenate and 1.9 mL of K media. Analysis of the mitochondrial functional indices in homogenates was assessed after the addition of selected substrates to the respirometer chamber (using the Hamilton syringe) at 4-min intervals, according to the manufacturer’s recommendations, as follows (final concentration in the respirometer chamber): malate (1 mM) + ADP (1 mM) to evaluate baseline OCR using intracellular substrates, followed by administration of palmitoyl carnitine (0.04 mM) to assess lipid oxidation capacity, and administration of succinate (10 mM) to provide unlimited substrate for mitochondrial complex II to simulate maximal mitochondrial respiration. Data were normalized to the sample protein concentration (mg) determined using the bicinchoninic acid assay. All the chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA). ## 4.5. Protein and Gene Expression Quantification qPCR: Biopsy samples were homogenized, and total RNA was extracted (TriPure Isolation Reagent, Roche Diagnostics, Rotkreuz, Switzerland) and treated with DNAse using a High Pure RNA Isolation Kit (Roche Diagnostics, Switzerland). A high-capacity cDNA Reverse Transcription Kit (Roche Diagnostics, Switzerland) was used to transcribe cDNA, which was subsequently assayed using a Real-Time PCR cycler ABI 750 (ThermoFisher Scientific, Waltham, MA, USA). TaqMan™ Fast Advanced Master Mix and probes Hs00192700_m1, Hs00169627_m1, and Hs03046298_s1 (Applied Biosystems, Carlsbad, CA, USA) were used to determine the expression of FATP4 (fatty acid transport protein 4), CD36 (platelet glycoprotein 4), and CPT1 (carnitine-palmitoyl transferase I), respectively. Data were expressed relative to the geometric mean of GUSB (β-glucuronidase) and TBP (TATA box binding protein) gene expression (TaqMan probes Hs00427620_m1 and Hs00939627_m1, respectively) using the 2−ΔΔCt method. Western blot: Biopsy samples were homogenized in T-PER lysis buffer (ThermoFisher Scientific, USA), centrifuged (10.000 rpm, 10 min, 4 °C), and mixed with Laemmli buffer (1:1, Bio-Rad Laboratories, Hercules, CA, USA). SDS-PAGE was performed using $8\%$ gels and blotted onto a 0.2 μm PVDF membrane for 2 h at 100 V in precooled transfer buffer (Bio-Rad Laboratories, USA). All membranes were blocked with $5\%$ nonfat milk in TBS-T buffer (100 mM Tris-HCl, 150 mM NaCl, pH 7.6, $0.1\%$ Tween-20) for 60 min. After washing with TBS-T, the membranes were incubated with the primary antibody overnight on a shaker placed in a refrigerator and subsequently washed in TBS-T. The following Abcam (Cambridge, UK) antibodies were used: anti-FATP4 (1:1000, Cat. No.: ab200353), anti-CD-36 (1:1000, Cat. No.: ab133625), anti-CPT1A (1:500, Cat. No.: ab234111), and anti-β-tubulin (1:3000, ab6046). A secondary antibody conjugated with HRP (1:10000, sc-2004, Santa Cruz Biotechnology, Dallas, TX, USA) was applied for 60 min; membranes were washed with TBS-T and subjected to chemiluminescence detection using a Radiance PLUS Chemiluminescent Substrate (Azure Biosystems, Dublin, CA, USA) and a ChemiDoc Imaging System (Bio-Rad, USA). Image Lab software (Bio-Rad, USA) was used for densitometric analysis, and band intensities of the evaluated proteins were normalized to β-tubulin signals. ## 4.6. Statistical Analysis The differences in outcome variables between the groups (control, OSA, T2DM, and T2DM + OSA) were analyzed using one-way analysis of variance (ANOVA) with least significant difference post hoc tests; mixed-model analysis was employed for linear trend analysis. 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--- title: 'Outdoor Kindergartens: A Structural Way to Improve Early Physical Activity Behaviour?' authors: - Jeanett Friis Rohde - Sofus Christian Larsen - Mathilde Sederberg - Anne Bahrenscheer - Ann-Kristine Nielsen - Berit Lilienthal Heitmann - Ina Olmer Specht journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048988 doi: 10.3390/ijerph20065131 license: CC BY 4.0 --- # Outdoor Kindergartens: A Structural Way to Improve Early Physical Activity Behaviour? ## Abstract Background: Studies have shown that outdoor play in nature is associated with a higher physical activity level than indoor play. We aimed to examine the effect of outdoor versus conventional kindergartens on objectively measured physical activity. Method: Using a pre-test-post-test design, we collected data in four kindergartens that provided a rotating outdoor and conventional kindergarten setting. Step counts were measured during one week in the outdoor setting and one week in the conventional setting. Differences in step counts between the outdoor and conventional setting were analysed using a paired t-test. Results: In total, 74 children were included. There was no statistically significant difference in total daily step counts between children in the two settings. When we looked at step counts during kindergarten hours, we saw that children were more physically active in the outdoor setting compared to the conventional setting (mean difference: 1089, $p \leq 0.0001$). When we looked at activity during time outside the kindergarten, we discovered that children had a lower step count in the outdoor setting as compared to the conventional setting (mean difference −652, $$p \leq 0.01$$). Conclusion: This study indicates that children are more physically active during the time they spend in outdoor kindergartens compared to conventional kindergartens, but may compensate with more inactivity outside kindergarten hours. ## 1. Introduction Several studies have shown that physically active children tend to remain more physically active across their lifespan [1]. Ideally, physical activity (PA) should be promoted among children of preschool age because it is easier to establish healthy behaviours in early childhood [2]. Nevertheless, a sedentary lifestyle with a long time spent indoors in front of a screen is becoming more and more common in children as young as three to five years, especially among children in urban areas [3]. This unhealthy lifestyle of preschoolers, leading to a low level of PA, is related to health problems including obesity and later increased risk of diabetes, cardiovascular diseases, musculoskeletal pain, and low self-esteem [4]. In Scandinavia, up to $97\%$ of all children at the age of three to five years attend kindergartens [5], and around $67\%$ of Danish children spends 6–8 h per day in kindergartens [6]. Therefore, the kindergarten environment presents an ideal setting to promote the early development of healthy PA behaviour [7]. Thus, kindergartens are an obvious place to create changes in children’s possibilities for physical experience and in relation to their PA level, motor skills, health, and possibly their well-being in the short and long term, regardless of social background, genes, and motor skills. In Denmark and other Scandinavian countries, children can attend two types of kindergartens. A conventional kindergarten with time spent both indoors playing with toys, drawing etc., and with options for outdoor activities in the kindergarten playground, and an outdoor kindergarten, where children spend almost all the hours during the day outdoors in forests or in rural areas often without a formal playground and little if any option for indoor activities. A combination of the two types also exists, the rotating kindergarten, where children on a weekly basis change their kindergarten environment from the conventional kindergarten to the outdoor kindergarten [8,9,10]. It has been suggested that increasing outdoor playing among children aged three to twelve years is positively associated with PA and inversely associated with sedentary activity [11]. Several studies have shown that children playing outdoors had a higher PA level as compared to indoor play [7,12]. Additionally, previous cross-sectional studies have indicated that children playing in a forest environment seem to have a higher overall PA level than children playing in playgrounds [13,14]. The uneven ground and more room for ingenuity have been proposed as the factors explaining the higher PA level observed in children playing in forest environments [15,16]. However, only a few, small studies have attempted to investigate differences in PA between children attending outdoor kindergartens compared to those in conventional kindergartens [12]. In this regard, a Swedish cross-sectional study, conducted among 369 preschool children and 84 preschool teachers found that preschool structural characteristics such as PA policies and more time spent outdoors were positively associated with children’s PA behaviour [12]. Another cross-sectional study conducted among 864 preschool children in Finland showed that frequent nature trips were associated with lower sedentary activity during preschool hours [17]. These findings would indicate that more structured outdoor activity during kindergarten may increase PA and decrease sedentary behaviour. However, given the cross-sectional nature of the previously conducted studies [12,17,18,19], studies are needed that follow children over time and use an experimental research design. Motor skill proficiency is known to be an important factor for future PA engagement and sports motivation [20]. Only a few studies have investigated motor abilities among children attending either an outdoor or a conventional kindergarten, and with mixed results. A Danish study which, among others, investigated motor development during 10 months among children in one outdoor kindergarten compared to a conventional kindergarten, showed that children in the outdoor kindergarten scored better in terms of attention, ingenuity and motor development. However, regarding frequency of illness, there was only a small difference between the groups [21]. Similarly, a Norwegian study among five-to-sevenyear-old kindergarten children that investigated versatile play during a nine-month period in an outdoor forest environment compared to a kindergarten playground, found that the children who daily played for 1–2 h in the forest gradually improved their motor ability more than the children who spent 1–2 h daily in the kindergarten playground [15]. However, in a large newly published study we did not see differences in motor abilities upon entrance to school between children attending either an outdoor or a conventional kindergarten [22]. In our previous study, we showed that children attending outdoor kindergartens differed in relation to parental socio- and early childhood demographics as compared to children attending conventional kindergartens, which can cause selection bias and is thus highly relevant to consider when investigating health outcomes related to kindergarten type attainment [23]. Thus, to avoid this, an ideal setting to investigate PA activity could be in rotating kindergartens, since the children here will be their own controls. In this study we aimed to investigate whether children attending rotating kindergartens were more physically active while in the outdoor kindergarten setting compared to when they were in the conventional kindergarten setting. ## 2. Materials and Methods The present study is part of the “Outdoor kindergartens-the healthier choice?“ ( ODIN) project, which aimed to investigate short- and long-term health effects on children attending outdoor and conventional kindergartens. ## 2.1. Study Population In total, parents of 134 children aged two to six years signed a consent to participate in the study. In cases where the child did not want to wear the activity tracker, this was respected. Children with missing measurements on PA level, in some cases due to loss of activity measures or missing data due to full data storage of the activity measure ($$n = 34$$) and children with fewer than three activity days in one or both weeks were excluded ($$n = 26$$). The final study population for analysis consisted of 74 children (Figure 1). ## 2.2. Data Collection Data collection for the present study was conducted in four rotating kindergartens in the Copenhagen area in the period February to mid-March 2020 and in September to December 2020. The data collection was paused due to the COVID-19 lockdown of all Danish kindergartens from 13 March 2020 until September 2020. The four rotating kindergartens both had an outdoor kindergarten setting and a conventional kindergarten setting, where the same group of children were one week in the outdoor setting and one week in the conventional setting. In that way, the children were their own controls. ## 2.3. Exposure Assessment The exposure was the kindergarten setting (outdoor/conventional). Children spend one week in the outdoor setting and one week in the conventional setting consecutively, and the order of rotation was random between the four included kindergartens. The conventional kindergarten settings were kindergartens in Copenhagen city where children spend time both indoors playing with toys, drawing etc. and with options for outdoor activities in the kindergarten playground. Before lunch, the activities are structured by the kindergarten teachers and could be both inside or outside. After lunch, the children are most often sent outside (for approximately 2 h) to play in the playground (weather permitting). The children decide for themselves what they want to do; however, the kindergarten teachers also organize activities in the playground, which the children can choose to participate in. The outdoor settings were outside Copenhagen in an environment surrounded by nature and where children spend almost full time outdoors in all seasons. Every morning parents dropped off the children at a collection point, typically the address of the conventional kindergarten setting, and from there the children were driven by bus for 30–60 min to the outdoor kindergarten setting. In the afternoon, the parents picked up the children at the drop-off point. ## 2.4. Outcome Assessment To obtain information about the PA behaviours, the SENS motion® system was used (https://sens.dk/da/, accessed on 1 February 2022). These sensors are medically approved devices designed for the long-term monitoring of physical activity. The sensors have only been validated among adults, but were chosen because of their ability to measure long-term physical activity [24,25]. The SENS motion® system consists of a single-use miniature tri-axial accelerometer (dimensions 50 × 21 × 5 mm, weight 8 g; SENS motion® activity measurement system) and a smartphone application. The system measures movement continuously at 12.5 Hz (every 10 s), 24 h a day. The accelerometer was placed on the lateral side of the right thigh on the first visit with a small waterproof band-aid (Medipore™, 3M, Soft Cloth Surgical Tape on Liner). The accelerometer was waterproof, and it was therefore not necessary to remove it during bathing and swimming. The accelerometer has an onboard memory of approximately 14 days. Data from the accelerometer were uploaded to the app via Bluetooth and then transmitted to a secured web server for storage and subsequent analysis. To avoid loss of data (due to full memory), the preschool teachers or researchers were required to connect the accelerometer to the app at least once a week. During the study period, participants could change the band-aid if needed. An instruction sheet and additional band-aids were provided to the preschool teachers and parents. The system presents an overall count of steps per day and furthermore has a built-in algorithm that categorized data into seven categories: [1] lying or sitting, [2] standing, [3] walking, [4] sporadic walking, [5] running, [6] cycling and [7] no data. Data from the predefined activity categories will not be evaluated in this study because these measurements were not validated among the children. The primary outcome, number of steps, was presented as an average of steps per day (Monday–Friday). Data were summarized as overall count of steps per day, count of steps taken during kindergarten hours (10:00 A.M. to 3:00 P.M., exclusive of bus transportation) and as count of steps taken outside kindergarten (time outside 10:00 A.M. to 3:00 P.M., including bus transportation). Children wore the accelerometer continuously for 12 days (10 week days and two weekend days). Research suggested that three days was acceptable to monitor preschool children’s PA levels [26]. Therefore, for the present study children should at least have worn the accelerometer for a minimum of three days in each kindergarten. As wear time from previous studies among preschool children varied with regard to the definition of a valid day, with six and ten valid hours being the most common, we decided that children should at least have 10 h of wear time per day to be included in the analysis [27]. Six out of the 10 h should be registered between 8:00 A.M. and 5:00 P.M., with the assumption that this would be during the period when Danish kindergarten institutions are generally open. ## 2.5. Statistical Analyses Descriptive statistics were presented as mean and standard deviations (SD) and percentages according to the type of outcome (binary/continual). Next, we used paired sample t-tests to analyse differences in PA behaviour between kindergarten settings (outdoor/conventional). We tested for differences in daily step counts during the whole day and separately for activity during kindergarten and leisure time. Interaction between gender and kindergarten settings in relation to total daily steps counts was explored in a repeated measures ANCOVA model. Potential significant interaction was further evaluated through stratified analyses. Given that the present study was paused due to the initial Danish COVID-19 lockdown and that kindergartens were advised to let children spend most of the day outside post lockdown, we chose additionally to investigate possible effect modification between the enrolment date and the kindergarten setting in relation to total daily steps. Moreover, analyses were performed to assess whether those children included in this study differed from those not included with regard to gender, ethnicity, transportation form to kindergarten, sports activities, whether parents were active with their child, whether the parents were participating in sports, and parental education levels. All statistical analyses were two-sided with a significance level at 0.05 and were performed using Stata SE 14 (StataCorp LP, College Station, TX, USA; www.stata.com, accessed on 1 February 2022). A power calculation was performed before the study began. Assuming a population of 40 children from outdoor kindergartens and 40 from conventional kindergartens, and a power of $80\%$ and $5\%$ significance, we would be able to detect a group difference of 3293 steps counts per day [28]. ## 2.6. Ethics Permission from the Ethical Committee was evaluated not to be relevant (journal nr.: H-19053587). Permission from the Capital Region Data Agency and the Danish Patient Safety Authority were granted (Journal nr.: P-2020-54 and 31-1521-8, respectively). Furthermore, parents and preschool teachers gave written consent for participation and for data to be used in the project. Information on full names, social security numbers or home addresses was not collected, and data were processed anonymized. Kindergarten teachers were informed about their right to withdraw at any time from the study. On behalf of the children, all parents were informed of their right to withdraw their children at any time from the study. In cases where the child did not want to wear the activity tracker, this was respected. ## 3. Results In total, 74 children ($55\%$ out of 134) had sufficient PA measurements (a minimum of three days of activity per week) and were included for analyses. The children’s demographics are outlined in Table 1. Not all of the included children had information on demographic factors (Table 1). The mean age of the included children was five years (SD: one) and most children (>$87\%$) were of Western origin. More girls ($65.08\%$) than boys were included in the study. There were no statistically significant differences in the step counts per day between the outdoor setting (mean: 13,253; SD: 3205) and the conventional setting (mean: 12,726; SD: 2896) (mean difference 527, $95\%$ CI: −61; 1115, $$p \leq 0.08$$) (Table 2). A difference in step counts was seen when we specifically looked at the time spent in the kindergarten, where children in the outdoor setting were more physically active as compared to children in the conventional setting (mean difference 1089, $95\%$ CI: 669; 1508, $p \leq 0.0001$). However, a difference in step counts was seen when we specifically looked at the time spent outside the kindergarten, where children in the outdoor setting were less physically active as compared to children in the conventional setting (mean difference −562, $95\%$ CI: −952; −171, $p \leq 0.01$) (Table 2, Figure 2). We further found no evidence of an interaction between kindergarten setting (outdoor/conventional) and child gender in relation to total daily step counts ($$p \leq 0.14$$). Moreover, we found no evidence of effect modification by enrolment date (before/after the Danish COVID-19 lockdown) ($$p \leq 0.13$$). Overall, the children included in this study were similar to those not included with respect to ethnicity, sporting activity, transportation form to/from kindergarten, whether the parents were active with their child, whether the parents participated in sports, and in relation to parental education level (Table 3). However, there was a higher proportion of girls among those included than those not included ($$p \leq 0.01$$) (Table 3). ## 4. Discussion The current study examined whether children, on those days they attended an outdoor kindergarten setting, were more physically active compared to when they were in a conventional kindergarten setting. Our findings indicated that time spent in an outdoor kindergarten was directly associated with preschool children’s step counts during kindergarten hours, but had no statistically significant influence on total daily step counts. The week the children were in the outdoor setting, they were less active outside kindergarten hours compared to the week they were in the conventional setting. The majority of earlier interventions on PA levels among preschoolers reported a small-to-moderate effect on general PA and a moderate effect on moderate-to-vigorous PA (MVPA) [7]. Those interventions with the greatest effects on MVPA that were short-term (i.e., lasted less than four weeks), were offered in childcare such as an early-learning environment, were led by teachers, involved outdoor activity, and incorporated unstructured activities [7]. Outdoor kindergartens include most of these elements, and as shown in the present study, children were more active during kindergarten hours when in the outdoor kindergarten setting compared to the conventional kindergarten setting. A recent systematic review highlights that pre-school environments with the presence of vegetation, open areas, portable play equipment, and lower playground density were positively associated with higher PA [29]. However, the review highlighted that more studies are needed to describe the association between environment and PA levels. Because we had measures of all-day activity (24 h), it was possible to isolate activity during kindergarten hours and non-kindergarten hours. Our results indicated that some degree of subsequent compensation or more sedentary activity outside kindergarten time may occur. This was potentially due to transport to and from the outdoor setting or the possibility that time spent outside in the outdoor kindergarten may generate more fatigue in children. Spending a good part of the day outside exposed to environmental conditions may lead to children feeling more relaxed and less likely to go outside and/or be physically active after kindergarten. A Danish study which conducted a three-year school-based PA intervention, surprisingly showed that doubling the amount of physical education from the first year of primary school had no significant effect on overall PA levels in the intervention group [30]. The study also emphasized that a possible explanation could be that children in the intervention group compensated for the increased school-time PA during the remainder of the day [30]. Another study, also conducted among primary school children, showed that PA participation during physical education classes and after-school was associated with children’s overall MVPA, but not with their sedentary behaviour [31]. In accordance, some studies have questioned whether children compensate for higher levels of activity during school hours by being less active after school [32,33]. A suggested hypothesis is that when an individual increases their PA in one domain, there is a compensatory change in another domain to maintain an overall stable level of PA [34,35]. Existing guidelines on PA for children are commonly expressed in terms of frequency, time, and intensity [36]. Recommendations based on a measure of step counts from accelerometers and pedometers are desired. Measuring step counts is becoming more common in research and may be a reasonable approximation of daily physical activity [36,37]. While it is recommended that children obtain 60 min of MVPA per day, which is suggested to be associated with approximate 10,000–14,000 daily steps in preschool children aged four to six years, there is no certain number of daily steps that cuts across all ages [37]. A systematic review aiming to identify, among existing guidelines, the optimal step-count cutoff for children and adolescents aged five to nineteen years found that existing guidelines ranged between 9000 and 14,000 steps per day for PA cohort studies [38]. However, due to the risk of methodological bias, none of the guidelines was endorsed. In total, 12,000 steps per day for children and adolescents were suggested by the study with the lowest risk of methodological bias [38]. In our study, children took approximately 13,000 step counts per day regardless of setting. This lies within the recommendations and may create less space to increase the physical activity level. The strength of the current study is that the children’s PA levels were assessed objectively with accelerometers for 24 h, thus limiting some of the biases associated with self-reported measures such as recall difficulties. Another strength is the use of the pre-post design where the children served as their own controls; this limits the risk of potential confusion. The optimal study would have been a randomized controlled trial; however, it would be extremely difficult to implement and recruit participants for a randomized study, as it essentially means that parents would have to give up their free choice of kindergarten type. Certain limitations in this study must be considered. The SENS motion® activity accelerometer has only been recognized as a technically reliable instrument to objectively capture daily step counts among hospitalized patients or patients with knee osteoarthritis [24,39] and has not been validated among children. We had information on the type of transportation to and from the kindergarten; however, this was not divided into the two weeks (the outdoor setting or the conventional setting weeks). We cannot exclude the possibility that the observed compensation seen outside kindergarten time during the outdoor weeks could be due to changed transportation form to and from kindergarten. It may be that parents choose to drive their children to kindergarten during the outdoor week. However, because the majority of children in Copenhagen live close to their kindergarten, we do not expect this to be very different in this study. The parents reported that around $60\%$ of the children walked or biked to kindergarten, which will probably be the same in the two weeks. Therefore, we do not believe the transportation form will differ much between the two weeks. Furthermore, it would also have been relevant to have information about the size of the kindergarten and the outdoor area including geographical information, because differences in space to move around in the two environments may have had an impact on the step counts taken during time spent in the kindergarten. In a qualitative study performed in the included kindergartens, we found that there were more opportunities for children to move more versatilely in the outdoor environment and there were greater opportunities for gross motor games and risky games in the outdoor than in the conventional setting [40]. This may indicate that greater space has an impact on step counts. We did not assess the PA or attitudes towards PA among the preschool teachers. However, preschool teachers’ individual attitudes and behaviours towards PA may play an important role in promoting PA among preschool children [19]. A study from Norway that explored accelerometer-assessed associations between preschool teachers’ and children’s levels of MVPA during preschool hours, demonstrated an association between preschool teacher’s aggregated PA levels and four-to-six-year-olds’ individual PA levels [20]. However, because we conducted a pre-post design where the same group of kindergarten teachers were both in the outdoor and the conventional setting, we do not suspect that kindergarten teachers’ attitudes towards PA would have influenced the results in this present study. Similarly, we did not assess the PA or attitudes towards PA among the parents. We did, however, ask if parents were active with their children and discovered that $80.7\%$ of the parents were active with their child. Research indicates a strong correlation between parental and child PA behaviours [41] and it may be that parental attitude towards PA may influence the compensation seen among the children during the week in the outdoor kindergarten setting. Methodological limitations, such as selection bias, may arise because of missing activity measures or being excluded from analysis due to incomplete data on physical activity. Moreover, a lack of statistical power may have reduced the chances of detecting the true effects. Thus, with a larger sample size we might have been able to show a significant association in total daily step counts. It is more common in Denmark to have rotating kindergartens in larger cities compared to smaller cities; however, outdoor kindergartens which roughly can be compared to when the children are in the outdoor setting in rotating kindergartens can be found in all areas. We have, though, in our previous study shown that parents in the two largest cities in Denmark choosing outdoor kindergartens differ with respect to socio-demographics as compared to parents from the same area choosing conventional kindergartens [23]. Thus, caution should be taken when generalizing the results on a national level. Data collection for the present study was paused due to the COVID-19 lockdown from 13 March until September 2020. Post-lockdown, the new situation may have changed the way the kindergarten teachers used the outdoor space, since it was recommended that children spend more time outdoor due to the lower infection risk. This could have influenced the children’s activity levels, leading to a smaller difference in steps taken between the two settings post-lockdown. However, we found no evidence of effect modification by enrolment date (before/after the Danish COVID-19 lockdown), though it should be mentioned that the number of children included pre-lockdown was low ($$n = 21$$). ## 5. Conclusions Findings from this study suggest that children are more physically active during kindergarten hours in outdoor kindergartens compared to conventional kindergartens, but this may at best lead to a marginally higher total daily activity level, perhaps indicating some degree of subsequent compensation or more sedentary activity due to transportation to and from the outdoor setting. Findings from this study indicate that a supportive environment may be a potential trigger for healthy PA behaviour in everyday life and especially that targeting behaviour changes through structure community intervention, such as outdoor kindergartens, may be beneficial. ## References 1. Jones R.A., Hinkley T., Okely A.D., Salmon J.. 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--- title: Handgrip Strength Is Positively Associated with 24-hour Urine Creatine Concentration authors: - Enkhtuya Ulambayar - Delgermaa Bor - Nandin-Erdene Sukhbaatar - Narkhajid Usukhbayar - Uugantuya Ganbold - Odmaa Byambasuren - Uranbaigali Enkhbayar - Oyuntugs Byambasukh journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048991 doi: 10.3390/ijerph20065191 license: CC BY 4.0 --- # Handgrip Strength Is Positively Associated with 24-hour Urine Creatine Concentration ## Abstract Background: Muscle mass evaluation methods are often expensive and therefore limited in their daily use in clinical practice. In this study, we investigated the relationship between hand grip strength (HGS) and other parameters of body measurements with urine creatinine, especially to investigate whether HGS measurement is an indicator of muscle metabolism. Methods: In total, 310 relatively healthy people (mean age 47.8 + 9.6; 161 people or $51.9\%$ of the total population were men) who were undergoing preventive examinations were included in this study and given a container to collect 24-h urine, and the amount of creatinine in the urine was determined by a kinetic test without deproteinization according to the Jaffe method. A digital dynamometer (Takei Hand Grip Dynamometer, Japan) was used in the measurement of HGS. Results: There was a significant difference in 24-h urine creatinine (24 hCER) between the sexes, with a mean of 1382.9 mg/24 h in men and 960.3 mg/24 h in women. According to the correlation analysis, the amount of urine creatinine was related to age (r = −0.307, $p \leq 0.001$ in men, r = −0.309, $p \leq 0.001$ in women), and HGS ($r = 0.207$, $$p \leq 0.011$$ in men, $r = 0.273$, $$p \leq 0.002$$ in women) was significant for either sex. However, other parameters of body measurements, such as girth, forearm circumference, and muscle mass measured by bioelectrical impedance, were not related to urine 24 hCER. A correlation between HGS and 24 hCER was observed in age groups. Conclusions: We found that HGS is a potential marker in muscle metabolism assessment that is proven through 24 hCER. In addition, therefore, we suggest using the HGS measure in clinical practice to evaluate muscle function and well-being. ## 1. Introduction Handgrip strength (HGS) is an important health indicator for older adults, as its lower value is associated with adverse outcomes such as fragility and falls [1,2]. As described in a review study, a lower HGS is positively associated with the risk of fragility independently with various aging markers and physical functioning [1]. A recent prospective study showed that a greater HGS was associated with better functional outcomes after 1-year follow-up among fragility hip fracture patients [3]. In addition, falls and fracture risk are greater among seniors with lower HGS [3,4]. A meta-analysis ($$n = 220$$,757) of 19 population-based prospective cohort studies showed a significant correlation between hand grip strength and the predictability of bone fractures, and the odds ratio was 0.70 in the highest tertile group compared to the lowest tertile group of HGS, which shows that the fracture risk was low in people with higher HGS [4]. Furthermore, recent studies show that the risk of mortality is higher in individuals with low HGS. For instance, in a Spanish study of 351 hospitalized cancer patients, the risk of mortality was not associated with body circumference measures, while the HGS measurement proved to be a significant predictor of mortality [5]. In recent years, in particular, clinical guidelines and recommendations have included HGS as a muscle strength indicator in the criteria for identifying lower muscle mass or sarcopenia [6,7,8,9]. In the literature, creatinine is an intermediate product of muscle metabolism and is regularly excreted in the urine at a rate of 1 g/day under normal conditions [10]. Most of the creatinine is produced by non-enzymatic metabolism, which occurs at a steady rate in the skeletal muscle as a function of muscle mass [11,12]. Therefore, urinary creatinine excretion is the main determining factor of skeletal muscle metabolism. Although measuring serum creatine is important in evaluating muscle metabolism, researchers noted that measuring creatinine in urine, particularly in urine samples taken 24 h a day (24 hCER), is an appropriate method for assessing muscle metabolism [13,14,15]. A few studies have examined the relationship between HGS and urinary creatinine excretion; most have been conducted in patients with chronic illness [16,17,18]. It is important to investigate an easily accessible and inexpensive tool to measure muscle metabolism; one would be HGS. In this study, we hypothesize that HGS could be a potential marker for muscular metabolism that can be shown to possess a positive association with urinary creatine excretion. Therefore, we aimed to explore the relationship between HGS and 24 hCER among healthy adults. We also investigated whether HGS is a better indicator of muscle metabolism than other body composition measures in relation to 24 hCER. ## 2.1. Study Participants This study was conducted among those who participated in health screening at the university hospital of the Mongolian National University of Medical Sciences between July and September 2022, and those who agreed to participate in the study ($$n = 443$$). The sample size was calculated based on the total number of people screened for the last 6 months in the hospital ($$n = 12$$,500) and assuming a $95\%$ confidence interval ($Z = 1.96$) with a $5\%$ acceptable margin of error ($e = 0.05$), which gave a sample size of 443 persons. During the data collection, we excluded individuals who were being treated for chronic diseases, which can impact muscle metabolism, such as diabetes and liver cirrhosis, as stated earlier in the patient report ($$n = 90$$). We also excluded cases where these conditions were newly diagnosed by doctors as a result of health screening ($$n = 19$$). Moreover, if an athlete had participated in sports sessions in the last seven days or if patients were using diuretics, we excluded them ($$n = 7$$). In addition, individuals who did not collect urine more than twice in the 24-h urine collection period and those whose measurement was less than 500 mL/24 h were excluded ($$n = 19$$) from the study [19]. A total of 310 participants were included in the current analysis. The study was conducted according to the Helsinki Declaration, and it was approved by the medical ethical committee of the Mongolian National University of Medical Sciences (METc 2022/Z-03). All participants provided their written informed consent. ## 2.2. Hand Grip Strength Measurement A digital dynamometer (Takei Hand Grip Dynamometer 5401-C, Tokyo, Japan), which is an isometric electronic device, was used to measure HGS in this study [20]. The instrument weighed 5 to 100 kg, and the minimum unit of measurement was 0.1 kg. The HGS was measured two times for both hands and the mean for each hand was recorded. During the measurement, the participant was recorded with feet shoulder-width apart, elbows fully extended, and fingers bent 90° once in each hand. Participants were instructed to continually tighten the handle with full force for at least 3 s and not to move the dynamometer or hold their breath while taking the measurement. The measures were the same for men and women and age groups, but we used different thresholds that had been established before in the same population [21]. The maximal measurement for one of the two hands was taken and called the dominant HGS [20]. ## 2.3. 24-h Urine Creatinine Collection and Measurement The kinetic test without deproteinization according to the Jaffe method was used to measure urine creatinine (BioMajesty® BM6010/C) [22]. Subjects were provided with a designated urine collection container and advised to stay at home or choose a convenient day for urine collection. When providing instructions, it was recommended that urine collection began at 8:00 a.m. or the time and date at the start of urine collection was recorded. In addition, on the day of urine collection, it was recommended not to consume beets, coffee, or foods that were too sweetened; not to use diuretics; and not to perform physical workouts and heavy sports. ## 2.4. Other Variables and Measurements The interview included questions related to participants’ general and lifestyle characteristics, including education, smoking, alcohol use, diet, and physical activity. Questions on the intake of meat, fruits, and vegetables were based on a daily to weekly frequency. We assessed physical activity behavior, using a question that dealt with workouts, exercise, and sports [23]. The question was “How often do you perform physical activities to work out or exercise or sports?” The answers were “never or less than once monthly”, “1–2 times per month”, “1–2 times per week”, “3–4 times per week”, and “daily”. Based on the descriptive results, lifestyle variables were classified as dichotomous variables: smokers/non-smokers, weekly/non-weekly alcohol use, daily/non-daily intake of meat, fruit and vegetable intake, and weekly/non-weekly regular physical activity. Participants’ body weight (in kg), height (in cm), blood pressure (Pangao, PG-800B69, The Hague, Netherlands), and body circumferences, such as neck, chest, midarms (upper arm circumference), forearm (lower arm circumference), waist, hip, and thigh circumference, were measured by well-trained assistants implementing a standardized protocol. Body mass index (BMI; kg/m2) was subsequently calculated based on body weight and height. A bioelectric impedance analyzer (Tanita BC-730) was used for muscle mass measurement (in kg). ## 2.5. Statistical Analysis The characteristics of the study population were expressed as means with standard deviation (SD) and as numbers with percentages according to 24-h urine creatinine tertiles. A histogram of the 24-h urine creatinine concentration was obtained and expressed as a median with a 25th–75th percentile. The differences between groups were compared using Student’s t-test, one-way analysis of variance (ANOVA), the Kruskal–Wallis test, and Pearson’s Chi-Square test. Crude and age-adjusted Spearman’s correlation coefficient was calculated between 24-h urine creatinine and age, HGS, and other anthropometry measures. Furthermore, sex-stratified and age-adjusted estimation of the 24-h urine creatinine concentration was performed in stratified groups of age, physical activity, and HGS category using univariate analysis. The age group was determined based on age tertiles. The HGS category was based on previous study results that categorized dominant HGS into higher and less than the 25th percentile of HGS based on its histogram in men and women, respectively. Less than the 25th percentile of HGS (31.8 for men and 20.5 for women) was categorized as the lower HGS group [21]. Additionally, a linear regression analysis was performed, and the unstandardized beta coefficient was reported with a $95\%$ confidence interval (CI). After crude analysis, the analysis was adjusted for age, education, and BMI. For all statistical analyses, we used IBM SPSS V.28.0. A statistical significance level was set at $p \leq 0.05$ for all tests. ## 3. Results The mean age of the subjects was 47.8 + 9.6 years and $51.9\%$ ($$n = 161$$) of the total subjects were male. The amount of creatinine excreted in the urine was significantly different for the two genders, with a mean of 1382.9 mg/24 h (median of 1322.0 and 25th–75th percentile of 869–1815) in men and 960.3 mg/24 h (median of 878 and 25th–75th percentile of 583–1265) in women. Therefore, further analyses were performed by gender. Table 1 shows the characteristics of the study participants, based on urine creatinine levels (tertiles). Significant differences were observed for age and HGS, and it seems that with increasing age, urinary creatinine levels declined, while those with high HGS tended to have higher creatinine levels in the urine. It was assumed that meat consumption, physical activity frequency, and muscle mass measured by the bioelectrical impedance analyzer were consistent with creatinine levels in the urine, but no differences were observed between the 24 hCER groups. Regarding the level of education, $13\%$ of men and $10\%$ of women had low education and no differences between groups were found ($p \leq 0.05$). In addition, there were no differences between groups (24 hCER groups) in systolic blood pressure. As shown in Table 1, there was a tendency for urine creatinine to increase with increased physical activity, but this was not significant. Therefore, age-adjusted urine creatinine was compared for three levels of physical activity. Although there was a tendency for urine creatinine to increase with an increased level of physical activity, this was not significant. Spearman’s correlation analysis showed that the 24 hCER was correlated significantly only with age and HGS in both sexes (Table 2). Although no significant correlation was observed, we can see from the table that there was a correlation between 24 hCER and BMI and muscle mass determined by the bioelectric impedance analyzer. For other body composition measures, body height and weight alone, as well as body circumference measurements, were not related to urine creatinine excretion. In addition, when adjusting for age, the correlations of 24 hCER with HGS remained. The positive correlation between 24 hCER and HGS shown in the table above shows that people with higher HGS or muscle mass may possess a higher metabolism of creatinine as they present higher 24 hCER (Figure 1). In the additional analysis, when we used the reference value of the study that established the lower limit of HGS among Mongolian people (31.8 for men and 20.5 for women), Figure 2 shows that with increasing age, the number of people in the low HGS group increases. When we used the HGS groups mentioned above, there were significant differences in the amount of creatinine excreted in the urine by groups of HGS (Table 3). Moreover, when considering age groups, the amount of creatinine excreted in the urine tended to decrease. Considering both sexes, 24 hCER levels were significantly lower in men with increasing age in the lower HGS groups. Similarly, in women, 24 hCER decreased in the low HGS groups with increasing age, but only a significant difference was observed in the middle age group (Table 3). Finally, we tested the relationship between HGS and 24 hCER using linear regression analysis (Table 4). The HGS was significantly associated with 24 hCER in both men and women. After adjusting for age, the association was attenuated but remained significant, indicating that the association was age-independent. Further adjustments showed the significant association of HGS with 24-h urine creatinine. ## 4. Discussion We found that there was a significant association between HGS and the level of creatinine in the urine within 24 h. We hypothesized that other body composition measures, especially muscle mass measured by bioelectrical impedance analysis, were positively associated with 24 hCER, but there were no significant correlations observed. These suggest that HGS may be a better indicator of muscle mass compared to other body composition indices, because urine creatinine is the main determining factor in skeletal muscle metabolism. Furthermore, there were significant differences in 24 hCER by sex and age, but the association of HGS with 24 hCER was independent of age and sex. Most previous studies examining the relationship between HGS and urinary creatinine levels were linked to nutritional status or in individuals with chronic renal disease [16,17,18]. A study in the Netherlands (including 184 renal transplant recipients) showed a significant association between HGS and 24 hCER (standardized β = 0.33, $p \leq 0.001$), independent of adjustment for potential confounders such as age, sex, eGFR, time after transplantation, living donor, BSA, history of CVD, hypertension, glucose levels, albumin, lipids, hs-CRP medication use, and protein intake [17]. In 50 dialysis patients, the rate of endogenous creatinine synthesis was calculated and found to be positively associated with HGS (β = 0.44, $p \leq 0.001$) [18]. These studies also tested the association of HGS with other muscle characteristics, including walking tests, etc., showing that HGS is a useful measure for assessing muscle function, not merely muscle strength. We found a significant association between HGS and urinary creatinine levels within 24 h in relatively healthy individuals. These suggest that this simple and fast method is suitable for clinical use, as HGS can be used as an indicator of muscular metabolism, in relatively healthy people and people with chronic illnesses. Although we found that there were no associations of other body composition measures with urine creatinine, previous research has found anthropometric predictors of creatine clearance in urine, including body weight, height, and girth [24,25,26,27,28]. Joachim et al. examined the association of the urinary creatinine excretion rate with mortality risk in a relationship with coronary artery disease [26]. The study analyzed the associations of body circumferences and the calculated rate of creatinine excretion (CER) with a marker of muscle mass. Only the WHR had a weak association with CER. Barrios et al. analyzed the relationships between waist circumference (WC), waist–hip ratio, and 24-h urinary albumin excretion rate (UAER) and creatine clearance [27]. Study participants underwent measurements of anthropometric factors and calculations (including WC, waist to hip ratio) and 24-h urine sampling to determine creatinine clearance and UAER. Of the anthropometric parameters, one increase in WC was found to be independently linked to 24 h UAER, although this association disappeared after the correction of creatinine clearance on the body surface area [28]. In addition, the researchers developed some indices, such as the creatine height index and the creatine arm index [29,30]. However, these indices are not commonly used for clinical application or research. Moreover, not all studies found a significant association between urinary creatinine and the aforementioned body composition indices [16,18]. It could be explained that these indexes are not direct measures of muscle mass or function. Indeed, HGS is also not a direct measure of muscle metabolism. Our results suggest that HGS might be a better measure than other anthropometric measures. Furthermore, some studies have shown a positive association between urinary creatinine and muscle mass measured by bioelectrical impedance analyzers (BIA) [16]. Our study did not reveal any significant association among these variables. This may be explained by the capacity and estimation of the BIA methodology. The results of a bioelectric impedance analysis are dependent on the BIA equation and the number of sensors used in the measurement [31]. For instance, one study found a significant association between 24 hCER and HGS, using the multifrequency BIA method [16]. A single-frequency BIA analyzer was used for this study. Therefore, we suggest using HGS in muscle evaluations instead of the single-frequency BIA analyzer in clinical settings. In accordance with the literature, we found that the concentration of urinary creatinine depends on age and gender [10,24,32]. As a result, the current study has identified creatinine differences by HGS with an age effect. The participants were divided into two groups, those with high HGS (>25 percentile) and low HGS (<25 per percentile); this threshold criterion was found in a previous study in the same population [21]. There were certain associations for the urinary creatine concentration in relation to HGS groups and age groups in this study. The relations among age–HGS and age–concentration of urinary creatine were logically correlated in males. The incidence of lower HGS and age were positively correlated, and a low urinary level of creatinine could be associated with sarcopenia. However, not all associations in the age groups were relevant for women. This may be associated with lower levels of creatine in urine in women than in men in order to divide them into subgroups. Studies showed that the urinary level of creatinine was induced by physical activity and exercise [16,33]. In the present study, there was no relationship between them. This could be due to the exclusion criterion used in the study that excluded people who had exercised in the last three days. In addition, studies have shown that creatinine in the urine is linked to protein intake [34,35]. There was no correlation between meat consumption and 24 hCER in this study. This could be explained by the fact that Mongolians regularly consume a large amount of meat, which does not lead to any difference [36]. According to the research hypothesis in this study, HGS seems to be a good marker for muscle metabolism, as shown by a positive association with urinary creatinine excretion. Therefore, HGS measurement should be used in daily clinical practice to assess muscle function and well-being, particularly in older adults, to prevent frailty and falls. There was a limitation in our study. The sample was not sufficient to conduct age group analyses for other groups of variables, such as sex, 24 hCER, and HGS. Furthermore, we did not calculate eGFR and creatinine clearance using the serum creatinine level in our study. Finally, the participants in this study were relatively young. ## 5. Conclusions We found that HGS is a potential marker in muscle metabolism assessment that is proven through 24 hCER. In addition, HGS can be a better indicator of muscle mass compared to other body composition indexes, such as single-frequency BIA and body circumferences. Therefore, we suggest using the HGS measure in clinical practice to evaluate muscle function and well-being. ## References 1. Abd Kahar N.S., Kuan C.S., Singh D.K., Mokhtar S.A.. Risk Factors Associated with Fragility Fracture Among Older Adults with Fragility Fracture: A Systematic Review. Mal. J. Med. Health Sci. (2022) 18 318-326. DOI: 10.47836/mjmhs.18.s15.44 2. Bautmans I., Gorus E., Njemini R., Mets T., Njemini R.. 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--- title: Hypertrophy and ER Stress Induced by Palmitate Are Counteracted by Mango Peel and Seed Extracts in 3T3-L1 Adipocytes authors: - Giovanni Pratelli - Diana Di Liberto - Daniela Carlisi - Sonia Emanuele - Michela Giuliano - Antonietta Notaro - Anna De Blasio - Giuseppe Calvaruso - Antonella D’Anneo - Marianna Lauricella journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10048994 doi: 10.3390/ijms24065419 license: CC BY 4.0 --- # Hypertrophy and ER Stress Induced by Palmitate Are Counteracted by Mango Peel and Seed Extracts in 3T3-L1 Adipocytes ## Abstract A diet rich in saturated fatty acids (FAs) has been correlated with metabolic dysfunction and ROS increase in the adipose tissue of obese subjects. Thus, reducing hypertrophy and oxidative stress in adipose tissue can represent a strategy to counteract obesity and obesity-related diseases. In this context, the present study showed how the peel and seed extracts of mango (Mangifera indica L.) reduced lipotoxicity induced by high doses of sodium palmitate (PA) in differentiated 3T3-L1 adipocytes. Mango peel (MPE) and mango seed (MSE) extracts significantly lowered PA-induced fat accumulation by reducing lipid droplet (LDs) and triacylglycerol (TAGs) content in adipocytes. We showed that MPE and MSE activated hormone-sensitive lipase, the key enzyme of TAG degradation. In addition, mango extracts down-regulated the adipogenic transcription factor PPARγ as well as activated AMPK with the consequent inhibition of acetyl-CoA-carboxylase (ACC). Notably, PA increased endoplasmic reticulum (ER) stress markers GRP78, PERK and CHOP, as well as enhanced the reactive oxygen species (ROS) content in adipocytes. These effects were accompanied by a reduction in cell viability and the induction of apoptosis. Interestingly, MPE and MSE counteracted PA-induced lipotoxicity by reducing ER stress markers and ROS production. In addition, MPE and MSE increased the level of the anti-oxidant transcription factor Nrf2 and its targets MnSOD and HO-1. Collectively, these results suggest that the intake of mango extract-enriched foods in association with a correct lifestyle could exert beneficial effects to counteract obesity. ## 1. Introduction Obesity is a multifactorial disease characterized by the accumulation of body fat resulting from excessive food intake, reduced physical activity, environmental factors and genetic susceptibility [1,2]. For decades now, the incidence of obesity has increased in developing countries, representing a public health problem [1]. Hypertrophic expansion of white adipose tissue (WAT) represents an important risk factor for the development of several chronic diseases, including insulin resistance, type II diabetes, non-alcoholic fatty liver disease (NAFLD), cardiovascular diseases and some forms of cancers, such as pancreatic, colorectal, ovarian, thyroid and breast cancers [3,4,5,6]. Excess dietary fat intake has been associated with overweight and fat deposition in mice and humans and represents a serious health risk [7,8,9]. However, the quality of dietary fats has been shown to induce differential lipid storage. In fact, evidence shows that a high intake of saturated long-chain fatty acids (SLFAs), such as palmitic acid (PA), is associated with obesity [10], while a diet containing monounsaturated (MUFAs) and polyunsaturated fatty acids (PUFA), such as oleic or linoleic acid, or medium-chain fatty acids (MFAs), including caprylic acid, capric acid and lauric acid, may have beneficial effects on body weight and obesity [11]. This can be explained considering that certain fatty acids (FAs) are more likely to be stored in adipose tissue versus being oxidated for energy. In particular, SLFAs have lower oxidation rates than MUFAs, PUFAs and MFAs, leading to increased fat storage in white adipose tissue (WAT) [12]. Fat accumulation into adipose tissue due to high consumption of LSFAs produces hypertrophic and dysfunctional adipocytes, leading to a state of chronic low-grade inflammation [13] that contributes to the development of obesity-related diseases [14]. PA induces hypertrophy by increasing lipids droplet (LDs) content, and causes DNA damage in adipocytes in vitro [15]. Moreover, high consumption of PA increases the expression of pro-inflammatory cytokines (TNFα, IL-6, IL-1β) in adipose tissue [16]. The mechanisms through which high levels of LSFAs induce adipocyte disfunction and inflammation in WAT are different. When a large amount of PA is present, adipocytes metabolize it into lysophosphatidylcholine, diacylglicerol (DAG) and ceramides [17]. These compounds have been shown to induce PKC activation, endoplasmic reticulum (ER) stress induction and NF-kB activation [18,19,20]. Several studies suggest that increased oxidative stress is positively correlated with obesity [21]. Obese patients exhibit an abnormal oxidant/antioxidant status with higher levels of oxidative stress markers such as hydroperoxides and carbonyl proteins, while their antioxidant defenses are lower than those of their normal-weight counterparts [22]. The increased presence of reactive oxygen species (ROS) causes extensive oxidative damage to proteins, lipids and DNA, promoting metabolic dysfunction and lipotoxicity in adipocytes [23]. High-fat diets promote oxidative stress in adipose tissue [24]. It has been shown that PA increases ROS production in adipocytes by increasing NADPH oxidase 4 (NOX4) activity [23,24,25]. Moreover, it has been suggested that the elevated bioavailability of FAs can overwhelm the mitochondrial respiratory chain and oxygen consumption, leading to mitochondrial dysfunction and ROS production [26]. Interestingly, oxidative stress and inflammation appear to be closely interlinked in obesity. ROS may activate redox-sensitive transcription factors, such as NF-κB, that transactivate pro-inflammatory cytokines, such as IL-6 and TNF-α [27]. These, in turn, may further induce ROS production, generating a vicious circle between inflammation and oxidative stress [27]. Several studies showed that caloric restriction or increased physical activity lowered fat mass with a consequent reduction of oxidative stress and inflammation-associated obesity [28,29]. In addition, there is an increasing interest in natural antioxidant compounds, such as polyphenols contained in plants, due to their effectiveness against obesity and the related chronic diseases [30,31]. Mango (Mangifera indica L.) is a tropical plant belonging to the Anacardiaceae family whose cultivation has recently spread to the coastal areas of Sicily (Italy), where the favorable climatic conditions stimulate the growth of the plant and the ripening of the fruit [32]. Mango fruit is appreciated for its nutritive and nutraceutical properties [32,33]. It has been shown that different parts of the plant and of the fruit exert anti-inflammatory, anti-oxidant and anti-tumor effects in in vitro as well as in vivo models because of the presence of a wide range of polyphenols [34,35,36]. In addition, several studies demonstrated that mango also exerts anti-obesity and antidiabetic effects. Mangifera indica L. leaf extracts have been shown to reduce adipogenesis in 3T3-L1 adipocytes by decreasing the expression of genes involved in lipid metabolism [37]. In addition, mango juice intake decreases adiposity and inflammation in high-fat-diet-fed obese rats [38], while mango fruit powder reduces insulin resistance and steatosis [39]. Furthermore, it has been shown that fresh mango consumption improves postprandial glucose and insulin responses in obese adults [40]. Arshad et al. demonstrated that the consumption of mango peel powder reduced oxidative stress and dyslipidemia in obese subjects [41]. These studies highlighted the potential of mango as a functional food for the treatment of obesity and related diseases. The edible part of mango is only the pulp. However, it has been reported that mango peel and seed, which are the main bio-wastes of mango processing, contain high levels of bioactive compounds [42,43]. We previously demonstrated that extracts of mango peel and seed cultivated in Sicily exert anti-adipogenic effects by reducing the differentiation of 3T3-L1 fibroblasts into adipocytes. These effects results from the down-regulation of adipogenic factors such as PPARγ and SREBP as well as the activation of AMPK [43]. Keeping in view the potent health benefits of these mango extracts, the present study was designed to evaluate the ability of mango peel extracts (MPE) and mango seed extracts (MSE) to counteract lipotoxicity induced in adipocytes by SLFAs. To this end, we used an in vitro model in which mature 3T3-L1 adipocytes were treated with high doses of PA, resulting in artificially hypertrophied mature adipocytes. In our model, we examined the effect of mango extracts on PA-induced hypertrophy and oxidative stress. Our data provide evidence that MPE and MSE reduced lipid accumulation and exerted anti-oxidant effects by reducing lipogenesis, inducing lipolysis and counteracting ER stress and ROS increase. The activation of the AMPK and Nrf2 pathways seems to suggest that MPE and MSE reduced lipotoxicity induced by PA in adipocytes. ## 2.1. MPE and MSE Reduce PA-Induced Toxicity in 3T3-L1 Adipocytes The present study aimed at investigating whether peel and seed extracts of mango were capable of reducing lipotoxicity exerted by high doses of PA on differentiated 3T3-L1 adipocytes. The compositions of both MPE and MSE have been previously characterized by HPLC-ESI-MS analysis [35,43]. Data showed that both the extracts are rich in polyphenols with antioxidant properties [35,43]. In particular, methyl digallate, methyl gallate, gallic acid and glucosyl gallate were the main phenolic compounds. A representative picture of the main phenolic compounds of MPE and MSE is shown in Figure 1. Moreover, our previous studies demonstrated that 100 μg/mL of MPE or MSE counteracted the adipocyte differentiation of 3T3-L1 cells [43]. In this study, we used an in vitro model in which differentiated 3T3-L1 adipocytes were treated with high doses of PA to generate artificially hypertrophied mature adipocytes [44]. Firstly, 3T3-L1 pre-adipocyte cells were differentiated into adipocytes as reported in Section 4 and then treated for 48 h with different doses of PA (100–750 μM) to evaluate their effect on cell viability, in accordance with other authors [45]. Data obtained by MTT assay demonstrated that PA inhibited cell survival in a dose-dependent manner with a reduction of cell viability of $50\%$ with 500 µM PA (Figure 2A). Notably, the addition of 100 µg/mL MPE or MSE increased cell viability by $46\%$ and $77\%$, respectively, in comparison with PA-treated adipocytes (Figure 2B). Microscope images highlighted that the number of cells was reduced in PA-treated adipocyte cells with respect to adipocytes co-treated with PA and MPE or MSE (Figure 2C). In addition, signs of toxicity were observed after PA treatment alone that disappeared after the addition of mango extracts (Figure 2C). Thus, in the following experiments, 100 µg/mL MPE or MSE was used to investigate the mechanism underlying the protective effects of mango extracts on lipotoxicity induced by 500 μM PA. ## 2.2. MPE and MSE Reduce Lipid Accumulation in Adipocytes Exposed to High Doses of PA Excessive lipid availability has been related to adipose tissue hypertrophy [46]. To examine the anti-lipogenic effect of MPE and MSE, differentiated 3T3-L1 adipocytes were treated for 48 h with 500 µM PA in the absence or presence of 100 µg/mL MPE or MSE. Microscope images highlighted that the treatment of mature 3T3-L1 adipocytes with PA increased the content of lipids, as demonstrated by the presence of larger lipid vacuoles with respect to differentiated control 3T3-L1 adipocytes (Figure 2C). Notably, the content of these vacuoles was markedly reduced by MPE and MSE (Figure 2C). These observations were confirmed by staining the cells with Oil Red O (Figure 3A). In comparison with differentiated control adipocytes, 48 h treatment with 500 μM PA resulted in an increase in lipid droplets (LDs) in adipocytes. The addition of 100 µg/mL MPE or MSE to PA-treated adipocytes lowered lipid accumulation in comparison with PA-treated adipocytes. A modest reduction of LDs was also observed in adipocytes not exposed to PA and treated with the extracts alone (Figure 3A). These data were confirmed by microscopic quantification of the Oil Red O staining area (Figure 3B) as well as by measuring the absorbance of the solubilized Oil Red O at 490 nm (Figure 3C). As shown in Figure 3B and 3C, the addition of 100 μg/mL MPE or MSE to PA-treated adipocytes reduced both the staining area and the absorbance of the stained cells by about $30\%$ and $47\%$, respectively in comparison with PA-treated adipocytes alone. Such a reduction in lipid accumulation was also sustained by measuring the TAG content (Figure 3D). The results showed that the intracellular TAG accumulation increased in PA-treated cells by $80\%$ with respect to untreated differentiated adipocytes. Interestingly, the addition of MPE or MSE to PA-treated cells significantly decreased the TAG content by $23\%$ and $34\%$, respectively compared with PA-treated adipocytes (Figure 3D). ## 2.3. MPE and MSE Inhibit PPARγ and Activate AMPK To investigate the molecular basis for the anti-obesity effect of MPE and MSE, we first evaluated whether mango extracts are capable of reducing the level of PPARγ, the master regulator of adipogenesis [47]. Our data supported the conclusion that PPARγ signaling sustained PA-induced hypertrophy in adipocytes. In fact, we observed an increase of $50\%$ of PPARγ levels in adipocytes treated for 48 h with 500 µM PA, with respect to untreated adipocytes (Figure 4). A concomitant increase in the perilipin-2 levels ($80\%$), a lipid droplet coating protein [48], was observed in PA-treated adipocytes (Figure 4). Notably, the addition of MPE or MSE to PA-treated adipocytes reduced the increase in PPARγ to only $18\%$ and $23\%$, respectively as well as that in perilipin-2 to only $15\%$ and $30\%$, respectively, in comparison with PA-treated adipocytes (Figure 4). Next, we examined whether MPE and MSE affects AMPK activation, a kinase promoting catabolic pathways in adipocytes [49]. As shown in Figure 4, the expression of the phosphorylated and active form of AMPK lowered in PA-treated differentiated 3T3-L1 adipocytes compared with control adipocytes. Interestingly, MPE or MSE alone and in the presence of PA significantly enhanced the phosphorylated form of AMPK (p-AMPK) (Figure 4). This is in line with our previous study demonstrating that MPE and MSE activate AMPK during adipocyte differentiation [43]. Moreover, the addition of MPE or MSE in control adipocytes as well as in PA-treated adipocytes increased the levels of the phosphorylated and inactive form of acetyl-CoA-carboxylase (p-ACC) (Figure 4), the key enzyme of fatty acid synthesis, which is inactivated by phosphorylation by AMPK [49]. Finally, our data also demonstrated that MPE and MSE markedly increased the phosphorylated and active form of hormone sensitive lipase (p-HSL), the enzyme activating lipolysis in adipocytes [50], by $30\%$ and $65\%$, respectively (Figure 4). ## 2.4. MPE and MSE Reduce PA-Induced ER Stress in 3T3-L1 Adipocytes Elevated levels of FAs, in particular saturated fatty acids (SFAs) such as PA, have been shown to produce ER stress in a number of cell types, including adipocytes [51]. The activation of ER stress, in turn, represents a potential molecular mechanism of lipotoxicity [52]. We thus examined whether high doses of PA induce ER stress in mature adipocytes and the ability of MPE and MSE to counteract it. Interestingly, we observed an increase in ER stress protein markers, evidenced by an up-regulation in the expression of PERK, GRP78 and CHOP, as well as in JNK phosphorylation following the treatment of mature 3T3-L1 adipocytes with 500 µM PA for 48 h (Figure 5). These results suggest that the ER-associated unfolded protein response (UPR) pathway is activated by PA [53]. Notably, the addition of 100 µg/mL MPE or MSE to PA-treated differentiated adipocytes reduced the levels of all ER stress protein markers (Figure 5), thus suggesting the ability of mango extracts to counteract ER stress. ## 2.5. MPE and MSE Prevent PA-Induced ROS Production It has been reported that free FAs generate ROS in different cell types, including adipocytes [19,54]. Thus, to evaluate whether PA increased intracellular ROS production, differentiated 3T3-L1 adipocytes were incubated with H2DCFDA, a specific fluorescent probe that visualizes intracellular ROS [55]. H2DCFDA-associated fluorescence was elevated by $65\%$ after incubation with 500 µM PA for 48 h compared with untreated differentiated 3T3-L1 adipocytes (Figure 6A,B). Interestingly, the addition of 100 µg/mL MPE or MSE markedly reduced ROS content to $35\%$ and $23\%$ compared with adipocytes only treated with PA (Figure 6A,B), thus highlighting that mango extracts counteract ROS production and oxidative stress induced in adipocytes after PA treatment. In addition, propidium iodide (PI) staining of cells confirmed the induction of cytotoxic effects in PA-treated differentiated adipocytes. PA treatment increased cell death by about $35\%$ compared with control adipocyte cells (Figure 7A,B). These effects were counteracted by the addition of 100 µg/mL MPE or MSE that markedly reduced cell death by about $57\%$ and $65\%$, respectively, with respect to PA-treated adipocytes. The cytotoxic effects induced by PA in adipocytes seem to be related to apoptosis induction. Pro-caspase-3 is a master apoptosis protein marker cleaved in active form during this process [56]. PA treatment decreased the level of pro-caspase-3 by $43\%$ (Figure 7C,D) and promoted the appearance of the cleaved active form of caspase-3. Notably, caspase activation was counteracted by the addition of MPE or MSE (Figure 7C,D). Our previous studies provided evidence that MPE and MSE contain factors capable of exerting ROS scavenger effects during 3T3-L1 adipocyte differentiation [43]. These effects have been correlated with the ability of mango extracts to increase Nrf2, the main antioxidant transcription factor [57], during adipocyte differentiation [43]. In accordance with our previous data, we demonstrated that in PA-treated adipocytes, MPE or MSE increased the level of Nrf2 by about $40\%$ and $60\%$, respectively (Figure 8). Our data also showed that the levels of MnSOD and HO-1, two scavenger enzymes transcriptionally regulated by Nrf2 [57,58], markedly increased after treatment with MPE or MSE. In particular, the increase in MnSOD in the presence of MPE or MSE was estimated to be $46\%$ and $50\%$, while that of HO-1 was estimated to be $12\%$ and $28\%$, respectively. ## 3. Discussion The current study was designed to investigate whether extracts of mango peel (MPE) and seed (MSE) could ameliorate PA-induced lipotoxicity in adipocytes. Peel and seed are the main bio-waste products of mango processing. In an earlier study, we demonstrated that MPE and MSE have the ability to reduce the number of adipocytes by preventing adipocyte differentiation of 3T3-L1 pre-adipocyte cells [43]. In the present study, we provided evidence that MPE and MSE are also capable of lowering adipocyte hypertrophy induced by high doses of PA, the main saturated long fatty acid present in the diet [59]. Notably, we demonstrated that MPE and MSE reduced PA-induced fat accumulation, as evidenced by the decrease in LD and TAG content in differentiated 3T3-L1 adipocytes co-treated with PA and MPE or MSE. The ability of MPE and MSE to reduce lipid content in PA-treated adipocytes results from both stimulation of lipolysis and inhibition of lipogenesis. PPARγ is a transcription factor that has been reported to play a critical role in adipocyte hypertrophy under high fat diets [60]. We provided evidence that the PPARγ level increased under PA-treatment in differentiated 3T3-L1 adipocytes. Notably, this effect was markedly counteracted by the addition of MPE or MSE to PA-treated adipocytes. These results are in line with our previous data demonstrating that MPE and MSE counteract 3T3-L1 adipocyte differentiation by reducing the level of PPARγ and its target FABP4 [43]. Furthermore, our data showed that MPE and MSE significantly enhanced the phosphorylation of AMPK and its substrate acetil-CoA carboxylase (ACC) in both controls and PA-treated adipocytes, thus suggesting a role of AMPK activation in reducing lipogenesis induced by MPE and MSE. AMPK is an important regulator of lipid metabolism [61]. Activation of AMPK by phosphorylation increases lipolysis and fatty acid oxidation, while inhibiting lipogenesis [62]. AMPK inactivates by phosphorylation ACC, the key enzyme of fatty acid synthesis [63], leading to the reduction of fatty acid synthesis [64]. Different phenolic compounds contained in plants and fruits, such as quercetin, curcumin, resveratrol and gallic acid, exert anti-obesity effects by activating AMPK [61]. We previously characterized the composition of peel and seed extracts of Sicilian mango fruits by HPLC/MS and demonstrated the presence of different polyphenols, among which methyl digallate and methyl gallate are the most represented components [34,43]. These compounds could be responsible for the anti-lipogenic effects of the mango extracts. In line with this conclusion, Fang et al. [ 65] demonstrated that gallotannin derivatives from mango counteract adipogenesis by activating AMPK. In addition, Lu, et al. showed that gallic acid reduced lipogenesis and improved liver steatosis by activating AMPK [66]. This effect could result by a direct interaction of gallic acid with AMPKα/β subunits, as evidenced by computational docking analysis [66]. Finally, mangiferin, a polyphenol derived from Mangifera indica promotes browning of adipocytes by activating AMPK [67]. In this study, we also provided evidence that MPE and MSE increased the level of the phosphorylated and active forms of hormone-sensitive lipase (HSL), the key lipase activating lipolysis of TAGs in adipocytes, in PA-treated adipocytes [68]. Different lipolytic agents activate HSL by increasing cAMP levels, with the consequent activation of cAMP-dependent protein kinase (protein kinase A; PKA). This enzyme in turn phosphorylates and activates HSL [69]. MSE and MPE could activate HSL because of their content of polyphenols. In line of this conclusion, it has been shown that different polyphenols are able to increase cAMP by inhibiting phosphodiesterase, the enzyme that degrades cAMP [70,71]. A high content of SLFAs has been associated with lipotoxicity in adipocytes as a consequence of ER stress induction [72]. Notably, when present at a high level, PA is metabolized into saturated DAG and saturated lysophosphatidylcholine [19]. These PA-derived metabolites accumulate in the ER, causing destructive changes in its structure and the activation of ER stress sensors [19]. In line with these observations, we demonstrated that PA treatment enhanced the ER stress markers GRP78, PERK and CHOP as well as activated JNK by increasing its phosphorylated form in differentiated 3T3-L1 adipocytes. ER stress is a protective cellular mechanism that initiates the unfolded protein response (UPR) to restore cellular homeostasis [73]; however, in severe ER stress, the adaptive response fails and apoptotic cell death is induced [73]. In obese animals, elevated ER stress is present in different organs [74,75]. In this condition, ER stress-induced UPR activates JNK, which in turn promotes apoptosis by inhibiting the mitochondrial respiratory chain and activating caspases [76]. Our data confirmed that PA causes lipotoxicity in differentiated adipocytes, as evidenced by cell viability reduction, increased PI-positive cells and caspase-3 activation. Interestingly, MPE and MSE counteracted PA-induced ER stress by lowering all ER stress markers, GRP78, PERK and CHOP, as well as p-JNK. Concomitantly, mango extracts restored cell viability, reduced PI-positive cells and the activation of caspase-3 induced by PA treatment, thus suggesting their protective effects against lipotoxicity induced by high levels of SFAs in adipocytes. Furthermore, we demonstrated that PA treatment increased in 3T3-L1 adipocytes the level of ROS, as evidenced by staining adipocytes with H2DCFDA. This finding is in line with previous reports demonstrating that high levels of fatty acid increase oxidative stress in adipocytes [77]. It has been reported that ceramide and DAG, which are fatty acid-derived lipid metabolites, activate NADPH oxidase (NOX), enhancing the ROS level in adipocytes [78]. In addition, dysfunction of the mitochondrial respiratory chain in obesity can amplify oxidative stress and inflammation [79]. ROS production has been shown to activate JNK, which mediates activation of NF-κB and AP-1 [80] with the consequent enhanced expression of pro-inflammatory cytokines, such as IL-6 and TNFα. Notably, we showed that the production of ROS in PA-treated adipocytes was markedly reduced by the addition of MPE and MSE. This effect could be a consequence of the high content of polyphenols in mango extracts. This is in line with the observation that methyl-gallate, the main component of MPE and MSE, protects the cells against oxidative damage through its ROS scavenger ability [81]. Furthermore, the lowering in ROS content induced by MPE and MSE could be a consequence of the up-regulation of Nrf2 and its transcriptional targets MnSOD and HO-1, two important antioxidant enzymes [34,43]. The activation of Nrf2, the main transcriptional factor against exogenous and endogenous oxidative stress injury [82,83], has been reported in different dietary polyphenols, including resveratrol, gallic acid and caffeic acid [84]. The mechanisms underlying Nrf2 activation include increased Nrf2 nuclear translocation, inhibition of Keap1-Nrf2 interaction and enhanced Keap1 ubiquitination [84]. Finally, MPE and MSE could reduce ROS levels and oxidative stress in adipocytes by activating AMPK. In line with this hypothesis, the deregulated activity of AMPK has been associated with an inflammatory state in in vivo models of obesity and obese patients [85]. Indeed, the activation of AMPK signaling has been shown to protect against oxidative stress by suppressing NOX [86] and mitochondrial dysfunction [87]. ## 4.1. MPE and MSE Preparation Peel and seed extracts were obtained from mango fruits (Mangifera Indica L.) cultivated in Sicily (Italy), as reported before [43]. In particular, after washing with distilled water, the peels and seeds of mango fruits were cut and lyophilized (Hetosicc Lyophilizer Heto CD 52-1). Then, an ethanol:PBS 1:1 solution was used in order to solubilize the lyophilized products by keeping them overnight at 37 °C under constant shaking. The final concentration of both the extracts was 75 mg/mL. Then, we centrifuged both the extracts of MPE and MSE at 120× g for 10 min. The obtained supernatants of MPE and MSE were centrifuged again at 15,500× g for 10 min and then the extracts (supernatants) were frozen at −20 °C until use. MPE and MSE working solutions were prepared by diluting them to the final concentration in culture medium. The final concentration of ethanol in the extracts showed no toxicity on differentiated 3T3-L1 adipocytes. ## 4.2. PA Solution Preparation PA was solubilized in an EtOH $10\%$ solution (25mM) in a heated and stirred water bath at 65 °C for 15 min. Once completely solubilized, a 500 µM working dilution was appropriately prepared in culture medium containing $5\%$ BSA and incubated at 37 °C for 1 h under constant shaking to ensure their conjugation before adding it to differentiated 3T3-L1 adipocytes. Vehicle containing $5\%$ BSA was used as control (differentiated 3T3-L1 adipocytes, Diff). ## 4.3. Cell Cultures A mouse 3T3-L1 pre-adipocyte cell line from the American Type Culture Collection (ATCC) was maintained in culture as monolayer in flasks of 75 cm2, at 37 °C and in a $5\%$ CO2 humidified incubator in DMEM (Euroclone, Pero, Italy), supplemented with $10\%$ (v/v) heat-inactivated fetal bovine serum (FBS; Euroclone, Pero, Italy), 2 mM L-glutamine (BioWest SAS, Nuaillé, France), 100 U/mL penicillin and 50 µg/mL streptomycin (Euroclone, Pero, Italy). Once $80\%$ of confluence was reached, 3T3-L1 pre-adipocytes were detached from the flasks using trypsin-EDTA (0.5 mg/mL trypsin and 0.2 mg/mL EDTA) and seeded according to the experimental conditions. All compounds and reagents used for our experiments, unless otherwise stated, were purchased from Sigma-Aldrich (Milan, Italy). ## 4.4. Adipocyte Differentiation, Reagents and Treatments Differentiated 3T3-L1 adipocytes were obtained from 3T3-L1 pre-adipocyte cells (undifferentiated cells) as previously reported [43]. In particular, 3T3-L1 cells were seeded at 0.2 × 105/well in 24-well plates or 0.8 × 105/well in 6-well plates and kept until the confluence was reached. Then, after two days post-confluence, undifferentiated cells were incubated with a differentiation culture medium constituted by DMEM supplemented with $10\%$ (v/v) heat-inactivated FBS, 2 mM L-glutamine, $1\%$ Non-Essential Amino Acids, 100 U/mL penicillin and 50 µg/mL streptomycin, containing the pro-differentiative agents 0.5 mM 3-isobutyl-1-methylxanthine (IBMX), 1 µM dexamethasone and 10 µg/mL insulin. After another three days, the differentiation medium was removed and maintenance culture medium (DMEM supplemented with $10\%$ (v/v) heat-inactivated fetal bovine serum, 2 mM L-glutamine, $1\%$ Non-Essential Amino Acids 100 U/mL penicillin and 50 µg/mL streptomycin containing 10 µg/mL insulin) was added and left for 5 days. Complete differentiation was reached at day 8 when the cells showed typical features of mature adipocytes, such as LD formation and TAG accumulation. PA alone or in the presence of 100 μg/mL MPE or MSE was added to differentiated 3T3-L1 adipocytes and kept for 48 h. ## 4.5. Cell Viability Assessment Cell viability was evaluated by measuring mitochondrial dehydrogenase activity using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), as reported before [88]. For the cell viability assay, undifferentiated 3T3-L1 cells were seeded in 96-well plates (8 × 103 cells/well) until complete differentiation. Then, differentiated 3T3-L1 adipocyte cells were exposed to different concentrations of PA alone or in the presence of 100 μg/mL MPE or MSE for 48 h. Then, 20 µL of MTT reagent (11 mg/mL diluted in PBS) was added to each well and incubated for another 2 hours at 37 °C. The colored crystals of the formazan produced by viable cells were dissolved by adding 100 μL of lysis buffer containing $20\%$ sodium dodecyl sulphate in $50\%$ N,N-dimethylformamide, pH 4.0 and the absorbance was measured by a microplate reader (OPSYS MR, Dynex Technologies, Chantilly, VA, USA) at 540 nm with a reference wavelength of 630 nm. Cell viability was measured as the percentage of the optical density (OD) values found in treated cells compared with those found in untreated cells as control. The cytotoxic effects of PA on differentiated 3T3-L1 adipocyte cells were also evaluated by propidium iodide (PI) staining. Differentiated cells were treated with 500 µM PA alone or together with 100 μg/mL MSE or MPE. After 48 h of treatment, cells were washed and stained with PI. After a short incubation at the dark, the fluorochrome in excess was removed and the cells were analyzed by fluorescence microscopy using excitation and emission wavelengths appropriate for PI fluorescence (λex = 488 nm and λem = $\frac{610}{620}$nm). ## 4.6. Oil Red O Staining of Treated Mature 3T3-L1 Adipocytes Oil Red O staining (Sigma-Aldrich, St. Luois, MO, USA) was performed for evaluating LD accumulation. Oil Red O stock solution was prepared by solubilizing 0.35 gr in 100 mL isopropanol $100\%$. Once differentiated in a 24-well plate, differentiated 3T3-L1 adipocytes were fixed by incubation in $10\%$ formaldehyde for 30 min, washed with PBS and rinsed with $60\%$ isopropanol for 5 min until they were completely dry. Fixed cells were then stained with Oil Red O working solution (3:2, stock solution—dH2O) for 10 min and then washed with dH2O several times. Red pixel areas, stained by Oil Red O, detecting LDs, were divided by the total area scanned. The whole bottom surface of a single well from a 24-well plate was analyzed for the establishment of LD production. A Leica DM-IRB microscope was used and pictures were taken by a Leica DC300F digital camera with a Leica IM50 software, as representative images of the experimental conditions. The pictures were analyzed in ImageJ, converted into high-contrast black and white images to visualize LDs and scored as the percentage area per field. Finally, Oil Red O quantification was performed by measuring its absorbance at 490 nm after extraction of the dye by $100\%$ isopropanol for 10 min. The percentages of the OD values found in treated cells were compared with those found in untreated differentiated 3T3-L1 cells as control. ## 4.7. ROS Detection ROS production was detected through the oxidation of the cell-permeant 2′,7′-dichlorodihydrofluorescein diacetate (H2DCFDA) (Molecular Probe, Life Technologies, Eugene, OR, USA) dye, as reported before [89]. Differentiated 3T3-L1 adipocytes were treated with 500 μM PA in absence or presence of 100 μg/mL MPE or MSE for 48 h. Then, the cells were washed in PBS and incubated with 10 μM H2DCFDA dye for 30 min in the dark and in the presence of $5\%$ CO2 at 37 °C. At the end of incubation, the fluorochrome in excess was removed washing in PBS and the fluorescent 2′,7′-dichlorofluorescein (DCF), produced by intracellular oxidation, was analyzed by fluorescence microscopy using excitation and emission wavelengths appropriate for green fluorescence (FITC filter with λex = 485 nm and λem = 530 nm). ## 4.8. TAGs Evaluation Differentiated 3T3-L1 adipocytes were treated with 500 μM PA in absence or presence of 100 μg/mL MPE or MSE for 48 h. Then, the cells were lysed with $5\%$ NP-40 and the number of TAGs in the supernatants was quantified by a spectrophotometric commercial kit for triglyceride determination (SENTINEL C H. SpA, Milan, Italy) [43]. A standard curve with different TAG concentrations, normalized to total cellular protein content measured by Bradford assay, was used for quantifying the samples’ TGA concentrations. ## 4.9. Western Blot Procedures Protein levels were detected by western blotting analysis. Differentiated and treated cells were lysed as reported before [90]. Bradford Protein Assay was used to quantify protein concentration (Bio-Rad Laboratories S.r.l., Segrate, Milan, Italy). Afterwards, the same number of proteins (30 μg/sample) was loaded and underwent sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE). Finally, gels were blotted onto a nitrocellulose membrane (Bio-Rad). Immunodetection was then performed, incubating the filters with specific primary antibodies against PERK (ab65142) purchased from Abcam (Cambridge, UK), namely, GRP78 (sc-166490), phosphorylated-JNK (sc-6254), CHOP (sc-793), PPARγ (sc-7273), MnSOD (sc-133254) and caspase-3 (sc-65487), all purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Phosphorylated-ACC (#07-303) was purchased from EMD Millipore Corporation (Temecula, 40 CA, USA), phosphorylated-AMPKα (#2535) and phosphorylated-HSL (#4126) were purchased from Cell Signaling (Danvers, MA, USA); Nrf2 (NBP1-32822) and Perilipin-2 (NB110-40877SS) was purchased from Novus Biologicals (Bio-Techne SRL, Milan, Italy); additionally, HO-1, Heme Oxygenase 1 (orb5455) was purchased from Biorbyt Ltd. (Cambridge, UKi). Immunoreactive signals, developed through HPR-conjugated secondary antibodies (Amersham, GE Healthcare Life Science, Milan, Italy), were detected using enhanced chemiluminescence (ECL) reagents (Cyanagen, Bologna, Italy) and obtained with ChemiDoc XRS (Bio-Rad, Hercules, CA, USA). A quantification of the signal was performed by Quantity One 1-D Analysis software (Bio-Rad) and γ-Tubulin (T3559; Sigma-Aldrich) was used for loading normalization. ## 4.10. Statistical Analysis All the experiments and their determinations were performed in triplicate. Data were represented as mean ± S.D and the statistical significance was provided. Data analysis was performed using the GraphPadPrismTM 4.0 software (Graph PadPrismTM Software Inc., San Diego, CA, USA). The differences between groups were evaluated using Tukey’s test following one-way ANOVA test. A p-value < 0.05 was considered the threshold for statistical significance. When not specified, the data were not significant with respect to the related control. ## 5. Conclusions In conclusion, the present study demonstrated that MPE and MSE protect against PA-induced lipotoxicity in differentiated 3T3-L1 adipocytes by reducing lipid content and oxidative stress. These anti-obesity effects of MPE and MSE might partly involve the inhibition of lipogenesis, the activation of lipolysis and the induction of antioxidant effects. A representative picture of the anti-lipolytic and anti-oxidative effects of MPE and MSE is reported in Figure 9. In light of the chemical data providing evidence of MSE and MPE composition, we wondered about the putative phytochemicals responsible for the effect observed in 3T3-L1 adipocytes exposed to MPE or MSE treatment. A possible candidate seems to be methyl gallate. This is a phenolic compound that is the most represented phytochemical in our tested mango extracts. Our hypothesis is also sustained by experimental evidence reported by Roh et al. [ 91] demonstrating that methyl gallate is able to counteract the lipid accumulation in 3T3-L1 cells and could represent a good candidate as an anti-obesity agent. However, we cannot exclude that the ability of MPE and MSE to counteract PA lipotoxicity, and as hypertrophy and ER stress induced by PA exposure could be ascribed to a combined or synergistic effect among the different phytochemicals identified in mango. To better elucidate this aspect, in our future studies we will test mango phytochemicals as compounds alone and their combinations on 3T3-L1 cells. Our data offer novel perspectives suggesting that MPE and MSE may be associated with the reduced metabolic dysfunction of adipose tissue induced by high levels of SLFAs. Thus, the development of mango extract-rich foods could be useful to counteract obesity and its consequences. ## References 1. Lin X., Li H.. 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--- title: 'The Effect and Cost-Effectiveness of Offering a Combined Lifestyle Intervention for the Prevention of Cardiovascular Disease in Primary Care: Results of the Healthy Heart Stepped-Wedge Trial' authors: - Emma A. Nieuwenhuijse - Rimke C. Vos - Wilbert B. van den Hout - Jeroen N. Struijs - Sanne M. Verkleij - Karin Busch - Mattijs E. Numans - Tobias N. Bonten journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC10048996 doi: 10.3390/ijerph20065040 license: CC BY 4.0 --- # The Effect and Cost-Effectiveness of Offering a Combined Lifestyle Intervention for the Prevention of Cardiovascular Disease in Primary Care: Results of the Healthy Heart Stepped-Wedge Trial ## Abstract Objective: To evaluate the effectiveness and cost-effectiveness of offering the combined lifestyle programme “Healthy Heart”, addressing overweight, diet, physical activity, smoking and alcohol, to improve lifestyle behaviour and reduce cardiovascular risk. Design: A practice-based non-randomised stepped-wedge cluster trial with two-year follow-up. Outcomes were obtained via questionnaires and routine care data. A cost–utility analysis was performed. During the intervention period, “Healthy Heart” was offered during regular cardiovascular risk management consultations in primary care in The Hague, The Netherlands. The period prior to the intervention period served as the control period. Results: In total, 511 participants (control) and 276 (intervention) with a high cardiovascular risk were included (overall mean ± SD age 65.0 ± 9.6; women: $56\%$). During the intervention period, 40 persons ($15\%$) participated in the Healthy Heart programme. Adjusted outcomes did not differ between the control and intervention period after 3–6 months and 12–24 months. Intervention versus control ($95\%$ CI) 3–6 months: weight: β −0.5 (−1.08–0.05); SBP β 0.15 (−2.70–2.99); LDL-cholesterol β 0.07 (−0.22–0.35); HDL-cholesterol β −0.03 (−0.10–0.05); physical activity β 38 (−97–171); diet β 0.95 (−0.93–2.83); alcohol OR 0.81 (0.44–1.49); quit smoking OR 2.54 (0.45–14.24). Results were similar for 12–24 months. Mean QALYs and mean costs of cardiovascular care were comparable over the full study period (mean difference ($95\%$ CI) QALYs: −0.10 (−0.20; 0.002); costs: EUR 106 (−80; 293)). Conclusions: For both the shorter (3–6 months) and longer term (12–24 months), offering the Healthy Heart programme to high-cardiovascular-risk patients did not improve their lifestyle behaviour nor cardiovascular risk and was not cost-effective on a population level. ## 1. Introduction Cardiovascular diseases cause a high burden of morbidity and rising health care expenditures [1,2]. They are known to be mainly caused by unhealthy lifestyle behaviour such as poor-quality diet, insufficient physical activity and smoking. Therefore, the key component of prevention and proactive clinical treatment might comprise changing unhealthy lifestyle [3]. Thereby, potentially more than one cardiovascular risk factor can be improved at the origin. Lifestyle changes as part of a cardiovascular risk management approach can be initiated at the level of primary care, for example by the general practitioner (GP), but may further be guided by other dedicated lifestyle professionals. This process could potentially be supported by health care professionals following the needs and possibilities of the patient. When available, GPs can refer to combined lifestyle intervention programmes, provided by lifestyle coaches who help patients to improve multiple lifestyle factors. Structured examples of such programmes are suggested to reduce cardiometabolic risk [4,5,6]. Several combined lifestyle interventions in The Netherlands have been recognised as being effective for a 1–3 kg weight reduction in people with obesity in the short term (1 year) and longer term (1.5–2 years) [7,8,9]. However, other cardiovascular risk factors such as blood pressure were only examined to a limited extent. Furthermore, implementation still faces several barriers [9,10,11,12] and may be more difficult in a socio-economically diverse urban area [13,14]. Additionally, the evidence of the cost-effectiveness of lifestyle programmes is inconclusive, especially when analysed on a population level [15,16,17]. Initiation of a healthy lifestyle is often considered to be time-consuming and influenced by the motivation and commitment of both patients and care providers [18,19,20]. In previous studies intention- and goalsetting appeared to have a positive effect on behaviour change [20,21] and should, therefore, be the starting point of all lifestyle changes. Awareness of the importance of lifestyle in the treatment and secondary prevention of cardiovascular disease, by both the care provider and patient, may act as a window of opportunity [22] leading to lifestyle change [23]. Discussing the importance of a healthy lifestyle, and the possibility to be supported by a combined lifestyle intervention during a consultation, may encourage behavioural change and thus lead to positive changes in lifestyle behaviour, even if a patient is not referred to a lifestyle programme. However, the effect and cost-effectiveness of implementing such a lifestyle programme on practice level in primary care is unknown. Therefore, in this study we aimed to examine the (cost-)effectiveness on a population level of offering referral to a combined lifestyle programme—“Healthy Heart”—as part of cardiovascular risk management in primary care in an urbanised, socio-economically diverse area. We examined the effect of offering the programme on weight, systolic blood pressure (SBP), cholesterol levels, diet quality, physical activity, smoking and alcohol consumption in the target population. Furthermore, we examined goalsetting on lifestyle behaviour and self-reported achievement of goals. To examine the cost–benefit of implementing the programme we performed a cost–utility analysis. With this study, we provide new insights on the effects of offering a lifestyle intervention in a real-life primary care setting. ## 2.1. Study Design and Setting A detailed description of the study and Healthy Heart programme was published elsewhere [24]. In short, this study is a practice-based, non-randomised stepped-wedge cluster trial including 56 primary care practices (clusters) in the region of The Hague (the Netherlands) with ~1,000,000 inhabitants. During the intervention period, potential participation in a combined lifestyle programme called “Healthy Heart” was discussed with participants and referral to the programme was offered as part of a structured cardiovascular risk management programme. Participants could either be referred or choose to receive regular care without support by the lifestyle coaches. During the control period, participants received regular care. The active trial recruitment period was between June 2017 and April 2019: each practice could recruit patients for study participation for one year in total with first a 2–6 month control (regular care) period and then a 4–10 month intervention (optional referral) period (Figure 1). ## 2.2. Participants Adults (>18 years) eligible to participate in the study were estimated to be at high risk for the development of cardiovascular disease (>$10\%$ 10-year cardiovascular morbidity risk) [25], and were recruited during routine cardiovascular preventive care by their GP or practice nurse (PN). Persons with pre-existing cardiovascular disease, diabetes mellitus, or significant or palliative comorbidities were not recruited. After oral and written informed consent, participants filled in questionnaires. Patients recruited during the intervention period discussed their lifestyle goals with their PN or GP and proceeded, based on shared decision-making, with standard care only or were in addition referred to a certified lifestyle coach who offered the Healthy Heart programme. During the control period, participants were provided standard preventive care [25]. Questionnaire data were linked to routine primary electronic health record (EHR) data for the analysis. During the full recruitment period, participants were also given the option to provide informed consent for use of their EHR data only, without filling in questionnaires, while receiving standard care. ## 2.2.1. Healthy Heart Lifestyle Programme The five-month lifestyle programme to which participants could be optionally referred was predesigned by the care group based on available evidence. It consisted of eight group sessions (8–10 persons) and three individual sessions supervised by a lifestyle coach, in which all aspects of lifestyle behaviour change (dietary quality, overweight, physical activity, smoking, alcohol intake and stress management), motivation and personal goals on behaviour change were addressed. The programme took place in patients’ own neighbourhoods and was free of charge. ## 2.2.2. Outcomes and Sources Effectiveness Study The primary outcomes were lifestyle behaviour—and cardiovascular risk factors (described below) in the period 3–6 months (short term) and 12–24 (longer term) months after baseline. The secondary outcome was the number of cardiovascular-risk-management (CVRM)-related primary practice consultations in the past 6 months and the difference in weight, physical activity, dietary quality and alcohol usage between those who set a goal at baseline for these outcomes and those who did not. Outcomes were measured at baseline, 3, 6, 12 and 24 months from digital or hard-copy questionnaires or continuously between baseline and 27 months from the EHR. An overview of all measurements is displayed in Supplementary Material File S1. Outcomes derived from questionnaires were total minutes of physical activity per week (Short Questionnaire to Assess Health-enhancing physical activity), dietary quality (total score from the Dutch Healthy Diet (DHD) index; higher scores indicate higher diet quality) and smoking (never/previous/current smoker). Patient-reported smoking cessation was defined as being a smoker at baseline and reported ‘no smoking during the last 7 days’ (3–6 months) or ‘no smoking during the last 6 months’ (12–24 months) at follow-up measurements [24]. Outcomes derived from the EHR were systolic blood pressure (SBP) (mmHg), LDL-cholesterol (mmol/L), HDL-cholesterol (mmol/L). EHR measurements were selected as the nearest to the exact date of baseline, baseline plus 3, 6, 12 and 24 months in a six-week range around baseline, baseline plus 3 and 6 months and a two-month range around baseline plus 1 and 24 months. CVRM-related visits during the 6 months prior to baseline, baseline–6 months, 6–12 months, 12–18 months and 18–24 months, respectively, were derived from the EHR, reasoning that a record of an anamnestic (e.g., alcohol intake), SBP measurement or laboratory test indicated either a live or telephone visit to the primary practice. Weight and alcohol consumption were primarily derived from questionnaires (patient-reported weight (kg) and mean number of units of alcohol per day, calculated from the DHD-index). When patient-reported data were missing, the data were filled in with weight (kg) registrations in the EHR registered with ‘average alcohol use’ in the EHR. For the EHR-derived data, all registered measurements between 3–6 months and 12–24 months were included in the effect analysis. ## 2.2.3. Utilities, Healthcare Use and Costs Utilities reflect the valuation of quality of life, on a scale from zero (as bad as death) to one (perfect health). Utility scores at baseline, 6, 12 and 24 months were calculated using the 5-level EQ-5D (EQ-5D-5L) using the Dutch tariff [26]. As sensitivity analyses, utilities were calculated from the SF-6D, as calculated from the SF-12 questionnaire (US version) [27] and the EQ-5D visual analogue scale (VAS), using the transformation 1 − (1 − VAS/100)1.61 [28]. Health care use in the preceding 6 months (including GP, medical specialists, dietician, physiotherapist, lifestyle coach (individual or in group), home and hospitalisation care) was patient-reported using questionnaires at baseline and, respectively, 6, 12, 24 months after baseline. Healthcare use between 12 and 18 months was interpolated from the questionnaires at 12 and 24 months. Cardiovascular medication prescriptions and laboratory procedures during the period baseline–24 months were derived from the EHR. Costs from a healthcare perspective were calculated in Euros at price-level 2021. Intervention costs included the individual participant costs for the Healthy Heart programme (EUR 434.59 per participant) and an information meeting for primary practices (189.59 per participant). Other health care was valued using Dutch standard prices for medication [29] (assuming generic products with standard daily dosage, plus administrative costs per prescription), for laboratory procedures [30] and for other health care [31]. ## 2.2.4. Determinants Baseline measurements were age, sex (patient-reported or from EHR) and origin (Dutch/non-Dutch), educational status (low (no education or vocational training)/high (college or university)), living status (living alone/co-habiting), job status (currently employed/non-employed (<65 years)/retired) (patient-reported), comorbidities (EHR). Neighbourhood liveability index, as measure of socio-economic position, was derived from governmental publicly available data [32]. Comorbidities (yes/no) were defined as active diagnosis registered with International Classification of Primary Care (ICPC) codes before 3 months after baseline and were reported based on their prevalence in the study population and clinical relevance. Comorbidity groups were: hypercholesterolaemia, hypertension, vascular, psychiatric and ‘other chronic’ comorbidities [33]. Self-efficacy was measured by the General Self-Efficacy Score and quality of life by the EQ-5D-visual analogue scale (VAS) (Supplementary Material File S1). Goalsetting (yes/no), motivation (0–10 scale) and self-confidence of achieving this goal (0–10 scale) for each lifestyle goal (more exercise, improve diet quality, intentional weight loss, lower alcohol intake, quit smoking) were assessed at baseline. Self-perceived achievement (yes/no) of these goals was assessed at 3 and 6 months. ## 2.3.1. Effectiveness Study Participants recruited during two observation periods were compared: the control period (all patients receiving standard care) versus the intervention period (all patients receiving standard care and who were offered to participate in the Healthy Heart programme). A third group of patients, who did not complete questionnaires, served as an extra control group for the outcome measurements derived from the EHR, as this group was not biased by motivation for completing or the content of the questionnaires. The baseline measurement for this group was set as their first GP visit after 1 July 2017 (start of the recruitment period). Differences between the intervention and control period in goalsetting and self-perceived achieved goals were assessed with chi-square tests and differences in motivation and confidence in lifestyle changes were assessed with independent t-tests. To examine the outcomes, multivariate linear, ordinal (alcohol) and Poisson (number of visits to primary practice) mixed models were used. Mixed models for [1] the period 3–6 months and [2] 12–24 months after baseline were calculated with period (control/intervention) as fixed effect and with random effects for participant and practice, adjusted for baseline measurement of the outcome, sex, age, antihypertensive- (SBP model) and lipid-lowering medication usage (LDL- and HDL-cholesterol model) and total general self-efficacy scale (alcohol model). For weight, SBP and cholesterol measurements, a period of 1.5 months–7.5 months and 10–26 months was included in the analysis to include all in-between measurements in the models. For CVRM-related practice-consultations, the timeframes 0–12 and 12–24 months were analysed. To assess effect modification with sex and age, an interaction term with group and these determinants were added to the models. Due to a limited number of current smokers, difference between the control and intervention period on smoking cessation (yes) was examined using logistic regression analysis with 7-day abstinence at 3 or 6 months and 6-month abstinence at 12 or 24 months. We performed two in-depth analyses. First, in order to examine the effect of the Healthy Heart programme itself, we repeated the models with participants who participated in the programme compared to those who did not (recruited both during the control and intervention period). Additionally, to assess external validity, we repeated the models regarding weight, SBP and cholesterol-levels of participants recruited during the intervention period compared to participants in the EHR-group for 12–24 months. Since routine consultations occur annually, measurements at 3–6 months in the EHR are likely to mainly include suboptimal outcomes. ## 2.3.2. Economic Evaluation Difference in costs and QALYs between the intervention and control period were estimated using linear regression analysis, adjusted for sex, age, recruitment period (control/intervention) and baseline values of costs and utilities. Cost-effectiveness acceptability curves (CEACs) were plotted, showing the probability that the intervention is cost-effective compared with the control period, for a range of threshold values for willingness to pay (WTP) per additional QALY [34]. ## 2.3.3. Missing Data For the economic evaluation, missing data were imputed using multiple imputation, with 100 imputed datasets. Predictors in the imputation procedure included inclusion period (control/intervention), sex, age, EQ-5D utilities, medication costs, GP cardiovascular risk management (CVRM) costs, GP other costs and total medical specialist outpatient costs (Supplementary Material File S3). Analyses were performed with SPSS version 25.0.02 and R version 2022.02.0, lme4 and ordinal packages. ## 3.1. Recruitment and Characteristics A total of 511 patients were included during the control period, and 276 during the intervention period, of which 451 ($88\%$) and 251 ($91\%$) could be linked to EHR data. During the intervention period, 40 persons ($15\%$) participated in the Healthy Heart programme. A total of 155 participants were included in the EHR-only group. Persons who did not complete the questionnaires, nor could be linked to the EHR, were excluded from the analysis ($$n = 16$$ and for the economic evaluation $$n = 17$$) (Figure 2). Baseline characteristics were comparable between the periods (Table 1). Overall, over half of the population (mean age 65.0 ± 9.6) were women ($$n = 442$$ ($56\%$)), most were of Dutch origin ($$n = 659$$ ($88\%$) and lived in a neighbourhood with high liveability index ($$n = 497$$ ($72\%$)). There were no differences between the groups in numbers of missing data, except for the questionnaire drop-out rate at 24 months (intervention: 93 ($34\%$) vs. control: 123 ($24\%$)). Missing data in the patient-reported outcomes increased during follow-up. In the EHR-derived outcomes, missing data were highest at 3 and 6 months (Supplementary Material File S2). ## 3.2. Cardiovascular and Lifestyle Behaviour Outcomes Trends in outcomes between baseline and 24 months are visualised in Figure 3 and estimates of effects are presented in Table 2. Weight, blood pressure, LDL-cholesterol and HDL-cholesterol, physical activity and alcohol usage did not show a clear change in trend over time and did not differ between the participants recruited during the control and intervention period during 3–6 months nor 12–24 months. Dietary quality increased over time in both periods, but with no differences between the periods (β ($95\%$ CI): 3–6 months: 0.95 (−0.93; 2.83), 12–24 months: 1.54 (−0.57; 3.64)). Participants in the intervention period were as likely as participants in the control period to quit smoking (OR ($95\%$ CI) 3–6 months: 2.54 (0.45; 14.24) 12–24 months: 1.07 (0.10; 11.64)). Number of CVRM-related consultations did not differ between the periods during 0–12 and 12–24 months (OR ($95\%$ CI): 0–12 months: 1.00 (0.91; 1.11) 12–24 months: 0.99 (0.83; 1.18)). ## 3.3. Effect Modification of Sex and Age Stratified analyses showed less minutes of physical activity for the intervention period compared to the control period in those aged <65 years (mean difference β ($95\%$ CI) −276 (−536; −18) and more minutes of physical activity for the intervention period for those aged ≥65 (mean difference β ($95\%$ CI) 231 (84; 377) (interaction term: β ($95\%$ CI) 21 (7.1; 36) $$p \leq 0.004$$). This difference disappeared during 12–24 months. Regarding CVRM-related consultations during 3–6 months, women in the intervention period had less consultations than those in the control period (OR ($95\%$ CI): 0.87 (0.77; 0.99). In men, there was no difference between the periods (OR ($95\%$ CI): 1.11 (0.96; 1.28)) (interaction term: OR ($95\%$ CI): 0.79 (0.65; 0.95) $$p \leq 0.011$$). No other effect-modifying effects were found. ## 3.4. Goalsetting and Outcomes Most of the participants ($$n = 575$$ ($77\%$) set one or more goals in the questionnaire at baseline. The percentages of set goals ranged between $69\%$ (more exercise) and $26\%$ (alcohol reduction) (Table 3). The number of participants with a goal for weight reduction was higher ($69\%$) in the intervention group compared with the control group ($57\%$) (Pearson chi-square: 10.1, $$p \leq 0.001$$). Motivation for weight loss and diet was higher in the intervention group, compared to the control group, but not for physical activity, alcohol reduction and smoking cessation (Table 3). Confidence in achieving the goal was higher for all lifestyle goals in the intervention group, except for smoking cessation. Percentage of patient-reported achieved goal at 6 months was highest for alcohol reduction (77–$84\%$) and lowest for weight reduction, with a significant difference between the control and intervention group (57 out of 204 ($28\%$) vs. 42 out of 93 ($45\%$) (Pearson chi-square: 8.17 $$p \leq 0.017$$)). For weight, physical activity, alcohol usage and smoking cessation, cardiovascular and lifestyle outcomes of those who set a goal at baseline were similar at 3–6 months and 12–24 months to those who did not set a goal. Those who set a goal ‘to improve dietary quality’ had a significantly higher dietary index score at 3–6 months compared to those without a goal (β ($95\%$ CI): 3.1 (1.2; 4.9), but this difference disappeared during longer-term follow-up (12–24 months: β ($95\%$ CI): 2.0 (−0.1; 4.0)) (Table S2, Supplementary Material File S2; Figure 1). ## 3.5. Economic Evaluation Participants’ utilities at baseline were comparable between the control and intervention period (adjusted mean difference ($95\%$ CI) −0.004 (−0.04; 0.03)) (Table 1). First-year QALYs were significantly lower in the participants in the intervention group (mean difference ($95\%$ CI) −0.06 (−0.11; −0.02)), while in the second year, the difference was non-significant (Table 2, Supplementary Material File S3). Also for the total study period, QALYs were lower in participants during the intervention period, but the association did not reach statistical significance (intervention: 1.38 ± 0.88, control: 1.49 ± 0.79, adjusted difference ($95\%$ CI) −0.10 (−0.20; 0.002)). QALYs based on the SF-6D questionnaire and EQ-VAS showed similar results (Figure 3 and Table S2 in Supplementary Material File S3). ## 3.6. Costs Baseline costs (6 months prior to study period) were comparable between the periods (adjusted mean difference ($95\%$ CI) −18 (−247; 212) $$p \leq 0.881$$) (Table 1). With $15\%$ of the patients participating in the Healthy heart programme, average intervention costs were estimated at EUR 91 per patient in the intervention period ($95\%$ CI 70–108). Mean costs of 2-year total CVRM care were comparable (mean ± SD intervention: mean adjusted difference ($95\%$ CI) EUR 106 (−80; 293) $$p \leq 0.266$$)). Mean 2-year total health care costs were mean ± SD EUR 2909 ± 4826 (intervention) and EUR 2457 ± 3494 (control), with no significant difference between the periods (mean adjusted difference ($95\%$ CI) EUR 484 (−110; 1079) $$p \leq 0.110$$) (Table S3, Supplementary Material File S3). Combining the difference in QALYs and costs, the probability that offering the Healthy Heart programme in primary practices is cost-effective compared to not offering the programme was below $6\%$, regardless of the willingness-to-pay per QALY (Figure S1, Supplementary Material File S3). In the sensitivity analyses using the SF-6D and EQ-VAS to calculate QALYs, this probability remained below $6\%$ (SF-6D) and $26\%$ (EQ-VAS). ## 3.7.1. Participants of the Healthy Heart Programme Participants of the Healthy Heart programme ($$n = 40$$), compared to those who did not participate ($$n = 747$$), showed a higher DHD index at 3–6 months and 12–24 months (group difference ($95\%$ CI): 8.74 (4.6; 12.9) and 7.29 (2.2; 12.2)), a slightly lower HDL-cholesterol (group difference ($95\%$ CI): −0.09 (−0.17–0.01)) during 3-6 months and used less alcohol (OR $95\%$ CI 0.66 (0.065–0.66)) during 3–6 months but not significantly during 12–24 months (OR $95\%$ CI 0.89 (0.08–9.66)) (Supplementary Material File S2). ## 3.7.2. Participants with Only EHR Data No differences between participants in the intervention period and participants who did not fill in the questionnaires (EHR-only group) were found regarding weight, SBP and cholesterol levels (Supplementary Material File S2). ## 4. Discussion In this study, we evaluated the (cost-)effectiveness on population level of offering referral to the lifestyle programme “Healthy Heart” in a high-risk cardiovascular disease population in primary care. The most important finding is that for both the shorter (3–6 months) and longer term (12–24 months), there were no differences in cardiovascular or lifestyle behavioural outcomes between the control period versus the intervention period on a population level. Furthermore, there was no effect on number of cardiovascular-related practice consultations and offering the Healthy Heart programme did not improve QALYs or reduce costs in the intervention period. Although a majority of the participants set a goal to change their lifestyle behaviour, only participants with a goal to improve their diet quality slightly improved their diet quality in the short term, but not in the long term. ## 4.1. Comparison with Previous Literature We assumed that introducing a lifestyle programme in primary practices would give lifestyle change a boost during the intervention period and participants would be stimulated to change their lifestyle through goalsetting and discussion of participating in the programme. However, a significant short-term or longer-term (24 months) effect on cardiovascular outcomes or lifestyle behaviours was not found. The most important difference between the current study and previous studies is that previous studies examined the actual participants of a lifestyle programme, while in our study, outcomes were measured on a population level, where participants had the possibility to participate in the programme—or not. It appeared that only 15 percent of the participants included in the intervention period were willing to participate in the Healthy Heart programme. In line with previous studies [6,7,8], the 2-year effect on the examined outcomes was limited among those who participated in the Healthy Heart programme during our study, although the analysis may have lacked power due to the small number of participants in this subgroup. Furthermore, the five-month supported programme may have been too short to result in significant changes. However, currently implemented Dutch lifestyle interventions with an intensive treatment period of 6–8 months have been proven effective in BMI reduction [6,8]. Total follow-up time was 2 years: if participants of the programme would have maintained their lifestyle adaptations, one should expect improved outcomes for those participated in the programme. In contrast, the comparable Spanish EIRA trial [35] implemented a lifestyle intervention on an individual, group-based and community level. Although it was, like our study, not cost-effective [17], it showed positive outcomes on lifestyle behaviour in the intervention group on a population level. The personalised approach based on stage of motivation may have contributed to the positive effects of the EIRA trial, where in our study, one group-based programme was offered for all. Van der Bruggen et al. [ 15] suggested in a large modelling study that offering lifestyle interventions to people at risk could be cost-effective, in contrast to offering the intervention to the full population. However, our study showed—in line with the EIRA trial [17]—similar (CVRM-related) costs and similar or worse QALYs in the intervention period compared to the control period. The lower utilities in our intervention period may be partly explained by the stepped-wedge design, which may have induced selection bias. The low percentage of participants in the programme may have resulted in a lack of power in detecting a difference in between the intervention and control period in health care costs. Additionally, the effects of prevention of cardiovascular disease are expected in the long-term, so longer follow-up (>10 years) may reveal different results. Because intention supports behaviour change, participants in our study were challenged—either through questionnaires (control period) or by their GP/PN (intervention period)—to set a goal on lifestyle change at baseline [20]. However, the number of achieved goals at 3 and 6 months was limited, which is a common pattern in behaviour changes [36]. People who did not complete the questionnaires (EHR-only group), had similar cardiovascular outcomes as people in the intervention period, indicating that goalsetting at baseline alone is not enough for cardiovascular risk reduction through behaviour change. Possibly, people did not receive the right support to achieve these goals, even when there was a focus on lifestyle change within the practices during the intervention period. Moreover, despite motivation, the barriers were probably too high to participate in the Healthy Heart programme [13], which could have helped achieve their goals [37]. This study confirms that implementation of lifestyle programmes in primary care still faces large barriers [11,38,39]. Our results are in line with in the recently introduced combined lifestyle intervention for people with obesity [12], which has been covered by basic health insurance since 2019, where the 1-year effects on BMI were limited. Furthermore, number of dropouts of this programme was $26\%$ and the number of participants in our region, South Holland, are relatively low. Since our study is fully based in “running” practice, it may better reflect the effect that will be realised in a real-life primary care setting than previous trials could [6,8,40]. The (cost-)effectiveness assessment of our study on a population level can support GPs and policymakers in deciding about implementation of such programmes. On the other hand, the implementation only lasted during the study period, and it is known that it takes time to implement new health policies well [41]; thus, it may have been too short to see results. Time, effort and lack of knowledge about the effectiveness of the programme were barriers for care providers to promote participation in the Healthy Heart programme [42] indicating that although lifestyle programmes are available within the region, both patients and care providers should be willing to participate for successful implementation and recruitment [43]. Higher volumes of usage of physiotherapy and dietician care may indicate a shift towards an improved focus on lifestyle change; however, the difference was not significant (data in Supplementary Material File S3). Adaptation by care providers may be influenced by lack of knowledge about the effect of the programme, scientific evidence, availability of lifestyle coaches or the belief that healthcare authorities may be better equipped to provide preventive care [39]. Furthermore, people who were not motivated to participate in the Healthy Heart programme may demand a more individualised approach to address lifestyle behaviour change [12,43], such as in the trial of Zabaleta-Del-Olmo [35]. A longer implementation period in our study could possibly have supported primary care providers to gain more confidence with the programme. ## 4.2. Limitations Missing data are a common problem in longitudinal and observational studies, especially when they are fully embedded in routine clinical practice. As both questionnaires and EHR-data were used, two different missing data patterns could be observed: first, through dropouts of the questionnaires, and second, to registration and attendance bias in the EHR. Missingness was addressed by the use of mixed models for the effect analysis and multiple imputation for the cost-effectiveness analysis, both common methods for similar studies. Concerning potential registration bias in the EHR data, we controlled for practice-variance in our models. Bias through non-attendance might have under- or overestimated the effects. Non attendees could be care avoiders with poor outcomes, and on the other hand be participants with good disease control and, therefore, not intensively monitored. However, registration patterns were similar for the control and intervention period; thus, bias on the effect estimates between the groups will be limited. We did not recruit a fully representative sample of the high-risk population in our region. Results may, therefore, not be generalisable to other regions. Participating practices and the lifestyle programme were located throughout socio-economically diverse neighbourhoods in an urbanized region, while, compared to the target population, the number of people included in the study with a migrant background or lower education was relatively low [44]. Regarding weight reduction, the mean weight of our population was representative for the target population [45]. However, generally overweight people with type 2 diabetes [3] were not recruited, in whom the largest effect of a weight-reduction intervention may have been found. Furthermore, the percentage of smokers in our study was low; therefore, the effect of the trial on quitting smoking could only be estimated to a limited extent. This low percentage is in line with the percentage of smokers in The Hague aged > 65 years or with a higher educational level, thus is representative for the population that was reached by this study [44,46]. However, higher percentages of smokers are known to be among people within groups with a migration background and with lower educational levels, who were not fully reached. Lastly, the number of participants already meeting the Dutch guidelines for healthy alcohol consumption (1 or less glasses of alcohol per day) is slightly higher than in the general population of The Hague (The Hague: $51\%$ [47], control group: $61\%$ and intervention group $68\%$), thus not meeting the target high-risk population, namely those with high alcohol consumption. ## 4.3. Future Perspectives Organisation and incorporation of an integrated lifestyle programme in primary practice is not sufficient yet to help patients with a high cardiovascular risk towards a longstanding healthy lifestyle. First, not all people eligible for combined lifestyle programmes are reached: future research should focus on reaching a larger (socially diverse) population, how to motivate them and how to refer effectively to a local lifestyle intervention initiative. Furthermore, in primary practice, only persons with an indication for indicated or health-related prevention are reached. The role of the social and governmental domain in universal prevention of cardiovascular risk should be examined instead of focusing on the healthcare domain only. 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